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ASME Early Career Technical Journal 2011 ASME Early Career Technical Conference, ASME ECTC
November 4 – 5, Atlanta, Georgia USA
EVALUATION OF THE EFFECT OF SIMPLIFIED AND PATIENT-SPECIFIC ARTERIAL GEOMETRY ON HEMODYNAMIC FLOW IN STENOSED CAROTID BIFURCATION
ARTERIES
Rodward L. Hewlin Jr., and John P. Kizito Department of Mechanical Engineering
North Carolina A&T State University Greensboro, NC, USA
jpkizito@ncat.edu Tel: (336) 334-7620 ext. 315
ABSTRACT
Numerous CFD studies have been performed on the motivation
to elucidate the role of hemodynamic forces in the development
of atherosclerosis in the coronary arteries and the carotid
bifurcation artery. In order to improve CFD predictions, and to
consider CFD as a clinical diagnostic or treatment planning
tool, there is a need to ensure its accuracy and reliability
through a systematic approach of comparing simplified
geometries to patient-specific geometries. There is also a need
to compare numerical results with clinical data for validation
and verification. In this paper, comparisons of 3-D pulsatile
blood flow through a simplified stenosed carotid bifurcation
arterial geometry and two stenosed patient-specific carotid
bifurcation arterial geometries were simulated using CFD. The
goal was to quantify the efficacy of arterial vessel geometry on
hemodynamic flow parameters (velocity, wall shear stress, and
vorticity) with the same grade of stenosis for all models. The
main conclusion in this work is that dynamic behaviors of
blood through the patient-specific carotid bifurcation arterial
geometries based on numerical approaches show differences
when compared to a simplified carotid bifurcation geometry.
Keywords—atherosclerosis, geometry, hemodynamics,
modeling, patient-specific, simplified, velocity, vorticity, and
wall shear stress
INTRODUCTION
Cardiac, cerebral and peripheral localizations of
atherosclerosis are the leading causes of morbidity and
mortality in the Western part of the world [1]. Factors such as
poor dieting or lack of exercise contribute to the pathogenesis
and progression of atherosclerosis disease. Although these
factors influence disease progression, atherosclerosis usually
involves vascular districts [2]. Clinical tools such as X-ray
contrast angiography, ultrasonography, magnetic resonance
imaging (MRI), and multislice (CT) have been used as a means
of measuring the degree of progression and/or regression of
atherosclerosis. Experimental measurements of fluid velocities
in a scale model of the human carotid bifurcation artery have
been conducted using Laser-Doppler anemometry [3, 4] and
MRI [5-7]. Although MRI does not usually provide data over
an extended area of the artery, 3-D data obtained from MRI
measurements have been used as a means to guide CFD studies.
Recently, wall shear stress (WSS) in the carotid bifurcation
arteries has generated considerable interest as a complementary
explanation for plaque formation [8, 9]. In particular, the
disturbed flow environment, often characterized by low shear
stress, is said to promote atherogenesis [10-12]. The carotid
bifurcation artery where the common carotid artery (CCA)
branches into the external carotid artery (ECA) and internal
carotid artery (ICA) is a common site for atherosclerosis. The
ICA is the most broadly studied vessel in context owing to its
clinical relevance in supporting blood-flow to the brain and also
due to its relative ease of access by high-resolution medical
imaging. Severe plaque buildup in this part of the carotid
bifurcation results in ischemic transient attack (stroke). It is
also recognized that the complex geometry of the carotid
bifurcation artery can affect the blood flow pattern [13]. The
bifurcation angle is considered an important geometric factor
which can affect the flow pattern in the sinus of the ICA, the
atherogenic prone site of the carotid bifurcation artery [14]. It
has also been noted the size of the sinus bulb is related to
gender [15]. Reports have also shown that the average female
bifurcation angle is 51 degrees and the average male bifurcation
angle is 67 degrees [16].
Flow in the carotid bifurcation artery has been investigated
computationally by Nazemi et al. [16], who studied 2D, steady,
pulsatile flow in a carotid arterial segment Perktold et al. [2]
developed a 3-D carotid artery model using a pressure
correction finite element method. Velocity and WSS values in
the carotid sinus were calculated and compared with
experimental data with good agreement. Rindt and Von
ASME 2011 Early Career Technical Journal - Vol. 10 39
Steehoven [17] stimulated blood flow in the carotid bifurcation
and found that decreasing the angle between the CCA and the
carotid sinus could reduce the risk of experiencing axial flow.
Understanding blood flow patterns in different carotid
bifurcations can lead to the identification of people vulnerable
to atherosclerosis and stroke. Moreover, the unique geometry
of each person’s carotid bifurcation artery can affect the blood
flow pattern throughout the artery which affects the risk of
experiencing plaque buildup and high level atherosclerosis.
This paper focuses on a systematic approach to modeling
carotid bifurcation arteries for CFD simulation and examines
the effect of 3-D carotid bifurcation arterial geometry on blood
flow pattern and on WSS. Patient-specific CA models
generated from computed tomography angiography (CTA) are
discussed. The simulated flow profiles in the patient-specific
geometries are compared against a simplified carotid
bifurcation geometry to evaluate the efficacy of modeling
patient-specific geometries against the alternate simplified
geometries.
MATERIALS AND METHODS
The methodology of this paper begins with the generation
of 3-D geometric models of the diseased carotid bifurcation
arteries to be evaluated. In this paper, CFD analyses were
performed on a simplified carotid bifurcation arterial geometry
(SA-1) and patient-specific carotid bifurcation arterial
geometries (CA-1) and (CA-2). The geometries are discretized
and used as computational domains. For blood flow
simulations, the conventional assumptions: flow in the carotid
bifurcation artery is Newtonian, laminar, incompressible, and
isotropic are implemented. By these assumptions, blood can be
modeled by the incompressible Navier-Stokes equations with
blood density specified as 1060 kg/m3 and the corresponding
dynamic viscosity . The commercial software
GambitTM
was used for geometric meshing and FLUENTTM
was used to solve the Navier-Stokes equations in the finite
volume formulation. Grid meshing levels were performed at
three levels: coarse (104,406), coarse (143,948) and medium
(233,045). A sinusoidal velocity waveform boundary condition
was specified at the inlet of the CCA of each arterial geometry
and the ICA and ECA outlets were modeled as pressure outlets
at 12kPa (90mmHg).
In comparison of the velocity at the ICA for geometric
models, a 0.21% difference in velocity was observed for a grid
independence study. For numerical solutions, the velocity
coupling solving technique was implemented. The WSS
vectors are predicted on fluid domain surfaces that represent the
interface boundary between the fluid and the neighboring
tissue. Velocity is monitored at the CCA, ECA, and ICA and
vorticity is calculated at the sinus bulb. CTA images of
stenosed carotid bifurcations were taken from a 50 and 66 year
old male and were processed in Solidworks. Figure 1 presents
a schematic diagram of the Solidworks processing
methodology. The parametric data of the patient-specific
carotid artery geometries (CA-1) and (CA-2) is provided in
Table 1 and obtained from [3].
Figure 1: (a) CTA scan point cloud image, (b) 3D spline
surface generation, (c) lofted and boundary base solid part model of the carotid bifurcation, (d) meshed post-processed carotid artery geometry.
33.2 10 Pa s
ASME 2011 Early Career Technical Journal - Vol. 10 40
Figure 2: CAD schematic of the arterial solid model: (a) CA-1 and (b) CA-2.
The images contain a point cloud, which defines the surface
geometry of the carotid bifurcation by discrete domains. The
images were processed in Solidworks using a curve creation
feature. The curve creation feature united the point cloud with
a 3-D surface spline. This technique was repeated at each
domain until the entire arterial geometry was defined as shown
in Figure 1b. Each 3-D spline domain was then united using a
lofting and boundary boss feature until the final solid product
was obtained as shown in Figure 1c and Figure 2.
RESULTS
Simulations and analyses were performed for three
geometries, the simplified carotid bifurcation arterial geometry
(SA-1) and two patient-specific carotid bifurcation arterial
geometries (CA-1) and (CA-2). The location of the sinus bulb
was monitored for calculating WSS and vorticity as a function
of time. Velocity has been calculated throughout the CCA,
ECA, and ICA of the simplified arterial geometry and patient-
specific arterial geometries at any point in time. Figure 3
shows the velocity waveform monitored at the ICA outflow of
the three geometries at time 0s to 3s. At the beginning of the
cycle, the blood flow rate starts increasing and reaches a
maximum level around 0.45s and then decreases to the lowest
level at 1.3s. Figures 4 and 5 present a plot of the vorticity vs.
time for the SA-1, CA-1, and CA-2 geometries.
Figure 3: ICA velocity waveform for SA-1, CA-1, and CA-
2.
Figure 4: Plot of sinus vorticity vs. time for the CA-1
patient-specific geometry.
Figure 5: Plot of sinus vorticity vs. time for the CA-2
patient-specific geometry and the simplified arterial geometry SA-1.
0 0.5 1 1.5 2 2.5 30
0.1
0.2
0.3
0.4
0.5
Flow Time (sec)
Velo
cit
y (
m/s
)
CA-1 ICA velcity
CA-2 ICA Velocity
SA-1 ICA Velocity
0 0.5 1 1.5 2 2.5 3-70
-60
-50
-40
-30
-20
-10
0
Flow Time (sec)
Vo
rti
cit
y (
1/s
)
0 0.5 1 1.5 2 2.5 3-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Flow Time (sec)
Vo
rti
cit
y (
1/s
)
CA-2 Sinus Vorticity
SA-1 Sinus Vorticity
(a) (b)
ASME 2011 Early Career Technical Journal - Vol. 10 41
(a)
(b)
(c)
Figure 6: Axial velocity contour at time T=0.5s (systole), for (a) SA-1, (b) CA-1, and (c) CA-2.
(a)
(b)
(c)
Figure 7: Axial velocity contour at time T=1.0s (diastole), for (a) SA-1, (b) CA-1, and (c) CA-2.
ASME 2011 Early Career Technical Journal - Vol. 10 42
Figure 6 shows the axial velocity contours of the sinus at
time 0.5s (systole), and Figure 7 shows the axial velocity
contours of the sinus at time 1.0s (diastole). The negative
velocity indicates flow reversal zones. As shown in Figures 4
and 5, the maximum flow reversal is observed in the patient-
specific geometry models. For the patient-specific geometries,
it was also observed that the axial velocity is the highest at the
region upstream of the ICA and downstream of the sinus near
the CCA. Regions of flow reversal formed near the sinus wall
are due to the rapid changes in the flow rate, flow viscosity and
the curvature of the sinus. Within these zones, blood flows in
the direction opposite to the mean flow increasing the
probability of plaque deposition.
For the patient-specific geometries, the velocity gradients
near the stenosis are much higher than those near the sinus.
The velocity at the sinus becomes negative and indicates flow
reversal and very low WSS. WSS is an important factor in the
progression and initiation of atherosclerosis. For the simplified
geometry, there is a small region of velocity increase at the
stenosis, and very little flow downstream of the stenosis to the
ICA outlet. The major increase of velocity in the simplified
geometry is experienced in the ECA branch of the artery.
Figure 8 shows the WSS contour for systole (peak velocity) for
the simplified arterial model and patient-specific geometries.
As noted by Malek et al. [12] regions with WSS less than
0.4 Pa are susceptible to atherosclerosis. In the wall shear
stress contours provided in Figure 8, it is shown that the
maximum WSS experienced is 16.3 Pa. The maximum WSS is
shown at the ICA stenosed region of SA-1 and at the stenosis
and bifurcating region of CA-1. A general conclusion from
these WSS patterns is that the sinus bulb is the most
atheroprone site where higher values of WSS appear at the
bifurcation apex and final ends of the ECA and ICA, which
have a smaller diameter. There is a stagnation point at the apex
because shear directions at its sides are different (granting that
the magnitude of the shear would be relatively high).
The stagnation point is the place of plaque buildup.
Another point is that, in general, realistic geometries have
higher values of WSS and the atheroprone site in the sinus bulb
is smaller. When considering WSS, the magnitude of the wall
shear is a function of the geometry of the arterial vessel. This
works proves that when studying hemodynamics of the arterial
vasculature, specifically the carotid bifurcation artery for
diagnostic purposes, it is important to have patient-specific
parametric data for the carotid bifurcation vessel to be
analyzed.
(a)
(b)
(c)
Figure 8: WSS contour at time T=0.5s (systole), for (a)
SA-1, (b) CA-1, and (c) CA-2
ASME 2011 Early Career Technical Journal - Vol. 10 43
CONCLUSION
The focus of this paper was on examining the effect of 3-D
carotid bifurcation arterial geometry on blood flow pattern and
on WSS. Patient-specific CA models were generated from
computed tomography angiography (CTA) scans and modeled
for CFD. The simulated flow profiles in the patient-specific
geometries were compared against a simplified carotid
bifurcation geometry to evaluate the efficacy of modeling
patient-specific geometries against an alternate simplified
geometry. The main conclusion in this work is that dynamic
behaviors of blood through the patient-specific carotid
bifurcation arterial geometries based on numerical approaches
showed differences when compared to a simplified carotid
artery bifurcation geometry.
For the patient-specific geometries, the velocity gradients
near the stenosis were much higher than those near the sinus.
Flow reversal and low WSS was observed in the sinus region of
the patient-specific models. Also, for the simplified geometry,
there was a small region of velocity increase at the stenosis, and
very little flow downstream of the stenosis to the ICA outlet.
The major increase of velocity in the simplified geometry was
experienced in the ECA branch of the artery, while in the
patient-specific geometries, the reverse is true.
Some of the limitations of this work consisted of not
implementing individual patient-specific flow rates/waveform
of the arterial models. This is because these flow rates/waves
were not available. A sine wave velocity profile was used at the
boundary of the CCA. Future studies will incorporate more
realistic boundary conditions obtained from experimental data
from flow phantoms or as an alternate blood flow rate obtained
from volunteers. Also future studies will incorporate the
moving boundary of the arterial wall, instead of using rigid wall
boundary conditions. A future study consisting of a broad
statistical study on the effect of patient-specific arterial
geometry on hemodynamic flow will be conducted.
ACKNOWLEDGMENT This work was supported in part by the Department of
Education Title III grant program to N.C. A&T State
University.
REFERENCES
[1] Cecchi, E., et al. "Role of Hemodynamic Shear Stress in
Cardiovascular Disease." Atherosclerosis 214.2 (2011): 249-56.
Print.
[2] Perktold, K., Resch, M., Peter, R.O., "3-Dimensional
Numerical Analysis of Pulsatile Flow and Wall Shear Stress in
the Carotid Artery Bifurcation," Journal of Biomechanics 24.6
(1991): 409-20. Print.
[3] http://www.charite.de/biofluidmechanik/en/research/
[4] Gijsen, F. J. H., F. N. van de Vosse, and J. D. Janssen. "The
Influence of the Non-Newtonian Properties of Blood on the
Flow in Large Arteries: Steady Flow in a Carotid Bifurcation
Model." Journal of Biomechanics 32.6 (1999): 601-08. Print.
[5] Fayad, Z. A., and V. Fuster. "The Human High-Risk Plaque
and Its Detection by Magnetic Resonance Imaging," American
Journal of Cardiology 88.2A (2001): 42E-45E. Print.
[6] Marshall, I., P. Papathanasopoulou, and K. Wartolowska.
"Carotid Flow Rates and Flow Division at the Bifurcation in
Healthy Volunteers," Physiological Measurement 25.3 (2004):
691-97. Print.
[7] Stary, H. C., et al. "A Definition of the Intima of Human
Arteries and of Its Atherosclerosis-Prone Regions - a Report
from the Committee on Vascular-Lesions of the Council on
Arteriosclerosis, American-Heart-Association." Circulation
85.1 (1992): 391-405. Print.
[8] Cunningham, K. S., and A. I. Gotlieb. "The Role of Shear
Stress in the Pathogenesis of Atherosclerosis," Laboratory
Investigation 85.1 (2005): 9-23. Print.
[9] Libby, P. "Inflammation in Atherosclerosis." Nature
420.6917 (2002): 868-74. Print.
[10] Ku, D. N., et al. "Pulsatile Flow and Atherosclerosis in the
Human Carotid Bifurcation - Positive Correlation between
Plaque Location and Low and Oscillating Shear-Stress,"
Arteriosclerosis 5.3 (1985): 293-302. Print.
[11] Ku, D.N., Giddens, D.P., "Laser Doppler Anemometer
Measurements of Pulsatile Flow in a Model Carotid
Bifurcation," Journal of Biomechanics 20.4 (1987): 407-21.
Print.
[12] Malek, A.M., Alper, S.L., Izumo, S.,. "Hemodynamic
Shear Stress and Its Role in Atherosclerosis," Journal of the
Americal Medical Association 282 (1999): 2035-42. Print.
[13] Lee, S. W., et al. "Geometry of the Carotid Bifurcation
Predicts Its Exposure to Disturbed Flow." Stroke 39.8 (2008):
2341-47. Print.
[14] Nguyen, K. T., et al. "Carotid Geometry Effects on Blood
Flow and on Risk for Vascular Disease." Journal of
Biomechanics 41.1 (2008): 11-19. Print.
[15] Goubergrits, L., Affeld, K., Fernandez-Britto, J. and
Falcon, L. "Investigation of Geometry and Atheroscleorosis in
the Human Carotid Bifurcations." Journal of Mechanics in
Medicine and Biology 3.1 (1999): 31-48. Print.
[16] Nazemi, M., Kleinstreuer, C., Archie, J.P. "Pusatile 2-
Dimensional Flow and Plaque-Formation in a Carotid-Artery
Bifurcation." Journal of Biomechanics 23.10 (1990): 1031-37.
Print.
[17] Rindt, C.C.M., and. Von Steenhoven, A.A "Unsteady Flow
in a Rigid 3-D Model Of The Carotid Artery Bifurcation"
Journal of Biomechanical Engineering-Transactions of The
ASME 118.1 (1996): 90-96
ASME 2011 Early Career Technical Journal - Vol. 10 44
ASME Early Career Technical Journal 2011 ASME Early Career Technical Conference, ASME ECTC
November 4 – 5, Atlanta, Georgia USA
EVALUATION OF PULSE JET MIXING USING A SCALAR QUANTITY AND SHEAR STRESS
Ibraheem R. Muhammad and John P. Kizito North Carolina A&T State University
Department of Mechanical Engineering Greensboro, NC, U.S.A
ABSTRACT
This paper uses computational fluid dynamics (CFD) to
investigate the behavior of pulsing, submerged, liquid jets in a
cylindrical mixing tank. The jet pulse was simulated by turning
the jet inlet velocity on and off in a cyclic manner. It was
proposed that a pulsing, liquid jet would provide sufficient
force to dislodge and suspend solid materials. Therefore, it is
necessary to evaluate its performance in different
configurations to determine which gives the best performance
parameters. The shear stress at the bottom of the tank was
calculated and compared for certain flow configurations. In
addition, studies were performed to determine the mixing time,
or time elapse for the tank to become uniform, using a scalar
quantity such as temperature. More specifically, a hot jet
stream was injected into a cooler bulk fluid until the desired
mixing criterion was met. The temperature was calculated at
different locations within the tank and the mixing time was
estimated to be the time elapse for the temperature in the tank
to reach 65% of the equilibrium temperature. The results
showed that the mixing time decreased with increasing number
of jets. However, there was no enhancement in mixing by using
pulsing jets. The wall shear stress at the bottom of the tank
increased as the jet height from the bottom of the tank
decreased.
INTRODUCTION Liquid jet mixers have been used for years in many
industrial processes. Jet mixers operate by withdrawing fluid
from the mixing tank and supplying the fluid back to the tank
through a nozzle at high velocities. As the jet travels in the
tank, the flow field expands as it entrains the bulk fluid. Jet
mixers are advantageous compared to other mixing devices as
they can achieve effective mixing by creating high turbulence
and high shear rates. These mixing devices can be easily
installed and transferred to different vessels, and can be used in
combination with other mixing equipment [1-5]. Jet mixers
have been used in off bottom suspension of solids in such
applications as nuclear waste, food, and pharmaceutical
processing.
To achieve suspension in these systems it is mainly
necessary to provide a force from the jet to overcome the
weight of the particle. Some solid materials contain smaller
particles, which have cohesion forces as the dominant forces
that must be overcome to cause suspension [6]. In these
systems, the material can be described as a Bingham plastic and
it is necessary to overcome a critical shear stress, or yield
stress, at which point the mixture then starts to move and
behave more like a Newtonian fluid [6, 7].
For practical purposes, it is usually not enough to just
dislodge the solid material from the bottom of the tank but to
also maintain the suspension for a certain time period. For
certain cases, particles may not stay suspended, which results in
inaccurate data when fluid samples are taken. In addition, if the
need may arise to move the mixture out of the tank to another
location, equipment failure or malfunction can occur since the
flow was not predicted accurately. Therefore, it is not only
desirable to understand the physics behind eroding or
dislodging stationary solid materials from a surface, but to
understand how they behave once they are suspended.
Previous researchers have studied erosion in unrestrained flows
such as in riverbeds, lakes, and estuaries [7, 8]. However,
erosion in tanks, or confined flows, is different because the
flow environment provides interactions between the solid
material and the solid walls.
There is not extensive literature on pulsing jet mixers for
solid suspension. A pulsing jet can be created using a periodic
discharge from the nozzle by controlling the power input to the
pumping equipment in a cyclic manner. By discharging the jet
in pulses, a unique flow structure is created within the mixing
vessel when compared to continuous jets. It is expected that
pulsing jet mixers will create different flow structures than
continuous jets and have better mixing in some situations due
to increased number of local vortices.
The present paper investigates the mixing behavior of pulse
jet mixer nozzles using the computational fluid dynamics
(CFD) package, FLUENT. The mixing time was calculated by
comparing the temperature throughout the tank as a function of
the inlet temperature, which was initially set at a higher value.
The shear stress on the bottom surface was calculated as well.
The rationale of the present paper is to draw attention to the
potential of pulsing jet mixers in the erosion and suspension of
solid particles in practical applications.
ASME 2011 Early Career Technical Journal - Vol. 10 45
PROBLEM FORMULATION Physical Model
The mixing tank was modeled with a diameter of 0.305 m
and a height of 0.305 m as shown in Figure 1. The different jet
orientations (single, dual, quad, and azimuthal) that were
studied are shown in Figure 1, as well. For the dual and quad
jet mixer systems, the jets were centered and directed outward.
The jets in the multiple jet systems were spaced 2 cm apart
from each other. For the single jet system, the jet was 0.07625
m from the center and directed away from the tank outlet and
towards the center of the tank. The inlet came from a nozzle 8
mm in diameter. The nozzle was positioned at a 45° angle and
is attached to a 1.27 cm (1/2”) diameter piece of piping. The
outlet was a 1.905 cm (3/4”) diameter, overflow hole in the side
wall of the mixing tank and the center of the hole was located
0.29 m from the bottom of the tank. Figure 2 shows an
additional orientation that was studied in which the jets were
directed in an azimuthal fashion. The top view and 3d view of
the system is shown in Figures 2a and 2b, respectively.
Grids
Figure 3 shows the tetrahedral element types that were used
to mesh the volume. Mesh intervals of 10 – 20 mm was used.
It was noticed that there was not much variance in the solution
when the interval size was decreased. The mesh of 15 mm was
chosen because it was the largest interval size in which resulted
in a grid independent solution.
CFD Modeling The CFD simulations were performed using FLUENT, a
commercially available software. The jet flow was modeled by
the standard k- turbulence model with standard wall functions.
The standard k- ɛ has been used to simulate jet mixers because
it was found to be less demanding in terms of computational
time and accuracy requirements were not compromised [9].
The basic equations which describe the flow given an
incompressible, Newtonian fluid with constant properties are
the continuity, momentum, and energy equation, presented in
Equations (1), (2) and (3), respectively [10].
0divV (1)
VpgDt
DV 2 (2)
TTC
k
Dt
DT
p
22
(3)
The transient simulations required a time step of 0.05 s. The
boundary conditions, such as velocity inlet, outlet, and free
shear surface are illustrated in Figure 3. The inlet velocity was
set at 10 m/s when the jet was on. The top surface is open to the
atmosphere and is modeled as a free shear surface. The other
walls were set with the no-slip boundary conditions.
Figure 3. Example of meshed volume and boundary conditions used in simulations.
(a) (b)
Figure 2. Azimuthal oriented jet system including (a) a top view and (b) three-dimensional view.
(a)
(b) (c)
Figure 1. The different jet mixer arrangements including (a) single jet, (b) dual jet, and (c) quad jet systems.
ASME 2011 Early Career Technical Journal - Vol. 10 46
The method used to calculate the mixing time for the
present system was based on a thermal criterion. Initially, the
bulk fluid is at 283° K and the liquid jet enters the tank at a
higher temperature, 350° K. A 65% homogeneity criterion was
used to find the mixing time for the simulations. The mixing
time was calculated as the time between the beginning of the
simulation until the time at which the temperature becomes
65% of the equilibrium temperature, or m = 0.35. This is
mathematically expressed by [11]
,35.0
oTeqT
TeqTm
(1)
where Teq is the equilibrium temperature, To is the initial
temperature of the bulk fluid in the tank, and T is temperature
at any location in the tank at any time. The value, m, is just a
dimensionless parameter which specifies the maximum allowed
deviation of mixing based on the criteria chosen for the study.
The mixing time criterion was chosen to save on
computational time. Temperature probes were placed in
various locations of the mixing tank and the temperature at
those locations were recorded as a function of time for the
duration of the numerical run. When all locations met the
mixing criteria, the tank was considered mixed and the time
elapse was reported as the mixing time. From scaling analysis,
the Peclet number, which compares convection time to
diffusion time in the flow, was determined to be 1.46 x 104.
Since the Peclet number was large, the flow was convection
dominated, thus finding the use of a scalar quantity, like
temperature, to be acceptable.
Initially, the jet mixers were simulated as continuous jets to
determine a baseline for comparison throughout the study. The
mixing time and the wall shear stress on the bottom surface
were calculated and reported. Simulations were then run using
pulsing jet mixers. Table 1 shows the various pulse settings
used in the present study. When the jet was set to on, the inlet
velocity was fixed to 10 m/s and when off the velocity was
fixed to 0 m/s.
The settings used in this system are not indicative of all the
possible settings that can be used, but rather to point in the
direction of which setting may give the best overall mixing. It
was important to keep the pulses such that the time off was
relatively short when measured in terms of particle settling
velocity thus ensuring uniform dispersion of particles.
Table 1: Various pulse settings used in simulations.
Pulse Setting Name Time on (s) Time off (s) Pulse 1 (P1) 2.5 0.5
Pulse 2 (P2) 3.5 0.5
Pulse 3 (P3) 5.0 0.5
Pulse 4 (P4) 5.0 0.25
RESULTS Continuous Jets
Figures 4 and 5 shows the results comparing the various
types of continuous jet configurations. Continuous jets were
modeled using four types of jet configurations (single, dual,
quad, and azimuthal) at two different heights from the bottom
of the tank 0.025 and 0.07625 m. The temperature within the
tank was reported in multiple positions at multiple heights to
ensure the behavior in critical zones of the tanks is accurately
represented. The results showed that to achieve 65%
homogeneity, the mixing time decreased as more jets were
added. In particular, Figure 4 shows the mixing time with the
jets positioned 0.07625 m from the bottom of the tank. Figure
5 shows the results with the jets positioned 0.025 m from the
bottom of the tank.
The results from both heights are summarized in Table 2.
The same trend was observed at both heights. By doubling the
number of jets, the mixing time for a set criteria was decreased
about half. The best jet arrangements at both heights were
shown to be the quad jets and the azimuthal jets which were
expected since they use the most number of jets and introduce
the most convection into the tank as compared to the other
configurations. Specifically, at a height of 0.025 m, the
azimuthal jet had the lowest mixing time, 15.8 s. At a height of
0.07625 m, the quad jet configuration had the lowest mixing
time, 15.4 s. The discrepancy in results between heights was
due to the way the jets were directed and interacted with the
walls.
The temperature contours for the quad jet and azimuthal jet
systems were compared. The temperature contours after 5
seconds of flow time for the quad and azimuthal jets are shown
in Figure 6. Figures 6a and 6b show the profiles in the yz-
plane for the quad and azimuthal jets, respectively. Figures 6c
and 6d show the profiles in the xy- plane for the quad and
azimuthal jets, respectively.
Figure 6 show that the quad jets have more mixing in the
middle of the tank compared to the azimuthal jets. The
phenomena can be attributed to the orientation of the quad jets.
As the quad jets interact with the tank walls, the flow turns over
and cause circulatory patterns. Figure 6b and 6d show the
azimuthal jets oriented such that a swirling flow is created and
the most prominent mixing takes place close to the walls. The
jet discharge from the nozzles for the azimuthal jets is not
shown in the plots because the jets are oriented such that the
nozzles are outside of the plane. The quad jets system has a
significant low mixing zone in the region near the side of the
tank above the edge of the jet circulation off the wall. Lack of
mixing can be clearly seen in the left side of Figure 6c. Low
mixing zones are also observed in Figure 6a, in the upper
vicinity.
The temperature profiles after 10 seconds of flow time for
the quad and azimuthal jets in the yz- and xy- planes are shown
in Figure 7. Specifically, the quad and azimuthal jets in the yz-
plane is shown in Figures 7a and 7b, respectively. The quad
and azimuthal jets in the xy- plane is shown in Figures 7c and
7d. The minimum temperature in the entire tank has increased
ASME 2011 Early Career Technical Journal - Vol. 10 47
from 297º K to 313 º K. Comparison of Figures 6c and 7c,
illustrates that the low mixing zones after 10 seconds are still in
the same general location as those after 5 seconds. Figures 6a
and 7a show that the low mixing zone in the yz plane, though
still in the upper right hand side, shifts from the top after 5
seconds to the side after 10 seconds. Figures 6b and 7b show
that the temperature profiles for the azimuthal jets seem to
become even more uniform after 10 seconds compared to the 5
seconds case. But the low mixing areas are still located in the
center axis of the tank.
Figure 4: Mixing times for continuous jet systems with jets located 0.07625 m from bottom of tank and at a 45° angle.
Figure 5: Mixing times for continuous jet systems with jets located 0.025 m from bottom of the tank and at a 45° angle.
Table 2: Mixing time comparisons for continuous jets of different configurations.
Jet Type H = 0.025 m H = 0.07625 m Single 61.6 s 60.5 s
Dual 29.4 s 29.4 s
Quad 16.1 s 15.4 s
Azimuthal 15.8 s 16.1 s
Figure 7: Temperature profile in the (a) yz-plane of quad jets, (b) yz-plane of azimuthal jets, (c) xy-plane of quad jet, and (d) xy-plane of azimuthal jets at a height of 0.07625 m after 10 seconds flow time.
Figure 6: Temperature profile in yz- plane of quad jets (a), yz-plane of azimuthal jets (b), xy-plane of quad jets (c), and xy-plane of azimuthal jets at a height of 0.07625 m after 5 seconds flow time.
ASME 2011 Early Career Technical Journal - Vol. 10 48
Pulse Jets The results for the mixing times of the different pulse
settings described in Table 1 are plotted in Figure 8. The
different pulse settings were modeled using the quad jet system
positioned at 0.07625 m from the bottom of the tank since the
lowest mixing times were reported for this configuration in
earlier studies. The mixing time results are summarized in
Table 3. Figure 8 shows that the P4 setting had the lowest
mixing time and the P1 setting had the highest mixing time.
The mixing time decreased as the period of jet discharge was
increased and the off period was decreased. The results in
Table 3 show that the P4 and P3 settings had mixing times that
were very close to that of the continuous jet in the same
configuration. The result may be due to there being more local
vortices created in the mixing tank compared to the steady jet
systems. The vortices are created as the jet is pulsed in a cyclic
manner. The mixing time results indicate that it may be
possible to increase mixing with some alternate pulsing settings
or techniques.
Figure 8. Mixing time for different pulse settings in quad jet arrangement with jets 0.07625 m from the bottom of the tank and at an angle of 45°.
Table 3: Mixing times for different pulse jet settings.
Pulse Setting Mixing Time P1 16.8 s
P2 16.3 s
P3 15.6 s
P4 15.5 s
Figure 9 shows the temperature profile for the entire first
pulse cycle (time on + time off) of the quad jet system using the
P1 setting. Figures 9a and 9b show the yz- and xy- planes,
respectively, after the initial on period of 2.5 seconds. Figures
9c and 9d show the yz- and xy- planes, respectively, after the
off period of 0.5 seconds. The jet momentum can be seen
impinging on the bottom of the tank and spreading up the wall.
The results show that the top of the tank is not well mixed
because the total flow time at this snapshot is still short. After
the complete first pulse cycle, the flow has been off for 0.5 s,
but the convection of the jet continues to spread slowly towards
the top of the tank. Figure 10 shows the temperature profile in
the yz and xy plane, respectively, at the end of 3 complete pulse
cycles. These figures show that though the low mixing zones
exist in similar vicinities, the overall profile differs from that of
the continuous jets.
Figure 10. Temperature contour of quad jet system after 3 complete pulse cycles using P1 setting in the (a) yz-plane and (b) xy-plane.
Figure 9. Temperature contour of quad jet system of first cycle using P1 setting in the (a) yz-plane after initial on time of 2.5 s, (b) xy-plane after initial on time, (c) yz-plane after initial off time of 0.5 s, and (d) xy-plane after initial off time.
ASME 2011 Early Career Technical Journal - Vol. 10 49
Figure 11 illustrates the temperature profile of the total first
pulse using the P4 setting. The temperature profile after the
time on phase for the yz and xy planes, respectively, is shown
in Figures 11a and 11b. The temperature profile after the off
phase of the first pulse is shown in Figures 11c and 11d. The
P4 setting had mixing time results just about the same as the
continuous jets. Compared to the P1 setting after a single
pulse, more mixing takes place, which is because a single pulse
cycle using the P1 setting is a total of 3 seconds while that of
the P4 setting is a total of 5.25 seconds. The temperature
profile after 3 complete cycles using the P4 setting is shown in
Figure 12. At this point, the low mixing zones are at a
minimum and because the minimum temperature within the
tank is about 326, the homogeneity criteria set for the study are
almost reached. However, the low mixing zones still appear in
the same locations as with the P1 settings and with the
continuous jets of this configuration. The low mixing zones did
not differ much from case to case, but the temperature profile
varied.
Shear Stress on Bottom Wall Table 4 summarizes the results for the maximum and the
average shear stress on the entire bottom surface for the
continuous jets. As the height from the bottom of the tank was
decreased, the shear stress on the bottom surface increased.
The highest shear stress values were reported for the azimuthal
jets at both heights. At a height of 0.07625 m, the azimuthal
jets had the highest average shear for the surface. The
azimuthal jets were able to cover more surface area with higher
amounts of shear. Similarly, the quad jets had the highest
average shear at the lower height, despite not having the
maximum shear stress values.
Figure 13 shows examples of the shear maps on the bottom
surface after 10 seconds of flow time at a height of 0.07625 m
from the bottom. The shear stress on the bottom wall for the
dual, quad, and azimuthal jets are shown in Figures 13a, 13b,
and 13c, respectively. As previously mentioned, the azimuthal
jets had higher shear stress covering the most surface area on
the bottom wall, as shown in Figure 13c.
The maximum shear stress found for the different pulse
settings and the continuous jets were approximately the same.
The results were expected because the discharge phase of the
jets was long enough that they began to behave like steady jets
while on. The only difference was that for the pulsing jets, the
shear stress dropped to below 1 Pa during the time off period.
The shear stress did not completely go to zero because the
discharge phase of the pulse provided enough momentum to
keep the fluid active during the short off period.
The shear maps can be used to determine the effectiveness
of the jets removing the solids off the bottom of the tank and
thus creating a suspension. For example, in Figure 13a, the
dual jets will not be able to suspend a large portion of solids in
the middle region of the tank. On the other hand, the quad and
azimuthal jets, shown in Figures 13b and 13c, respectively,
have smaller regions where the jets are less effective at the
bottom of the tank. The aim was to minimize these areas
relative to the total base of the tank. Through modification of
Figure 12. Temperature contour of quad jet system after 3 complete pulse cycles using P4 setting in the (a) yz-plane and (b) xy- plane.
Figure 11. Temperature contour of quad jet system of first pulse cycle using P4 setting in the (a) yz-plane after initial on time of 5 s, (b) xy-plane after initial on time, (c) yz- plane after off time of 0.25 s, and (d) xy- plane after off time.
ASME 2011 Early Career Technical Journal - Vol. 10 50
the design or the addition of more jets, these regions can be
further reduced.
Table 4. Maximum and average shear stress on bottom wall using different continuous jet configurations.
H = 0.07625 m H = 0.025 m
No. of jets
Max
Shear
Avg.
Shear on
Surface
Max
Shear
Avg.
Shear on
Surface
Dual Jets 12.36 Pa 2.98 Pa 79.64 Pa 6.79 Pa
Quad Jets 14.19 Pa 4.95 Pa 70.35 Pa 13.39 Pa
Azimuthal
Jets 21.81 Pa 6.58 Pa 80.48 Pa 12.92 Pa
CONCLUSIONS Continuous and pulse jets were modeled to determine the
best configuration for solid suspension processes. The mixing
time was calculated using a 65% homogeneity criterion. The
mixing time decreased as more jets were added. There was no
improvement in mixing times using pulse jet flows over
continuous jets. However, the quad jets using the P4 and P3
pulse setting had mixing times very close to the continuous jets
of the same configuration. The P4 and P3 settings had mixing
times of 15.5 s and 15.6 s, respectively, and the continuous
quad jets had a mixing time of 15.3 s. The results show that
using pulse jets can perhaps lead to a reduction in operational
costs in some applications, as less working fluid and less
energy is used, if properly optimized. However, more studies
must be run to reduce the uncertainties in the data. In addition,
more studies should be run using different pulse settings and
studies should be run for a longer period of time (i.e. higher
mixing time criterion).
The shear stress on the bottom of the tank was calculated
for different jet configurations as well. The azimuthal jets had
the highest shear on the bottom wall for both of the heights
studied. But, the maximum shear cannot be the only factor in
determining overall performance for solid suspension
processes. In the suspension of solids it is ideal to have a mixer
which is able to completely remove the solids from the bottom
surface. Therefore, the distribution of the bottom shear stress is
an important factor. In the current study, the distribution of the
shear stress was visually illustrated using contour color maps
and quantified using the averaging techniques along the bottom
surface. Overall, the quad jet systems provided the highest
average shear stress on the bottom surface at a height of 0.025
m from the bottom of the tank. The quad jet configuration
showed promising results and more studies should be done to
optimize its performance. The results from the current study
provide some insight into the mixing using pulsing jets and its
possible applicability to solid suspension processes.
ACKNOWLEDGMENTS The authors would like to acknowledge the financial
support of the Title III Program at North Carolina A&T State
University, which is administered by the U.S. Department of
Education, Institutional Development and Undergraduate
Education Services.
REFERENCES [1] Bathija, P.R., 1982, " Jet mixing design and applications,"
Chemical Engineering, pp. 89-94.
[2] Manjula, P., P. Kalaichelvi, and K. Dheenathayalan, 2010,
"Development of mixing time correlation for a double jet
mixer," Journal of Chemical Technology & Biotechnology,
85(1), pp. 115-120.
[3] Maruyama, T., Y. Ban, and T. Mizushina, 1982, "Jet
Mixing of Fluids in Tanks. Journal of Chemical Engineering of
Japan, 15(5), pp. 342-348.
[4] Patwardhan, A.W. and S.G. Gaikwad, 2003, "Mixing in
Tanks Agitated by Jets," Chemical Engineering Research and
Design, 81(2): pp. 211-220.
[5] Tatterson, G.B., 2003, Scaleup and Design of Industrial
Mixing Processes, McGraw-Hill, New York, NY, 2nd ed.
[6] Wells, B.E., et al., 2009, "Assessment of Jet Erosion for
Potential Post-Retrieval K-Basin Settled Sludge," Pacific
Northwest National Laboratory: Richland, WA.
[7] Abulnaga, B.E., 2002, Slurry Systems Handbook, McGraw-
Hill, New York, NY.
(a) (b)
(c)
Figure 13. Shear stress map on the bottom surface after 10 seconds of continuous flow time, at a height of 0.07625 m from the bottom, for (a) dual jets, (b) quad jets, (c) and azimuthal jets.
ASME 2011 Early Career Technical Journal - Vol. 10 51
[8] Powell, M.R., Y. Onishi, and R. Shekarriz, Sept. 1997,
"Research on jet mixing of settled sludges in nuclear waste
tanks at Hanford and other DOE sites: A historical perspective,"
PNL-11686.
[9] Patwardhan, A.W., 2002, "CFD modeling of jet mixed
tanks," Chemical Engineering Science, 57(8), pp. 1307-1318.
[10] White, Frank M., 1974, Viscous Fluid Flow, McGraw-
Hill, New York,NY.
[11] H. D. Zughbi and M. A. Rakib, 2004, "Mixing in a fluid
jet agitated tank: effects of jet angle and elevation and number
of jets," Chemical Engineering Science, 59.
ASME 2011 Early Career Technical Journal - Vol. 10 52
ASME Early Career Technical Journal
2011 ASME Early Career Technical Conference, ASME ECTC November 4-5, Atlanta, Georgia USA
MANUFACTURING KNOWLEDGE SHARING: THE UTILISATION OF KNOWLEDGE BASE TECHNOLOGIES IN PLM
Abdul Shakoor, Arshad Mehmood
Loughborough University, UK
Khizar Azam, Riaz Akber Sayyed Department of Mechanical Engineering University of Engineering & Technology
Peshawar Pakistan
KEYWORDS : PLM, NPDI, Knowledge Management, Knowledge Capturing, Knowledge Sharing
ABSTRACT
The maximisation of the prospects for substantial benefits and the preparation of industry to fulfill competitive needs in a globally oriented business environment requires a wide range information-based decision support system. This knowledge sharing framework needs an extensive research to improve inter-operations of cross-functional systems, which subsequently provides the basis for diverse utilisation of knowledge base technologies in manufacturing. The effective knowledge sharing framework in manufacturing industries is used to speed-up the New Product Development & Introduction (NPDI) process and improves the effectiveness of information flow in Product Lifecycle Management (PLM) environment. This paper describes the development of Knowledge Based System (KBS) and to select a process for making circular shapes and to explore the manufacturability of the intended design product. The KBS is an expert system, used to select a suitable process and cutting tools by defining the system’s constraint. The KBS is designed using E2KS; the state of the art software and knowledge capturing tool. The machining examples are used to demonstrate the implementation of the system, which will provide a basis for knowledge sharing in PLM environment. This approach provides a basis for knowledge sharing in manufacturing industries. Future manufacturing industry will benefit from further development and successful implementation of PLM system.
INTRODUCTION
The highly competitive environment and modern business requirements in world of globalisation always drives technological solutions to sustain the competitiveness of the
company. In a globalised economy, companies always face challenges to accelerate the new product development process to shorten time–to-market for early entry into the market and time - to - profit for quick revenue generation. In response to these challenges, companies are under high pressure to meet the demands of the market by launching tailored products to customers for economy of scope, to reduce time-to-volume via mass production for the economy of scale, and to decrease time-to-profit by increasing efficiency of the entire lifecycle for the economy of service [1].
Recently the traditional business model in manufacturing paradigm shifted from make-to-order (MTO), to engineer-to-order (ETO), to configure -to-order (CTO), to design-to-order(DTO) and in near future to innovate-to-order (ITO) as illustrated in Figure 1. The new business models have developed a competitive environment to provide a technical solution to the customer demands. Hence these technologies have changed the traditional mass production (MP) to flexible manufacturing system (FMS) followed by manufacturing knowledge management (MKM) leading to the product customisation (PC), product knowledge management (PKM), and to the product lifecycle management (PLM) as illustrated in Figure 2 [2]. PRODUCT LIFECYCLE MANAGEMENT
Product Lifecycle Management (PLM) is a product
strategic approach based on the product and stakeholder's interaction from inception to the end of life [3]. Different organisations have defined PLM in their own context. The NIST [7] defines PLM as a strategic business approach for the effective management and use of corporate intellectual capitals.
ASME 2011 Early Career Technical Journal - Vol. 10 53
Figure 1. Development of New Business Models
Figure 2. Technological Development
KNOWLEDGE BASE SYSTEM Knowledge Base System (KBS) is considered to be an
important tool for maximising the utilisation of knowledge. KBS aims to follow the experience and knowledge of humans to be represented and used on a computer so as to enhance decision-making ability [8]. This database provides a base for knowledge management. KBS has the potential to accomplish objectives such as to capture, represent , share, reuse , transfer, and maintain the knowledge. It is implemented in a variety of decision making process during last decades, and still remains as centre point for research [4]. Utilisation of the KBS in PLM environment is a novel approach to accelerate the entire product design and development process and enhance innovation, as it
1. Capture and re-use of design and manufacturing
knowledge 2. Digitizing and automating the cyclic and same design
tasks. 3. Encapsulation of design rules using standard parts 4. Enhanced collaboration among design, simulation,
manufacturing, and services and recycling.
This research is mainly focused on the KBS design for manufacturing processes and suggests different ways to share this knowledge in PLM environment, especially for design and manufacturing in PLM system, which has eminence in the information and knowledge domain, needed to support decision process more quicker and more effectively. KBS not only covers the process related knowledge but also the resource knowledge as a prototype in manufacturing industry. This can be extended to capture all related knowledge and information from upstream to downstream applications. MANUFACTURING KNOWLEDGE SHARING SYSTEM DESIGN
There are different techniques to tackle the challenges
faced by manufacturing industry. One of such techniques is the PLM and inter-operatiability of organisations for developing a new product. Successful implementation of a PLM system requires a knowledge base support and a rich information core accessible by all departments and organisations working in a collaborative environment at every stage of PLM. This new innovation in manufacturing knowledge utilisation will put the manufacturing industry into a new paradigm of product design and development. Knowledge base technologies enable the manufacturing industry to exist in the era of knowledge wisdom, information creation and sharing [5]. Manufacturing process in reality is a combination of tangible and intangible transformation to get the end product. Tangibly, we obtain the materialistic form of the end product, but intangibly the knowledge is created with the virtual transformation of data information and management of knowledge. Subsequently this knowledge is utilised, have no boundaries and limits in manufacturing or PLM environment, which is the essence of this research. Figure 3 explains this concept in block diagram.
Raw Materials Products Data Information Information Knowledge
MTO
ETO
CTO
DTO
ITO
MP FMS MKM PC PKM PLM
Business Model changes
Driving Force (Market Dynamics)
Technological Changes
Driving Force (Business Model)
The Manufacturing
Process
Figure 3. Real and Virtual Manufacturing Transformation
ASME 2011 Early Career Technical Journal - Vol. 10 54
METHODS AND TOOLS The KBS design is essentially based on the knowledge
transfer approach, from domain experts directly to systems. However, this KBS has been replaced by the model approach which is based on using conceptual model to model the problem-solving skill of the domain expert [6]. Different tools and methods are applied to organise the data. Various logics and controlling factors are implemented to manage the knowledge, usesd as a resource further downstream in a repetitive environment. Due to these features , knowledge base technologies have become a focus for many manufacturing organisations. Two different tools are used for design the KBS in this research.
• Unified Modelling Language (UML)
• Emergent System’s Engineering Knowledge
Management solution - E2Ks (Software)
The system functionality plays a significant role in KBS. UML is used to organise and arrange the random information and data into a specific order. The UML class diagram is used to structure the process and resource knowledge in research whilst the use-case diagram is used in the graphical representations of systems behaviour and response in terms of system output when an input is provided. E2KS is emerging as state-of-art software used for knowledge capturing in the manufacturing environment, introduced by Emergent Systems for manufacturing knowledge management solutions. E2KS provides rich interface to capture and manipulate information and knowledge for human usage. Furthermore, the captured information can be accessed and used by any tool. For example, E2KS information can be accessed within CAD environment and the CAD information can be validated against the captured E2KS information. E2KS provides software development kit (SDK) based on C++ and Java programs. The E2KS SDK can be used with CAD tools such as NX, CATIA and PLM tools like Teamcenter.
MANUFACTURING KNOWLEDGE
The most important step in this research was to determine what type of manufacturing knowledge is needed to capture and develop the methodology for that. Therefore limiting the scope of research, control convergence methodology was adopted to capture the information and knowledge for making circular type shapes, cutting tools and the materials that can be machined with. The KBS will contain all this information as a source of manufacturing knowledge.
Generally the knowledge base system will respond in two ways. Firstly the required process is identified and followed by the required tool according to the selected process. The base line was compatible with the required tolerance for the manufacturing process under specific conditions. Generally the degree of accuracy expected reasonably from various manufacturing processes under average conditions was a basis for calculation in designing the manufacturing KBS.
WORKING PRINCIPLE
The KBS is working in three steps. The first step in the process is selection under specific requirements given to the system as an Input and in second step the required tool will be selected and in third step the knowledge is shared in PLM environment as shown in Figure 4.
(Material data, Process data, Tool data)
Figure 4. Manufacturing Knowledge Base (Sharing
System) E2KS Process Overview
Step 1 Process Selection: process selection procedure for simple hole making process is shown in Figure 4. The process selection based on three input values, upper tolerance (U.T), and lower tolerance (L.T) and diameter (D) of the hole. The E2KS knowledge base is responded with suitable process. Step 2 Tool Selection: Once the process selected for required product, the KBS will select the required tool in available knowledge resource. Two input parameters are required the flute length (F.L) and diameter (D) of the hole. Generally the
Tool Data
Process Data
Material
Data
Step 1 •Process Selection
Step 2 •Tool Selection
Step 3 •knowledge sharing in
PLM environment
Manufacturing Knowledge
E2KS KBS
ASME 2011 Early Career Technical Journal - Vol. 10 55
overall overview and structure of the KBS and its utilisation is PLM system shown in Appendix A. MANUFACTURING PROCESS SCENARIO TO SHOW THE WORKING OF KBS Scenario 1 (Figure 5) uses a simple case illustrating the hole making process. Scenario 1
• Diameter (D) of the hole: 15 mm • Upper Tolerance (U.T): 50 (µm) • Lower Tolerance (L.T): -50 (µm) • Depth of hole (F.L): 55 mm
The KBS resolves to what process is suitable for required size of hole as the simulation (E2KS Process Look-Up K-PAC) shown in Figure 6.
According to the E2KS Knowledge base drilling is the required process to make the required size hole. Now the drilling tool
K-PAC (knowledge pack) will resolves the required tool. The E2KS simulation is shown in Figure 7. The KBS system shows the two different tool series with required size which both can be used for drilling the required hole size. Along with tool series, the KBS also shows the surface finish that can be achieved with this tool and the materials that can be machined with. Different types of materials can be processed with both series. The tool selection depends on the material that will be processed. This is shown in Figure 7 and it shows how the KBS proceeds with tool selection for required dimensions.
Figure 7. E2KS Tool Selection Process for Scenario 1
Scenario 2
• Diameter (D) of the hole: 4 mm • Upper Tolerance (U.T): 10 (µm) • Lower Tolerance (L.T): -5 (µm) • Depth of hole (F.L): 9 mm
Figure 5. Hole Making Scenario 1
+50 Ø = 15 + -50
Drilling is Required
Figure 6. KBS Process Selection for Scenario 1
Material Type
+10 Ø=4 -5
Required Drill Size
Surface Finish
Material Type
Available Tool
Figure 8. Hole Making Scenario 2
ASME 2011 Early Career Technical Journal - Vol. 10 56
The E2KS process and tool selection for the material in Figure 8, is shown in Figures 9 and 10. The required process is reaming, followed by available tool needs to be selected.
Now as for the reaming operation the tool is available but we cannot make the required hole size directly with reaming. First we have to drill the hole smaller than the required size. For this, first have to consult KBS, either the required drill size is available or not. Let say that for 4mm (D) size and 9 mm (F.L) reamer, required drill is 3.5 mm (D) and 9mm (F.L). Figure 11 shows the available drill size to prepare the hole for reaming operation. The problem occurs if the tools, reamer and drill are of different functionalities, like if the materials are different which can be processed or if there is different in surface finish requirements.
DISCUSSION AND CONCLUSION
The future benefits for the manufacturing industry to remain competitive in the age of highly dynamic digital manufacturing are significant for manufacturing industry if the manufacturing knowledge can be shared within the company and across the whole supply chain. It is very substantial to explore the meaning of sharing to achieve any level of success in knowledge sharing. Different approaches and techniques are used in the manufacturing industry to share the manufacturing knowledge effectively and improve the manufacturability of the system. The KBS is developed for manufacturing industry and used the knowledge capturing tool (E2KS). It demonstrates the effectiveness of knowledge base (KB) technology and approach for capturing and managing the manufacturing knowledge for manufacturing process and required cutting tools. It is also support its value in providing an effective foundation to capture manufacturing concepts and knowledge. The system was kept only to capture the manufacturing knowledge about the processes for making the circular shape designs and to check the capability of the tools. It is a tentative implementation of the proposed system where the library model is implemented in E2KS knowledge base to provide the manufacturing feedback during the product design stage and its manufacturing. Generally the knowledge base approach develops the implementation of the following functionalities;
• E2KS has a variety of option to be used for manufacturing knowledge capturing.
Required D ill Si
Surface Finish
Material Type
Available Tool
Figure 10. Tool and Materials Selection for Scenario 2
Reaming
Figure 9. Process Selection for Scenario 2
Surface Finish
Material Type
Required Reamer size
Available Tool
Figure 11. Drill Tool Selection for Scenario 2
ASME 2011 Early Career Technical Journal - Vol. 10 57
• The different K-PAC like methods K-PACs, look-up K-PACs, Calculation K-PACs etc provides different options to capture the knowledge.
• Relationship between manufacturing and design part can be captured as rules
• The rules can be very complex as multiple logical rules can be established.
• The manufacturing best practice can be captured as method K-PACs
• This E2kS knowledge base provides a foundation for manufacturing capturing and this knowledge can be shared in PLM environment.
The E2Ks knowledge base system partially implemented which can be further developed and integrated with some sophisticated functionalities like:
1) E2KS knowledge base can be shared in PLM environment tools like Teamcentre, etc
2) E2KS offers the integration facility with other CAD software tools like NX, CATIA, so the manufacturing and Design knowledge can be directly captured from CAD data.
3) E2Ks also perform the design realisation activities within the CAD environment by accessing the captured information with knowledge fusion functionality.
The integral E2Ks Knowledge base will provide customer, developer, manufacturer and supplier the capability of knowledge sharing within PLM system and successful implementations. It will improve the product lifecycle collaboration within the enterprise and will manage the product lifecycle activity effectively to accelerate the decision making process by providing the right information at the right time. Indeed the knowledge base will provide the foundation for developing a PLM system and concept. The use of ICT in the manufacturing sector will lay strong frontiers for further development and applications of PLM strategy for the benefit of industries. This research will provide a referring guide for professional researchers to conduct further research. An extensive research is currently underway to explore the inter-operation and knowledge sharing issues across the virtual manufacturing organisation and improve the future inter-operatiability of the system and knowledge sharing procedures.
NOMENCLATURE
PLM: Product Lifecycle Management KBS: Knowledge Base System U.T: Upper Tolerances L.T: Lower Tolerances F.L: Flute length D: Diameter E2Ks: Software CoP: Community of Practice K-PAC: Knowledge Packets
ACKNOWLEDGMENTS The authors acknowledge the financial support of
University of Engineering & Technology (UET) Peshawar and Higher Education Commission (HEC) of Pakistan for this research. REFERENCES
1. Group, A. A. (2001). OLM Strategy, Key to Future Manufacturing Success
2. Guixiu Qiao, C. M. (2004). Manufacturing Information Integration in Product lifeCycle Management. New York: Wiley.
3. Ameri, F., & Dutta, D. ( 2005). Product Lifecycle Management: Closing the Knowledge Loops . Computer-Aided Design & Applications , 577-590
4. Guerra-Zubiaga, D. A. (2004). A manufacturing Model to enable knowledge maintenance in decision Support Systems. Doctoral Thesis . Loughborough University
5. S.Loub, Z., & Fang, X. (2001). KBS-aided Design of Tube bending processes. Engineering Applications of Artificial Intelligence (14), 599-606.
6. R.I.M Young, A. A.-D. (2007). Manufacturing Knowldge Sharing in PLM: a progression towards the use of heavy weigh ontologies. International Journal of Production Research , 45 (7), 1505-1519.
7. Grieves, M. (2006). Product Lifecycle Management : Driving the next Generation of Lean Thinking. New York: McGraw-hill.
8. Stark, J. (2004). Product Life Cycle Management-21st Century Paradigm for Product Realisation. London: Springer.
ASME 2011 Early Career Technical Journal - Vol. 10 58
ANNEX A E2KS Knowledge Base System Overview & KB Utilisation in PLM Environment
Resource Knowledge
3-Axis VMC Knowledge
M/C Knowledge
Tool Knowledge
Material Knowledge
Metal Tool Knowledge
Tolerances Knowledge
Knowledge Capturing Tool
(E2ks)
Drilling Knowledge
Processes Knowledge
Reaming Knowledge
Types of Knowledge
Knowledge Sharing PLM Environment
PLM Environment
PLM tools
Teamcentre, CATIA etc
Facility Knowledge E2KS KBS
ASME 2011 Early Career Technical Journal - Vol. 10 59
ASME Early Career Technical Journal
2011 ASME Early Career Technical Conference, ASME ECTC
November 4 – 5, Atlanta, Georgia USA
MULTIDISCIPLINARY DESIGN OPTIMIZATION OF AEROSPACE VEHICLE – SINGLE
ENGINE ROTORCRAFT
Adeel Khalid Southern Polytechnic State University
Marietta, GA, USA
ABSTRACT Aerospace vehicle design includes several disciplines
with often-conflicting requirements. A formal system design
framework is developed in this research where the designer
coordinates with disciplinary experts to find an overall
optimized design while simultaneously optimizing disciplinary
objectives. The overall system objective function chosen in the
preliminary design is minimum production cost for a light
turbine-training helicopter. Several disciplinary objectives
including specific fuel consumption for propulsion, empty
weight for weights group, and figure of merit for aerodynamics
group are optimized. In addition to disciplinary optimization,
several analyses are performed including vehicle engineering,
dynamic analysis, stability and control, transmission design, and
noise analysis. The design loop starts from the conceptual stage
where the initial sizing of the helicopter is done based on
mission requirements. The initial sizing information is then
passed to disciplinary experts for preliminary design. The
design loop is iterated several times using multidisciplinary
design techniques like All At Once (AAO) and Collaborative
Optimization (CO) approaches. A light training helicopter is
proposed that satisfies all the mission requirements, is
optimized for several disciplines and has minimum production
cost. Merits, demerits, requirements and limitation of the
proposed methodology are discussed.
INTRODUCTION The multidisciplinary nature of the helicopter makes it
hard for the preliminary designer to estimate the actual cost of
the aircraft in the early design stage. There has been a lot of
emphasis on bringing more and more design information early
in the design stage [1, 2, 3, and 4]. In this research, a variety of
disciplinary design analyses are performed at the preliminary
helicopter design stage where the design cycle is repeated in an
iterative fashion using Multidisciplinary Design Optimization
(MDO) to ensure an overall optimized design. All At Once
(AAO) and Collaborative Optimization (CO) techniques
developed by Kroo, Sobieski, Braun et al. [5, 6, 7 and 8] are
used. These methods help integrate various design disciplines
while removing their interdependency. AAO approach solves
the problem by removing disciplinary optimizers and
introducing a multi-objective criterion for the system level
optimizer. CO allows simultaneous use of disciplinary
optimizers thus ensuring that not only an overall optimized
design is obtained, but also the design is best from disciplinary
point of view.
A light turbine-training helicopter is used as baseline
for analysis. The requirements come from the Request for
Proposal (RFP) from AHS 2006 student design competition.
The helicopter is expected to lift two 90 kg people, 20 kg of
miscellaneous equipment, and enough fuel to Hover Out of
Ground Effect (HOGE) for 2 hours, into HOGE at 6,000 ft. on
an ISA + 20oC. The winning design team at Georgia Tech made
use of a variety of software packages and codes for different
disciplines to perform the analyses. The idea is to perform the
required mission with minimum cost. A variety of software
packages and codes for different disciplines are integrated in
this research to perform analyses.
In a traditional design approaches, due to strong
dependency of disciplines on each other, it is not possible to run
several analyses in parallel and therefore the design process is
slowed down significantly. Infact, in several cases, by the end of
the design, it is not possible to complete even one design loop
involving all analyses simultaneously. This process does not
guarantee overall system cost minimization.
In this research, a platform is developed where all the
disciplinary codes, software and analyses are integrated and
several design loops are performed. The overall system design
criterion is chosen to be the minimum production cost. All the
disciplinary and the system level design constraints are
satisfied. Optimized results obtained from AAO and CO
approaches are compared. These methods, when fully
converged, ensure an overall optimized design with several
disciplines involved. A data repository is created where design
information is stored iteratively. As the design matures, new
information is obtained from disciplines and subsequent
analyses are performed. A parallel design helps reduce the
design time from several months to a few hours. The entire
design process is automated in this research. There are no
feedbacks or feed forward loops between different disciplines
ASME 2011 Early Career Technical Journal - Vol. 10 60
and all the disciplinary optimizers are retained. The weight
optimizer minimizes the empty weight of the helicopter, the
propulsion optimizer minimizes the fuel consumption and
emission, and the aerodynamics optimizer maximizes the hover
Figure of Merit. All the other disciplines are of analysis type.
The initial problem setup is time consuming and
tedious, but after the problem is set up and software packages
are integrated, information flow becomes very efficient.
ModelCenter is used as a platform for information transfer and
system level optimization. Individual wrappers are written for
data transfer between ModelCenter and disciplinary codes.
These codes include commercial software, legacy codes and
customized in house programs. Data is transferred in batch
mode for rapid analysis.
The design team estimated the average vehicle cost,
based on the production of 3000 units, to be $203,541.85 per
unit. This estimate was based on sequential disciplinary
analyses. The results obtained from MDO environment with
parallel execution show a reduction in the average unit cost to
$178,175.69. This is a 12.46% reduction in cost while all the
constraints and RFP mission requirements are satisfied. This
study demonstrates an automated framework for preliminary
helicopter design. The framework ensures that detailed
disciplinary analyses are performed in an automated manner in
parallel at the initial design stage while at the same time overall
disciplinary and system level optimized results are obtained.
The platform also allows the designer to add further detailed
disciplinary analyses so that the overall fidelity of the system
can be improved.
DESIGN METHODOLOGY The generic Integrated Product and Process Development
(IPPD) methodology allows the engineers and program
managers to decompose the product and process design
iterations [5]. The product development cycle of IPPD
methodology is further divided into the conceptual and
preliminary design loops. In this research, disciplinary analyses
are identified, linked with each other through a common
platform and preliminary design loop iteration is performed in
an automated fashion. This approach provides an opportunity to
perform system optimization using MDO techniques.
Several requirements exit for a framework to provide an
easy to use and robust MDO environment. Key attributes for the
MDO environment, as listed by Sobieszczanski Sobieski [3],
are computer speed, computer agility, task decomposition,
sensitivity analysis, human interface and data transmission. The
framework requirements for MDO application development
have been outlined in the work of Salas and Townsend [6] as
architectural design, problem formulation, problem execution,
and access to information.
Sobieski [3] indicates that among several tools that
specialize in process integration and exploration, ModelCenter
from Phoenix Integration Inc. focuses on product modeling and
ease of tools and process integration across distribution and
heterogeneous computing environments. ModelCenter is used in
this research because of its flexibility to incorporate several
existing commercial packages e.g. Excel, Matlab, MathCad,
CATIA etc. ModelCenter also facilitates the use of wrappers to
integrate in-house legacy codes.
Two MDO approaches, i.e. All At Once (AAO) and
Collaborative Optimization (CO) are used in this research to
resolve the conflicting objective functions of different
disciplines and the system level optimizer. AAO approach
dictates that all the local design variables and constraints are
moved to the system level. The local disciplinary optimizers are
eliminated. The problem is reduced to a single level scheme
with just one system optimizer. AAO approach with the
capability of parallel execution of disciplines is depicted in
Figure 1. With this approach, the disciplines become
independent of each other. This approach facilitates the
information flow from the repository to the respective
disciplines and back to the repository in an automated fashion.
Figure 1. AAO Approach with a Central Data Repository
When the feedback loops are removed, compatibility
constraints are added to the system level optimizer. The generic
system level problem is defined as follows.
Objective Function: Minimize F
Subject To: BA ggg ,,
Variables: BA XXX ,,
Where
F = Overall Evaluation Criterion (OEC)
g = System level constraints
BA gg , = Compatibility constraints
02
'≤−−= εBBg A
02
'≤−−= εAAg B
X = System level variables
A
B
System
Optimizer
Repositor
y
ASME 2011 Early Career Technical Journal - Vol. 10 61
BA XX , = Disciplinary variables
Where
BA, = Actual output vectors from disciplines A and B
'' ,BA = Intermediate variables
The intermediate variables are copies of true outputs from
disciplinary analyses. These variables are additional
independent variables and are treated like design variables that
the optimizer has to serve. The compatibility constraints ensure
that the difference between the disciplinary outputs and outputs
from system level result in the same values. This approach
eliminates iterations between disciplines. There is no
optimization conflict between disciplines. However the system
level optimizer can become very large for a problem with
several disciplines and several variables. AAO also removes the
control of local variables by local experts or local optimizers.
The expert codes are reduced to simple analysis only mode,
making them servants to the system level optimizer. No problem
solving occurs at the local level. Some of these issues are
addressed by the CO approach.
Collaborative Optimization is a design architecture
developed by Sobieski et al [2, 3], specifically created for large-
scale distributed analysis applications. In this approach, a
problem is decomposed into user-defined number of subspace
optimization problems that are driven towards interdisciplinary
compatibility and the appropriate solution by a system level
coordination process. The fundamental concept behind the
development of Collaborative Optimization architecture is the
belief that disciplinary experts should be able to contribute to
the design process while not having to fully address local
changes imposed by other groups of the system.
To facilitate this decentralized design approach, a problem
is decomposed into sub-problems along domain specific
boundaries. Through subspace optimization, each group is
given control over its own set of local design variables and is
charged with satisfying its own domain specific constraints.
Communication requirements are minimal because knowledge
of other group’s constraints or local design variables is not
required. The objective of each sub-problem is to reach
agreement with the other groups on values of the
interdisciplinary variables. A system level optimizer is
employed to orchestrate this interdisciplinary compatibility
process while optimizing the overall objective. To avoid the
conflict between disciplinary objectives, collaborative
optimization replaces the objective functions of each
disciplinary optimization.
The new objective function attempts to minimize a newly
defined error function, known as J term. These J terms measure
the relative error between the output variables of the
disciplinary tools and corresponding target values. These target
values are set by the system level optimizer, which is configured
to optimize a system level objective function under the
constraint that the J terms in each discipline are kept within a
certain tolerance. Each disciplinary tool is allowed to vary all of
its usual inputs and local variables to minimize its own
objective function. This allows for the disciplinary experts to
focus on their domain specific issues while maintaining
interdisciplinary compatibility. Collaborative Optimization in a
Design Structure Matrix (DSM) format is shown in Figure 2. A
generic Collaborative Optimization problem is given as follows.
Figure 2. Collaborative Optimization Design Architecture
ASME 2011 Early Career Technical Journal - Vol. 10 62
Base
airfield
Training
airfield
0
1 2
3
4
5
6
7
8
10 9
System Level Problem:
Objective Function: Minimize f
Constraints: 0,, ≤CBA JJJ
Variables: ''' ,,, CBAX
Disciplinary Problem A:
Objective Function: Minimize 2
'2
'2
' AACCBBJ AAA −+−+−=
Subject To: 0≤Ag
By Changing: AAA CBX ,,
Similar problems are defined for disciplines B and C. This
decomposition strategy allows for the use of existing
disciplinary analyses without major modification and is also
well suited to parallel execution across a network of
heterogeneous computers.
A complex system like rotorcraft design is composed of
several levels of problems. Each system is divided into further
subsystems. Several models including in-house codes,
commercial software, legacy codes and programs are integrated
in this research using ModelCenter as the common platform.
ROTORCRAFT MDO MODELS AND ANALYSES
Light Turbine Training Helicopter (LTTH) is chosen for the
application of the IPPD methodology using MDO tools.
Detailed disciplinary tools, analyses, software packages, and
programs are identified and integrated using a common
platform. The integration of disciplines using a common
platform enables the transfer of design information from one
discipline to another in an efficient manner. A centralized
database is created where all the latest design information from
all the disciplines is stored. The dynamic platform enables the
application of optimization techniques at the system level.
LTTH baseline information comes from the 23rd annual student
design competition 2006 Request For Proposal (RFP) [5]
published by the American Helicopter Society (AHS). The goal
is to develop a two-place single turbine engine, training
helicopter that is affordable. The mission requirements and
some of the system level constraints are also defined in the RFP.
This includes the capability to lift two 90kg people, 20kg of
miscellaneous equipment, and enough fuel to Hover Out of
Ground Effect (HOGE) for 2 hours, into HOGE at 6,000 ft. on
an International Standard Atmosphere (ISA) + 20oC day. The
winning LTTH design team at Georgia Tech further refined
these requirements and new details are added. Based on the
RFP, a mission profile is generated as shown in Figure 3.
The LTTH design team developed a conceptual baseline
vehicle using the performance requirements stipulated in the
RFP. This was followed by preliminary design, which provides
more detailed analysis in multiple disciplines to identify the
necessary baseline vehicle modifications. Disciplinary analyses
Figure 3. Mission Profile for LTTH design
include aerodynamic performance optimization, structural
design, analysis, and material selection, CAD modeling,
helicopter stability and control analysis, dynamic analysis,
propulsion system design, helicopter training industry research
and cost analysis. The goal of the proposed design can be
summarized as reducing the cost while improving product
quality and value.
Important disciplines involved in the preliminary design
loop of IPPD methodology are identified in this research. All
possible tools, analysis packages, commercial software, legacy
codes and in house programs are utilized and integrated using a
common platform i.e. ModelCenter. Individual optimization
problems are defined for propulsion, economics, weight and
balance and aerodynamics groups. Details of the analyses used
in each discipline are discussed in the following section.
PERFORMANCE ANALYSIS Two methods that are used for performance analysis
are the Fuel Ratio (RF) method and the Georgia Tech
Preliminary Design Program (GTPDP), which is based on
regression of historical data.
Extended RF method for advanced VTOL aircraft is used
to size the vehicle in this research. It is a graphical technique for
the parametric analysis of aircraft for a given design
specification, and provides a preliminary design tool for
investigation of the interrelated effects of significant design
parameters on the gross weight of the aircraft. One of the
biggest limitations of RF method is that it only allows the
designer to minimize the gross weight. Only a few variables are
involved in the analysis. More comprehensive preliminary
design tools like HESCOMP and GTPDP are available for
initial sizing. GTPDP is utilized iteratively in the design loop in
this research.
GTPDP is a preliminary performance analysis program in
nature based on an energy approach and has been written to
yield results quickly and inexpensively [5]. Yet, the results are
of sufficient accuracy that sound understanding of a helicopter
behavior can be obtained and valid comparison can be made.
The mission profile analysis is also performed in GTPDP.
GTDPD is simple and quick preliminary helicopter design code
but the calculations are approximate and it uses simple
approaches and equations.
ASME 2011 Early Career Technical Journal - Vol. 10 63
The initial outputs obtained from the baseline vehicle
analysis in GTPDP are used as inputs for the other disciplinary
analyses. Besides the initial analysis, GTPDP is also used in the
system design iteration to get approximate mission performance
output. GTPDP is integrated with ModelCenter, so information
can be transferred from GTPDP to other disciplines.
VEHICLE ENGINEERING
Besides developing graphical models of vehicle
components, a design package is used for several other analyses
including weight and moment estimates. A state of the art design
package, CATIA V5 is used for the CAD design of vehicle in
this study. LTTH design team developed several detailed
models of components of the baseline helicopter. These
components are then integrated using assembly function.
Isometric and three view baseline vehicle drawings developed
by the design team are shown in Figure 4. With the help of these
parametric models, the vehicle geometry can be updated as
design values change. Some of the data that is extracted from
the models include the moment of inertias, surface areas,
volumes, and masses of individual components. This
information is then passed on to respective disciplines as
needed.
STABILITY AND CONTROL AND TRIM ANALYSIS A variety of analysis tools including the Georgia tech
Unified Simulation Tool (GUST), FlightLab and Matlab trim
analysis code and linear control root locus plots are investigated
for stability and control discipline in this research. GUST, used
at Georgia Tech for UAV research and development, has been
designed to take input parameters from rotor dynamics,
aerodynamics, gear dynamics, and flight controls with sensors –
enabling it to monitor flight characteristics and perform
missions using trajectories in real life scenario [5].
GUST is an instrumental tool in the pilot development
process. FlightLab on the other hand is an industry standard
program. For integration with preliminary design loop, Matlab
based trim analysis and root locus analyses are performed. Pitt
and Peters [9] first harmonic inflow is assumed. The detailed
trim analysis using all the nonlinear equations ensures a
complete and comprehensive helicopter system.
AERODYNAMIC ANALYSIS A combination of preliminary and detailed analyses is
performed in this research. The results obtained from low
fidelity analysis help accelerate the system level iterations. The
high fidelity results obtained from CFD are introduced from
time to time to get better results. A FPDA (Flat Plate Drag Area)
code is used for calculating component drag. Blade element
theory is used to calculate the forces of the blade due to its
rotation through air, and hence the forces and performance of
the entire rotor. Blade element theory combined with Landgrebe
wake model [3, 4] is also used to evaluate the rotor hover
performance. The blade element code with Landgrebe wake
model is integrated with ModelCenter and is used in
conjunction with simplified closed form expression of Blade
element momentum theory model for forward flight. In hover
case, Figure of Merit is maximized by changing the variables
that directly affect hover performance and keeping the
remaining variables fixed at the baseline value. The
optimization problem is given as follows:
Figure 4. Three-view drawing of baseline vehicle
ASME 2011 Early Career Technical Journal - Vol. 10 64
Aerodynamic
Data
Aerodynamic
Data Repository
Optimizer
BEMT
Model
FPDA
Model
NASCART
Model
From System Optimizer
To System Optimizer
Objective Function: Maximize Figure of Merit
Variables:
-12 ≤ Twist angle ≤ -5
2 ≤ No. of blades ≤ 4
1% ≤ Root cutout ≤ 15%
A drag buildup method is used to estimate the equivalent
flat plate drag area of the vehicle. The FORTRAN based drag
estimation code is integrated with ModelCenter. A summary of
Aerodynamic analysis integration is shown in Figure 5.
Figure 5. Summary of Aerodynamic Analyses
PROPULSION ANALYSIS Historical light turbine engines are compared on the
basis of engine weight, specific fuel consumption, compressor
pressure ratios, and mass flow rate. This comparison gives an
approximate idea of the size, weight and compression ratio of
the new engine to be designed. Various analyses packages
available commercially and non-commercially for turbine
engine design are explored. These include the NASA Engine
Performance Program (NEPP), On-Design (ONX) and Off-
Design (OFFX) from Aircraft Engine Design AEDsys [5] and
Gas Turb 10. Gas Turn 10 is a sophisticated program available
for on-design and off-design cycle analysis for turbo shaft
engines. Parametric studies, Monte Carlo simulations and cycle
optimization tasks are completed using this program. An
optimization problem is defined in the propulsion group to
minimize the SFC by changing the size of the engine and
keeping the engine cycle parameters constant. Allison 250-C 20
engine is use for Gas Turb 10 calibration. The optimization
problem is defined as follows.
Objective Function: Minimize SFC
Constraints:
x1 ≤ Mass Flow Rate ≤ x2
y1 ≤ Pressure Ratio ≤ y2
Variables:
Mass Flow Rate
Pressure Ratio
Sequential Quadratic Programming (SQP) is used to
perform the optimization. The results are discussed in the
results section.
ECONOMIC ANALYSIS Economic analysis is of significant importance in this study
because of the fact that the total cost of the vehicle is used as
the Overall Evaluation Criterion (OEC) for the system level
optimizer. All the other disciplines work not only to maximize
their own individual criteria but also minimize the overall cost
of the system. Therefore minimum cost is of interest to every
discipline.
The cost analysis packages used in this research include the
GTPDP cost model [5], Bell cost model and Price H model.
Preliminary cost estimates obtained using GTPDP are group
weights based on historical data. More detailed development,
recurring production, and operating and support cost analyses
are performed using PC based Bell cost model. The model
predicts the cost of each aircraft subsystem by dividing it into
three separate categories: sub-contractor, labor, and materials. A
weight-cost factor is calculated for the baseline design by the
LTTH design team to identify the real cost drivers.
The five areas identified as the most influential are the
power plant, fuselage, flight controls, drive system, and rotor.
These subsystems with their sub-contractors, labor, and material
categories allocated by percentage for the baseline vehicle are
shown in Figure 6. This figure demonstrates the strong
influence of material selection for the fuselage, flight controls,
drive system, and rotor; accounting for over 50% of the total
vehicle production cost. The total average cost for LTTH
baseline design is $200,576. This cost includes both direct and
indirect operating costs. The baseline cost is used as a starting
reference for the objective function of system level design loop.
Direct and indirect costs are also calculated using Bell cost
model. The design team used Price H model to calculate the
new engine cost.
The PC based Bell cost model is integrated with
ModelCenter using a built-in ModelCenter API. The economics
optimization problem is given as follows:
Objective Function: Minimize Avg. Prod. Cost
Constraints: Production units ≥ 3,000
Production rate ≥ 300/yr.
Variables: Number of blades (MR)
Number of blades (TR)
ASME 2011 Early Career Technical Journal - Vol. 10 65
Figure 6. Cost structure for major cost driving systems
RESULTS AND DISCUSSION The application of MDO techniques in the preliminary
rotorcraft design stage involves identification of all the
disciplines involved, their key variables, disciplinary
constraints, objective functions, and optimum results. The goal
is to design a rotorcraft starting from a baseline in minimum
amount of time, at minimum cost while satisfying disciplinary
and system constraints.
Four different MDO problems are solved in this study.
Before performing the AAO and CO analyses, which are
computationally expensive, it is prudent to verify the new
design environment against known design. This provides a level
of confidence in the program design environment and reveals
any inconsistencies within the integration process. The baseline
LTTH is used to verify the analyses that are used. Analyses
performed with the new framework are discussed in problem 1.
There is no optimization involved in any of the disciplines. The
disciplines are reduced to simple analysis modes. This ensures
that the results obtained match with those obtained from AHS
design team and hence prove that the framework developed, its
integration, and connectivity of analyses are correct. In problem
2, individual disciplinary optimizers are included but no system
level optimization is performed. So individual disciplines are
optimized but overall system may not be optimal. In problem 3,
AAO approach is employed. Problem 3 has two parts. In the
first part, three is a single system level objective function, i.e.
cost, that is of interest to every discipline but there are no
disciplinary optimizers. In the second part, an Overall
Evaluation Criterion (OEC) is defined that makes it a Multi-
Objective optimization problem. The OEC is a composite of all
the objective functions of individual disciplines that have
optimizers. The results of problem 3 with the OEC are shown in
Figure 7. Finally, in problem 4, Collaborative Optimization is
performed at the system level; as well as individual disciplinary
optimizations are performed at the disciplinary levels. The
individual disciplinary objective functions are modified from
those of Problem 2 to ensure that there is no conflict between
optimizers. The convergence history and results of problem 4
are shown in Figure 8. The results of all the problems are
compared. Some of the important baseline variables are shown
in Table 1.
Table 1: Results obtained from baseline analysis
Main rotor radius 12.2 ft.
Main rotor tip speed 650 ft./sec
Main rotor chord 0.64 ft.
Main rotor twist angle -10 deg
Main rotor effective hinge offset 0.07
Main rotor vertical distance from C.G 6.64 ft.
Main rotor lateral offset from C.G 0 ft.
Main rotor longitudinal offset from C.G 0.33 ft.
Pre cone angle 2.75 deg
No. of main rotor blades 3
Figure of Merit 0.68
Tail rotor radius 2.25 ft.
Tail rotor twist angle 0 deg
Tail rotor pre cone angle 1.5 deg
Tail rotor blade chord 0.23 ft.
Engine takeoff Horsepower 184 HP
Engine max continuous power 160 HP
Vehicle empty weight 800 lbs.
Vehicle gross weight 1454 lbs.
Vehicle flat plate drag area 7.29 ft2
Flyover noise 90.6 dB
Tail boom length 17 ft.
Average unit production cost $203,541
Direct operating cost $93.48
Figure 7: Convergence history of AAO single objective approach
$178,175
$203,576
ASME 2011 Early Career Technical Journal - Vol. 10 66
Figure 8: Convergence history of AAO with multi-objective approach
CONCLUSIONS Key components involved in the rotorcraft preliminary design
process are identified in this study. Design tools available both
at the conceptual and preliminary design stages are determined.
Capabilities and limitation of these design tools are identified.
These design tools include commercially available software,
legacy codes, and in house programs. Design variables that are
of interest to more than one discipline are linked via a
repository. The architecture enables the transfer of information
between different tools in an integrated manner.
According to IPPD methodology, the detailed mission
analysis is performed at the conceptual stage of the design. The
suggested design methodology allows the integration of various
disciplines in a plug and play fashion. The framework allows
the designer to visualize the effects of change of important
design variables simultaneously at all the disciplines involved.
Objectives and constraints of various disciplines are identified.
Few optimization techniques that facilitate this architecture are
applied in this study. These include individual disciplinary
optimization, All At Once approach, and Collaborative
Optimization.
A light turbine training helicopter is chosen for the proof of
concept. Different optimization problems are compared and
their results are analyzed. It is observed that system
optimization problem requires significant more execution time
than individual design analyses. However the overall design
time is reduced from several months to a few hours with the aid
of the integrated and automated design architecture. Although
the setup time is significant, an overall optimized design is
obtained. It is observed that by making little changes in the
design variables, all the disciplinary constraints are satisfied,
and a helicopter with minimum cost is designed. In summary,
following improvements are observed in the preliminary
rotorcraft design with the use of MDO techniques.
• The design process is automated
• Disciplinary inter-dependency is removed which facilitates
parallel execution of analyses
• Current and updated information is available to all the
disciplines at all times during the design process
• The overall time required to design a rotorcraft is
significantly reduced
Disciplinary and system optimization is possible using
MDO techniques
ACKNOWLEDGEMENTS The author would like to thank the U.S. army for funding
this project through various grants and is especially grateful to
Dr. Daniel Schrage, School of Aerospace Engineering, Georgia
Institute of Technology for supervising the research work that
was done as part of the author’s Ph.D. thesis.
REFERENCE: [1] Mavris, D., Baker, A., Schrage, D., “IPPD through robust
design simulation for an affordable short haul civil tilt rotor”,
Proceedings of the American Helicopter Society 53rd Annual
Forum, Virginia Beach, VA, April 29 – May 1 1997
[2] Braun, P. J. Gage, Kroo, I. M., Sobieski, I., “Implementation
and Performance issues in collaborative optimization”, NASA
Langley Research Center, Proceedings of the 6th
AIAA/USAF/NASA/ISSMO symposium on Multidisciplinary
Analysis and Optimization, September 4-6, 1996
[3] Sobieski, I., Kroo, I. M., “Aircraft design using
Collaborative Optimization”, Proceedings on the AIAA 34th
Aerospace Sciences meeting and exhibit, Reno, NV. January
15-18, 1996
[4] Kirby, M. R., 2001, “A methodology for technical
identification, evaluation, and selection in conceptual and
preliminary aircraft design”, Ph.D. dissertation, Georgia
Institute of Technology
[5] Khalid, S. A., 2006, “Development and implementation of
rotorcraft preliminary design methodology using
multidisciplinary design optimization”, Ph.D. dissertation,
Georgia Institute of Technology
[6] Salsa, A. O., Townsend, J. C., “Framework requirements for
MDO application development”, AIAA paper no. AIAA-98-
4740, 7th AIAA/USAF/NASA/ISSMO symposium on
Multidisciplinary Analysis and Optimization, St. Louis, MO.,
September 2-4, 1998
[7] Braun, R. D., Moore, A. A. Kroo, I. M., “Use of
Collaborative Optimization architecture for launch vehicle
design”, NASA Langley Research Center, Proceedings of the 6th
AIAA / USAF / ISSMO / Symposium on multidisciplinary
analysis and optimization, AIAA Paper No. 96-4018, September
4-6, 1996
[8] Braun, R. D., Moore, A. A., Kroo, I. M., “Collaborative
approach to launch vehicle design”, Journal of Spacecraft and
Rockets Vol. 34, No. 4, July-August 1997
[9] Pitt, D. M. and Peters, D. A., “Theoretical prediction of
dynamic inflow derivatives”, Vertica, Vol. 5, No. 1, 1981
OEC
ASME 2011 Early Career Technical Journal - Vol. 10 67
ASME Early Career Technical Journal 2011 ASME Early Career Technical Conference, ASME ECTC
November 4 – 5, Atlanta, Georgia USA
OPTIMAL LIQUID FILM THICKNESS OF AN EVAPORATING FILM DOWN AN INCLINED PLANE FOR MAXIMUM HEAT FLUX IN SPRAY COOLING
APPLICATIONS
Richard Opoku1and John P. Kizito2 Mechanical Engineering Department, McNair Building
North Carolina A&T State University Greensboro, N.C 27411, USA
ropoku@ncat.edu and jpkizito@ncat.edu
ABSTRACT Spray cooling technique as a thermal management
scheme for high heat flux applications has been reported to depend on major parameters such as: the spray droplet size, the spray nozzle type, fluid volumetric flux, spray angle, the surface configuration of the test specimen, working fluid type and the liquid film thickness that forms on the test surface. In this present paper, theoretical investigation has been carried out to determine the optimal non-wavy liquid film thickness to maximize Critical Heat Flux (CHF) in spray cooling applications. A temperature function has been obtained for a heat flux correlation to estimate an optimal liquid film thickness for maximum heat flux of an evaporating thin liquid film flowing down an inclined heated flat surface.
Our results indicate that higher heat fluxes are obtained for vertical plates than inclined planes for the same liquid film thickness and fluid upstream conditions. Maximum Critical Heat Flux (CHF) of 800 W/cm2 was achieved for an optimal liquid film thickness of 400 µm. However, for liquid film thickness above 2000 µm, it is observed that the heat flux is independent of the angle of inclination of the test surface. NOMENCLATURE
u = Velocity in x-direction v = Velocity in y-direction Um = Mean film velocity Ts = Temperature of plate surface To = Free-stream temperature ρo = Free-stream density P = Pressure r = Liquid to vapor density ratio g = Acceleration due to gravity ρl = Density of liquid film
µl = Dynamic viscosity of liquid δ = Liquid film thickness ρv = Density of vapor kl = Thermal conductivity of liquid hfg = Latent heat of vaporization Vn = Evaporating film velocity q = Heat Flux β= Volumetric thermal expansion coefficient µФ`= Viscous dissipation term INTRODUCTION
Previous research works in spray cooling have identified Critical Heat Flux (CHF) to be highly dependent on the liquid film thickness on the test surface. Previous researchers have therefore adopted different mechanisms to obtain thin liquid film on the test surface to enhance heat transfer coefficient and critical heat flux. Zhang et al. [1], experimentally investigated liquid film flow down a heated and/or cooled vertical plate using an infra-red imaging camera. They observed that for a heated plate, the flowing liquid film contracted reducing the wetted surface area; whilst for a cooled plate, the liquid film extended thereby increasing the wetted surface area. The contraction and/or extension of the flowing liquid film on the heated and/or cooled surface were attributed to lateral Marangoni effect due to a heating temperature difference and the liquid flow rate.
Toda [2-3] reported in his experimental investigation that heat flux is a function of the wall superheat and thin liquid film thickness that forms on the test surface. He observed that for a critical thin liquid film thickness on the test surface, direct liquid evaporation into the vapor phase ensues without bubble generation. However, for film thickness beyond the critical film
ASME 2011 Early Career Technical Journal - Vol. 10 68
thickness, nucleate boiling was prominent and heat flux was reduced due to vapor patches on the test surface which decreased the wetted surface area. In a spray cooling experiment by Yang et al. [4-5], film thickness measurement of 85-235 µm was reported. It was reported that the film thickness augmented the heat transfer mechanism; however, the heat flux magnitudes were not reported in their paper. Model simulation of fluid droplet impact on vapor bubble growth and bursting in a thin liquid film of thickness 44.17 µm was conducted by Selvam et al. [6-8]. Their study showed that droplet impingement during bubble nucleation increased mixing of the thin liquid film on the heater surface which also increased the heat flux.
Martinez-Galvan et al. [9], conducted experiments on film thickness measurements using high speed camera equipped with a long distance microscope. In their investigation, they found out that there exists a relation between the variation in the average Nusselt number and the film thickness along the spray cooling boiling curve. They indicated that the heat transfer regimes along that curve are related not only with a variation in the average Nusselt number but also with changes in the film thickness. In the work of Gong et al. [10], micro-conductive probes and confocal optical sensors were used to measure the instantaneous film thickness in an isothermal flow over a silicon wafer to obtain the film thickness profile and the interfacial wave characteristics. The dynamic thickness of an evaporating film on a horizontal silicon wafer surface was recorded using the optical sensor in their experiment. Their results indicated that a critical film thickness (84 µm) initiated film instability on the silicon wafer at heat flux of 56 kW/m2 (5.6 W/cm2).
Bhattacharya et al. [11], theoretically estimated heat transfer involved in spray evaporative cooling from single droplet studies viewpoint with the notion that a spray is equivalent to a multi-droplet array of liquid at low spray flux density. They developed an analytical expression of droplet evaporation time from fundamental heat transfer perspective to estimate strip cooling rate. Their analytical model developed predicts that it is possible to achieve an anomalously high strip cooling rate of Ultra Fast Cooling in a 4 mm thick steel strip by spray evaporative cooling provided the fluid droplet size is reduced to 70 μm. They also observed that smaller droplets were capable of providing the increased cooling load of Ultra Fast Cooling for thicker steel strips. Adiabatic and diabatic film thickness measurement was conducted by Pautsch et al. [12] in a spray cooling experiment using FC-72 as the working fluid. In their experiment, they observed that regions of the test die that exhibited the poorest heat transfer performance had the thickest liquid film. The reduced heat transfer performance was attributed to vapor patches in the thick liquid film.
Review of the literature indicates that the liquid film thickness on the test surface is one of the major parameters that affects critical heat flux and heat transfer coefficient in spray cooling experiments. However, not enough work has been done on the optimal liquid film thickness that will result in a maximum heat flux and enhanced heat transfer coefficient. The
present work is, thus, focused on liquid film thickness on the test surface and how it affects heat flux. MODEL FORMULATION
In this work, a model analysis of a thin liquid film flow over a heated flat surface is considered. The flat plate is subjected to a heat flux ( q ), with liquid flow at free-stream
conditions (To, ρo, µl) on the surface. The plate is inclined at an angle, θ. Figure 1 below shows the representation of the model.
Figure 1. Model representation of thin liquid film
flow down an inclined heated plate The present formulation is based on ultra thin (µm) liquid film flow over an inclined heated surface. The mean non-wavy film thickness (δ) model is used. As the thin liquid film flows on the heated surface, heat transfer from the metal plate to the liquid in contact with the plate is by pure conduction. At the liquid-vapor interface, the heat transfer is by latent energy due to phase change of the liquid into the vapor phase. Thus, evaporating thin liquid film without nucleate boiling is considered in the present analysis. For the present analysis, evaporation of the thin liquid film ensues such that the liquid film recedes at a velocity Vn between the liquid and vapor inter-phase. Thus the wavy hydrodynamic instability which occurs for liquid flow down an inclined plane is mitigated due to the liquid film evaporation. The continuity, momentum and energy conservative laws are applied to obtain the fluid transport properties for the evaporating liquid film. The effect of the liquid film thickness on critical heat flux is the specific objective of the present analysis.
ASME 2011 Early Career Technical Journal - Vol. 10 69
ANALYTICAL CONSIDERATION The conservative transport equations describing the
velocity and temperature field for a laminar liquid flow over a flat plate is considered with the following approximations and boundary conditions:
1. No slip condition at the wall: 0)0()0( ==== yvyu
2. No penetration effects: 0),( =yxv
3. Velocity boundary layer approximations:
2
2
2
2
;x
u
y
u
x
u
y
u
∂∂>>
∂∂
∂∂>>
∂∂
4. The body force in x-momentum equation for an inclined place: )sin(θρ g
5. Viscous dissipation term is negligible: 0=Φμ
6. Constant fluid properties except density which depends on temperature
7. No heat generation inside the fluid: 0=genQ
8. Thermal boundary layer approximations:
2
2
2
2;
x
T
y
T
x
T
y
T
∂
∂>>
∂
∂∂∂
>>∂∂
9. Phase change at the liquid vapor inter-phase is by latent energy:
nVfghlyq ρδ == )(
10. Heat flux at the wall:
0=∂∂−=
yy
Tkq
With the above assumptions and the boundary layer
approximation for the pressure gradient, gvx
P ρ=∂∂
; the x-
momentum equation reduces to:
(1) ))sin((
2
2
l
vg
y
u
μρθρ −
−=∂
∂
The velocity profile in the flow for a film thickness (δ ) is thus obtained as:
(2) 2
1))sin((
)(
−
−=
δμδρθρ y
l
yvgyu
The mean velocity of the falling liquid film is obtained by integration of the velocity profile over the film thickness (δ ). The mean film velocity is obtained as:
(3) 3
2))sin((
l
vgmU
μδρθρ −
=
Using Boussinesq approximation for density on temperature dependence for an evaporating film, a temperature function is obtained as:
(4) 12
3
)sin(
11
1
+
+−= oT
vg
mU
rT l
ρδ
μθβ
where: , ratiodensityr
v
o
ρρ
=
THIN LIQUID FILM EVAPORATION
The current knowledge base has indicated that liquid film evaporation is associated with higher heat fluxes than nucleate boiling. Vapor patches on the surface of test specimen in nucleate boiling heat transfer reduce critical heat fluxes. Thus to maximize critical heat flux in spray cooling, liquid film evaporation is ensured. Using the zero film thickness model/assumption for maximum heat flux, the liquid film instantaneously vaporizes (active thermal flashing) into the vapor state without bubble generation. In such case, the total heat flux is the sum of the sensible and latent energy terms. The total heat flux is thus expressed as:
(5) latentqsensible
qtotalq +=
Noting that the phase change occurs at the liquid-vapor interface ( δ=y ), the flux at the interface following Stefan’s
condition [13] is expressed as:
(6) )(δ
ρ=∂
∂−=yy
TvklknVfghl
Combining the latent energy term and the sensible energy using the temperature function for the thin liquid film evaporation, the total heat flux is obtained as:
( ) (7) 12
3
sin
11
1nVfghl
vg
mUlroTsTlk
q ρρδ
μθβδ
+
+−−−−=
ASME 2011 Early Career Technical Journal - Vol. 10 70
RESULTS AND DISCUSSION We have obtained results with the above analysis using
water as the working fluid Figure 2 shows the effect of the liquid film thickness on the maximum heat flux that can be obtained for different plate inclinations. From Figure 2, it is observed that highest maximum heat flux is obtained for a vertically oriented flat plate with liquid film thickness of about 0.4 mm.
Figure 2. Effect of liquid film thickness on Heat Flux for different angle of inclination
Figure 2 also shows that lower heat fluxes are obtained with decreasing angle of inclination. It is noted that for all angles of inclination, a critical film thickness is reached where a maximum heat flux is obtained. With film thickness higher than the critical film thickness, heat flux is mitigated. Thus for practical applications where maximum heat fluxes are to be obtained, an optimal film thickness should be maintained on the heated surface to ensure efficient film evaporation.
Figure 3 shows the effect of the mean film velocity on
the liquid film thickness on the heated test surface. From Figure 3, it is observed that increasing the film velocity increases the film thickness. For an inclined plate at an angle of 10o, the critical film thickness is obtained at mean velocity of about 0.25 m/s. For vertical and other inclination angles above 10o, maintaining mean velocity 0.3-0.5 m/s will ensure that optimal thin liquid film ensue on the heated surface.
Figure 3. Film thickness versus mean liquid film velocity
CONCLUSION Evaporating thin liquid film on a heated flat surface
has been considered in this study. An expression for heat flux as function of film thickness based on film evaporation has been derived. From the present investigation, it is observed that a maximum heat flux can be obtained when an optimal thin liquid film thickness is maintained on the test surface. Maximum heat flux of 800 W/cm2 was obtained for 0.4 mm liquid film thickness on a vertically oriented heated surface. It is also observed that for the same liquid film thickness, the maximum heat flux decreases with decreasing angle of inclination of the heated test surface.
ACKNOWLEDGEMENT This work was supported by Air Force Research Laboratory (AFRL) through Universal Technology Corporation. REFERENCES
[1]. Zhang Feng, Y.T. Wu, and J. Geng: An investigation of
falling liquid films on a vertical heated/cooled plate. International Journal of Multiphase Flow, 2008. 34(1): p. 13-28.
[2]. Toda S: A study of mist cooling (1st report: investigation of mist cooling). Trans Jpn Soc Mech Eng, 1972. 38:581–8.
[3]. Toda S: A study of mist cooling (2nd report: theory of mist cooling and its fundamental experiments). Trans Jpn Soc Mech Eng, 1973. 39:2160–93.
ASME 2011 Early Career Technical Journal - Vol. 10 71
[4]. Yang J, Pais M, and L. Chow: Critical heat flux limits in secondary gas atomized liquid spray cooling. Exp Heat Transfer, 1993. 6:55–67.
[5]. Yang, J., L. Chow, and M. Pais: Nucleate boiling heat transfer in spray cooling. ASME J Heat Transfer 1996. 118:668–71.
[6]. Selvam R.P., L. Lin, and R. Ponnappan: Computational modeling of spray cooling: current status and future challenges. In: Space technology and applications international forum (STAIF 2005), February 13–17, Albuquerque, NM;, 2005. 746. p. 56–63.
[7]. Selvam RP, L. Lin: Computer modeling of liquid droplet impact on Heat transfer during spray cooling. In: ASME summer heat transfer conference, July 17–22, San Francisco, CA., 2005. HT2005-72569.
[8]. Selvam R., L. Lin, and R. Ponnappan: Direct simulation of spray cooling: effect of vapor bubble growth and liquid droplet impact on heat transfer. International Journal of Heat Mass Transfer, 2006. 49:4265–78.
[9]. Martinez-Galvan E, J.C. Ramos, and R. Anton: Film Thickness and Heat Transfer Measurements in a Spray Cooling System With R134a. Journal of Electronic Packaging, 2011. 133(1).
[10]. Gong S. J, W.M. Ma, and T.N. Dinh: Diagnostic techniques for the dynamics of a thin liquid film under forced flow and evaporating conditions. Microfluidics and Nanofluidics, 2010. 9(6): p. 1077-1089.
[11]. Bhattacharya P, A.N. Samanta, and S. Chakraborty: Spray evaporative cooling to achieve ultra fast cooling in runout table. International Journal of Thermal Sciences, 2009. 48(9): p. 1741-1747.
[12]. Pautsch A.G. and T.A. Shedd: Adiabatic and diabatic measurements of the liquid film thickness during spray cooling with FC-72. International Journal of Heat and Mass Transfer, 2006. 49(15-16): p. 2610-2618.
[13]. Shyy Wei, H.S. Udaykumas, and M.M. Rao: Computational Fluid Dynamics with Moving Boundary. Dover Publications, Inc, Mineola, New York.
ASME 2011 Early Career Technical Journal - Vol. 10 72
ASME Early Career Technical Journal 2011 ASME Early Career Technical Conference, ASME ECTC
November 4–5, Atlanta, Georgia USA
SIMILARITY SOLUTION FOR FLOW OVER A WEDGE WITH PHASE CHANGE
Yacob M. Argaw and John P. Kizito1
North Carolina A&T SU, Mechanical Engineering Department Greensboro, NC, USA
1Assistant Professor, North Carolina A&T State University, Mechanical Engineering Department, 1601 East Market Street, Greensboro, North
Carolina, 27411, jpkizito@ncat.edu
ABSTRACT Flow of dry air past a heated wedge, traditionally known
as the Falkner-Skan problem, can be solved using similarity
approach. However, if a thin liquid film layer of thickness ∆δ
covers the surface of the wedge, phase change process becomes
a major mode of heat transfer as the fluid takes a large amount
of heat flux from the heated surface. In this paper, we have
developed a similarity solution for flow over a wedge covered
with liquid film layer using similarity analysis. The liquid
film is treated as secondary phase and is added to the original
energy equation as a source term. Our analysis shows for the
problem to have a similarity solution the wedge angle
parameter, m, and temperature distribution parameter, n,
should be related as 2n=m-1. The temperature profile plot and
the heat flux distribution shows for low Jacob number the heat
transfer rate is enhanced as a result of the phase change.
NOMENCLATURE u,v = velocities in x and y direction (m/s)
U,V = characteristic velocities along x and y
Vi = velocity of liquid to fill the film
ρ = density (kg/m3)
k = thermal conductivity (W/mK)
L,h = characteristic lengths along x and y
T = temperature (K)
S = Source term
Re = Reynolds number
Pr = Prandtl number
Ja = Jacob number
t = time (s)
q = heat flux (W/m2)
T ∞ = ambient temperature (K)
Ts = surface temperature (K)
hfg = latent heat of vaporization (kJ/kg)
Ue = jet free stream velocity, (m/s)
m, β = wedge angle parameters
n = temperature distribution parameter
δ = liquid film layer thickness (m)
η = similarity variable
ψ = stream function
Θ = dimensionless temperature
INTRODUCTION The temperature and velocity flow field for flow over a
wedge, also known as the Falkner-Skan problem, have been a
study topic due to its wide practical applications in
aerodynamics and hydrodynamics fields. Many researchers
like Cebeci et al. [1] Hartree [2] Elbashbeshy et al. [3] Lin et al
[4] and several others have numerically examined the Falkner-
Skan problem using different methods and wide range of
difficulty. Effects of dimensionless parameters like Reynolds
number and Prandtl number; variable viscosity and geometric
wedge angle [3, 5] have been the subject of investigation by
others. Okhotsimskii [6] studied the effect of Jacob number on
the shape and collapse time of a bubble which in turn affects
the heat transfer rate. He found out that when Ja=0 the radius
of the bubble as a function of time assumes a convex profile.
When 0<Ja<2 the curve becomes S-shaped, and when Ja>2
the bubble shape becomes concave. Thus the equation that
describes bubble radius through time changes for these
different ranges of Jacob number. The majority of phase
change studies consider a Jacob number greater than 2, the
present paper also considers Ja>2.
Cooling a wedge surface by a gaseous fluid and
incorporating phase change process to take away a large
amount of heat load maximizes the cooling rate. In particular,
many applications utilize blowing air impingement on a
surface as the easiest way of cooling. We envision a technique
where a constant thickness layer of liquid film on top of the
wedge surface can improve the cooling abilities. The liquid
film will remove heat as it changes its phase to vapor and the
generated vapor will be removed by the air free stream. Phase
change process will not only improve the heat transfer process
but also reduces the surface temperature of the wedge. In the
present paper, we use the Jacob number to measure the effect
of phase change on the flow over a wedge covered by a thin
liquid film. The Jacob number is the ratio of the two modes of
heat transfer used to compare sensible heat to latent heat.
ASME 2011 Early Career Technical Journal - Vol. 10 73
PROBLEM FORMULATION The present study focuses on the effect of phase change on
the overall heat transfer mechanism. Heat from a hot surface
is transferred to the neighboring liquid layer via conduction.
When the temperature of the liquid layer reaches to its
saturation temperature, a portion of liquid evaporates and
removes a large amount of heat from the surface. The
generated vapor is carried away by convection via the moving
fluid.
Free stream of air approaches a wedge surface with
constant velocity U as shown in Figure 1. The wedge angle is
represented as βπ and its half angle is measured as the wedge
is aligned symmetrical to the horizontal axis. Liquid film of
thickness is ∆δ and it covers the surface at all time. Normally,
the liquid film thickness reduces as bubbles are generated and
carried away by the free stream. But in the present problem
formulation, the same amount of liquid that leaves the surface
is added to keep the liquid film thickness constant at ∆δ. The
process is akin to the way high temperature gas turbine blades
are cooled using film cooling technique by liquid aspiration.
Analytical methods to these problems are difficult because
multiple phases are involved and the fluid flow is coupled to
boundary layer and the surface geometry.
We search for a similarity solution given the assumption
that the film layer thickness is kept constant. The fluid
thickness can be constant when the vapor generation is equal
to the aspired fluid for a given heat flux. The other
assumption is that the presence of the liquid layer does not
induce an aerodynamic loss and boundary layer distortion of
the dry air free stream. The dry air free stream instead of
being in contact with the surface is now flows over the liquid
film. Thus the velocity and temperature boundary layers
extend from the surface of the liquid film rather than from the
heated surface. The presence of the liquid film is expected to
enhance the heat transfer process as phase change takes away a
considerable amount of heat. The problem formulation is
summarized in Figure 1 with a schematic which represent the
process.
SIMILARITY SOLUTION APPROACH
Apparently the three general flow equations for the free
stream are transformed to their non-dimensional forms and
then normalized in order to make sure the maximum value in
the velocity and temperature distribution calculation is one of
order unity. For flow over a wedge (Falkner-Skan problem)
the Navier-Stokes equation is modified since the inertial term
is much more dominant than the viscous component.
Therefore continuity, momentum and energy equations of air
flowing over a wedge are given respectively as:
0=∂
∂+
∂
∂
y
v
x
u (1)
2
21
y
u
xd
dP
y
uv
x
uu
∂
∂+−=
∂
∂+
∂
∂ν
ρ (2)
Sy
Tk
y
Tv
x
TuC p +
∂
∂=
∂
∂+
∂
∂2
2
ρ (3)
S is the source term that represents the contribution of the
liquid film layer on the heat removal as it takes a heat
equivalent to the fluid’s latent heat of vaporization. The
source term is constant given that the film thickness is kept at
unvarying level by adding equal amount of liquid that leaves
through phase change.
( )t
ht
hS fg
fg
∂
∂=
∂
∂=
δρ
δρ (4)
Thus the time derivative of the film thickness in Eq. (4) is
the same as rate of pumping additional fluid to the surface, Vi,
which is a value that depends on different parameters. The
velocity, Vi, is to be determined later.
Note that the coordinate system is attached along the
incline plane, as shown in Figure 1. No slip condition
demands a zero velocity at the wall and far from the wall the
velocity become that of the free stream. Thus the conservation
equations, Eq. (1-3), are subjected to the boundary conditions
given in Eq. (5) and Eq. (6).
0,0 === vuy (5)
∞==∞→ TTUuy e;, (6)
Heat flux applied to the wedge is transferred through
conduction across the phase change material and the boundary
layer. Therefore at the wall the heat flux can be written as:
fgiwall hVy
Tkq ρ+
∂
∂= | (7)
For incompressible continuous flow with free stream
velocity distribution Ue, the pressure gradient term can be
related to the free stream velocity. Thus the momentum
equation is modified to the expression in Eq. 8.
2
2
y
u
xd
dUU
y
uv
x
uu e
e∂
∂+=
∂
∂+
∂
∂ν (8)
LetL
xx = ,
U
uu = ,
h
yy = , and
V
vv = (9)
Figure 1. Flow over a wedge with thin liquid film
ASME 2011 Early Career Technical Journal - Vol. 10 74
Normal to the surface the velocity V and the boundary
layer height h are defined in a way that would simplify the
scaling process as follows
2/1Re
Lh = and
2/1Re
UV = (10)
Substituting Eq.9 and Eq. 10 insures that continuity is
conserved. Thus the normalized and non-dimensionalized
momentum equation becomes:
2
2
y
u
dx
dUU
y
uv
x
uu e
e∂
∂+=
∂
∂+
∂
∂ (11)
The free stream velocity is allowed to vary along the
wedge surface, where the exponent, “m”, depends on the
wedge angle related to it as m = β/(2-β). For Falkner-Skan
problem the half angle coefficient can range from zero (flow
over a flat plate) to one (stagnation flow). As a result the value
of m similarly varies between zero and one.
( )m
eCxU = (12)
To find a self-similar solution for the Navier-Stokes
equation the following transformation equations are defined.
( ) fCxx m2
11),( +=ηψ (13)
( )2
11−= mCxyη (14)
The stream line equation is defined in terms of x and a
single variable function f. The derivatives of f are given as:
oU
u
d
dff ==
η' ;
2
2
''ηd
fdf = ;
3
3
'''ηd
fdf = (15)
The constant that multiplies the similarity variables can be
ignored in this analysis since it will disappear at the final
expression of the conservation equations. Apparently, the
transformation of the momentum equation from the x-y plane
to x-η plane is done using chain rule relation. The velocities
and their partial derivatives are converted to the corresponding
similarity expressions as follows:
( ) '2
1
2
1 12
1
yfxm
fxm
v m
m
−
−
−+
+=− (16)
( ) 'fxy
um=
∂
∂=
ψ = 'fUe (17)
( ) ''2
1' 2
33
12
yfxm
fxmyxx
um
m
−+=
∂∂
∂=
∂
∂−
−ψ (18)
''2
13
2
2
fxyy
um
=
∂
∂=
∂
∂−
ψ (19)
( ) '''12
3
3
2
2
fxyy
u m−=∂
∂=
∂
∂ ψ (20)
Then these expressions can be substituted into N-S
momentum equation. After simplification and rearrangement
the non-dimensional normalized momentum equation
becomes:
( ) ''.2
11'''' 2 ff
mfmf
+−−= (20)
Their corresponding boundary conditions are:
( ) 00 =f ( ) 00' =f (21)
( ) 1' max =ηf (22)
The energy term can also be scaled in a similar fashion.
Therefore, normalizing and non-dimensionalizing gives the
following equation.
∗+
∂
∂=
∂
∂+
∂
∂
V
V
U
L
Jay
T
y
Tv
x
Tu i
2
2
21
RePr
1 (23)
The last term, which is the source term due to phase
change, has a Jacob number associated with it. The
normalizing velocity for the rate at which the liquid film is
added on the surface can be defined in a way that would
simplify the energy equation further. Therefore defining
ULV /2=∗ the energy equation becomes:
iVJay
T
y
Tv
x
Tu
1
RePr
12
2
+∂
∂=
∂
∂+
∂
∂ (24)
The non-dimensional temperature is defined as:
s
s
TT
TT
−
−=
∞
θ (25)
Thus temperature distribution can be expanded and
rearranged as
( )( )θ−−−= ∞ 1TTTT ss (26)
( )( )θ−−+=⇒ ∞∞ 1TTTT s (27)
The temperature difference of the heated surface to the
free stream is a function of heat flux and considered to have a
profile given by the following expression, where the term, “n”,
is related to the applied heat flux.
( ) ( )n
sxTT =− ∞ (28)
Therefore partial derivatives of temperature are given as:
( ) '2
1
θ
−=
∂
∂−m
n xxy
T (29)
( )( ) ''1
2
2
θ−−=∂
∂ mn xxy
T (30)
( )( ) ( ) '2
1-1 2
3
1 θθ yxxm
xnx
Tm
nn
−−=
∂
∂−
− (31)
At the wall the applied heat flux has to be equated to the
heat taken away by conduction because of the temperature
ASME 2011 Early Career Technical Journal - Vol. 10 75
gradient along the film plus the latent heat removed by the
phase change process.
ifgVhy
Tkq ρ+
∂
∂−='' (32)
ifg
mn
Vhkxq ρθ +=−
+
''' 2
1
(33)
Therefore solving for iV yields:
fg
mn
ih
xkqV
ρ
θ '.'' 2
1−+
−= (34)
The equation shows the rate at which additional fluid fill
the layer is dependent on the wedge angle parameter “m” and
the temperature distribution parameter “n”. Choosing a
relationship between m and n that would make the dependence
on x to vanish allows a similarity solution to the problem.
Therefore, for ( ) 02/1 =−+ mn and 10 << m a
similarity solution can be found for flow over a wedge covered
by thin liquid film. Once the dependence on x vanishes; since
all the other quantities are constant Vi can be expressed as a
function of yT ∂∂ / only as:
( )'θFVi = (35)
Then these expressions can be substituted into the energy
equation and the latent heat term can be redefined to obtain the
similarity solution. After simplification and rearranging the
general non-dimensionalized form of the energy equation
become
( ){ } '1
''RePr
1''1 θθθθ
Jaffn +−=+− (36)
The corresponding boundary conditions are:
( ) 00 =θ
( ) 10' =θ (37)
( ) 1max =ηθ (38)
RESULTS A Matlab code is written to solve the momentum and
energy ode. The wedge angle parameter m value is fixed to
1/3 (which equivalent to a half wedge angle value of 45o) and
heat flux parameter n =1/3 to satisfy the self-similar variables.
Air is considered as the free stream fluid with Pr =0.72 and
moving at a Reynolds number greater than at least 100. The
solution to the momentum equation gives the well studied
velocity distribution curve as shown in Figure 2. The
convergence of the velocity profile helps to validate the
stability of the code. As the momentum equation shows the
velocity distribution does not depend on the Jacob number.
Thus, the study focuses on solving the energy equation and
determining the thermal boundary layer profile.
The definition of the Jacob number implies that as heat
transfer through phase change in a flow system becomes
dominant the Jacob number gets smaller. For a flow with little
or no Latent heat term the Jacob number is a high quantity.
Figure 3 shows for a higher Jacob number, or heat transfer
predominantly through sensible heat, the dimensionless
temperature distribution converges in a very short boundary
layer thickness. The smaller the non-dimensional position
parameter η, the smaller the thermal boundary layer thickness
becomes. For a Jacob number greater than 30 the temperature
distribution approaches the free stream value at around η=1.0.
However, for flow conditions with smaller Jacob numbers the
boundary layer thickness reaches as high as 2.25, as shown in
Figure 3.
Figure 2. Dimensionless velocity distribution
Figure 3. Dimensionless Temperature distribution
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The temperature and non-dimensional heat flux plots for
heat transfer without phase change process are compared in
Figures 3 and 4 respectively. The rate of change of the
dimensionless temperature distribution along the boundary
layer is presented in Figure 4. The peaks of heat flux curves
reduce as the Jacob number decreases. The results show that
the presence of a phase change material blunts the heat flux
intensity.
CONCLUSION Flow over wedges of certain angles superimposed with a
thin liquid film layer that incorporates a phase change
phenomena does have a self-similar solution. The similarity
analysis gives a solution when the wedge angle and the heat
flux rate definition satisfy the relationship
( ) 02/1 =−+ mn for 10 << m . Since this paper does
not study the effect of the wedge angle on the heat flux, the
parameters m and n are selected just to satisfy the condition
specified. Therefore, m=1/3 and n=1/3 are assumed. The
effect of the Jacob number on the heat flux and temperature
distribution is examined. The heat flux profile in the boundary
layer shows for low Jacob number the change in temperature
across the boundary layer thickness is much smaller compared
to a flow without phase change material. The temperature
boundary layer thickness for low Jacob number is much larger
than boundary layer thickness when no phase change material
is involved. The result shows a strong effect of Jacob number
on the heat flux distribution in the boundary layer.
REFERENCES [1] Cebeci, T. and Keller, H. 1971, “Shooting and Parallel
Shooting Methods for Solving the Falkner–Skan Boundary
Layer Equations,” J. Comput. Phys. 7, pp. 289–30
[2] Hartree, D.H., 1937, “On an Equation Occurring in
Falkner and Skan’s Approximate Treatment of the Equations
of the Boundary Layer,” Proc. Camb. Philol. Soc. 33 (Part II)
223–239.
[3] Elbashbeshy, E.M.A., and Dimian, M.F., 2002, “Effect of
Radiation on the Flow and Heat Transfer Over a Wedge with
Variable Viscosity,” Appl. Math. Comput. 132, pp. 445–454.
[4] Lin, H.T, and Lin, L.K, 1987, “Similarity Solutions for
Laminar Forced Convection Heat Transfer from Wedges to
Fluids of Any Prandtl Number,” Int. J. Heat Mass Transfer 30
1111–1118
[5] Graebel, W. P., 2007, “Advanced Fluid Mechanics,”
Elsevier Inc. USA
[6] Okhotsimskii, A.D., 1988, "The Thermal Regime of Vapor
Bubble Collapse at Different Jacob Numbers," Int. J. Heat
Mass Transfer 31(8): 1569-1576.
Figure 4. Temperature gradient along the Boundary Layer
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ASME Early Career Technical Journal 2011 ASME Early Career Technical Conference, ASME ECTC
November 4 – 5, Atlanta, Georgia USA
SOLAR POWER GENERATION: METHODS AND COMPARISONS
Rambod Rayegan, Yong X. Tao
University of North Texas Denton, Texas, 76203
ABSTRACT Solar radiation has the highest capacity and the lowest
replenishment time among renewable energies. Available technologies to convert solar radiation to electricity can be studied in two different categories: Photovoltaic (PV) systems and solar thermal power systems. PV systems are employed for both building-scale and utility-scale applications. Solar thermal power systems have been mainly developed for the large scale applications. Low and medium temperature solar thermal power generation is at the research level and it is not commercialized yet. This paper presents a review on different solar power system technologies and pros and cons of each technology.
INTRODUCTION Fossil fuel depletion, atmospheric pollution, global
warming and ozone layer destruction are serious problems experts face in finding more sustainable ways to satisfy the requirements of human life. Today, the total energy consumption in the United States still predominantly comes from fossil fuels, although recent interests in investing in wind and solar electricity have been accelerating. Solar radiation has the highest capacity and the lowest replenishment time among renewable energies. Available technologies to convert solar radiation to electricity can be studied in two different categories: Photovoltaic (PV) systems and solar thermal power systems.
PV systems convert solar radiation into DC electricity using solar panels composed of a number of solar cells. Solar cells work based on photovoltaic effect which is the creation of voltage in a material, upon its exposure to light. PV panels which employ crystalline silicon modules are the main stream of the PV panel market. They are being partially replaced in the market by panels that employ thin film solar cells which are expected to account for 31 percent of the global solar panel market in terms of watts by 2013 [1]. PV systems are employed for both building-scale and utility-scale applications. Building-scale solar power systems, also known as distributed power systems, generate electricity locally for the building. They may be connected to the grid or may be stand- alone systems that need batteries or other electricity storage units.
Solar thermal power generation technology generally refers to a power generation system that involves collecting solar radiation through concentrated collectors to an absorber surface, which will heat a heat transfer fluid. Through a piping system and heat exchangers, the hot fluid will be able to generate vapor to power a turbine by means of a conventional or organic Rankine cycle. A generator connected to the turbine will then generate electricity. In general, such a system can be seen as similar to a coal-burning power plant except that the equipment component for vapor generation with coal combustion is replaced by a solar heating system.
Because of the limitation of solar irradiation and the efficiency of solar collectors, the conventional Rankine cycle is economically feasible only for large scale power plants. The Organic Rankine cycle (ORC) is a substitutive technology that is applicable for small scale power generation. ORC employs low grade heat from different sources such as biomass, geothermal, solar and waste heat of industrial processes. The main difference between ORC and the conventional Rankine cycle is in the working fluid. The boiling point of the working fluid in ORC is much lower than steam, hence there is no need to achieve high temperatures to generate vapor for running a micro-turbine or expander. As a result ORC can be driven at lower temperatures than the Rankine cycles that use water. ORC for use in residential and commercial buildings is at the research level [2-3] and it is not yet commercialized.
Currently, two types of high-temperature solar thermal power systems are in commercial-scale operation. One is parabolic trough technology, and the other is the tower configuration. The parabolic dish and Stirling engine arrangement is another technology that has not been developed enough yet to be available for large-scale operation. Flat plate, evacuated tube and compound parabolic concentrator (CPC) collectors are applicable for low and medium temperature ORCs.
This paper presents a review on photovoltaic and solar thermal power systems for both utility-scale and building-scale applications. The main components of each system and pros and cons of each technology will be discussed.
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SOLAR CELLS
The main component of each photovoltaic system is the PV panel composed of a number of solar cells. Solar cells can be studied in two main categories: Crystalline silicon and thin film. Crystalline silicon is the most prevalent bulk material for solar cells and is separated into mono-crystalline silicon (c-Si) and poly-crystalline silicon (poly-Si). c-Si wafer cells are more expensive than poly-Si ones. They are cut from cylindrical ingots. poly-Si cells are made from cast square ingots. They are less expensive to produce than c-si silicon cells but they are less efficient.
A thin-film PV cell is a solar cell made by depositing one or more thin layers of PV material on a substrate [4]. The thickness range of such a layer is wide and varies from a few nanometers to tens of micrometers. The three main types of thin film solar cells are amorphous silicon (a-Si), cadmium telluride (CdTe), and copper indium gallium diselenide (CIGS). Other types include dye-sensitized and organic cells.
a-Si solar cells are fabricated on substrates such as low-cost soda lime glass, stainless steel, and polyimide. The plasma-enhanced chemical vapor deposition is used for the deposition process. A 12% to 13% efficiency range has been demonstrated for small-area laboratory a-Si solar cells now [4]. Some of the major challenges to produce a-Si solar cells are increasing the a-Si solar cell efficiency, reducing the light-induced changes in the devices, developing higher deposition rates for the microcrystalline bottom cell without compromising on the opto-electronic properties of the a-Si tandem devices (which could potentially have negative effect on the solar cell performance), and ultimately reducing the manufacturing cost.
Thin-film CdTe solar cells that have a perfect match with the solar spectrum are one of the most promising thin-film PV devices. Theoretical efficiencies for these devices are about 26%. Laboratory efficiencies of 16.5% for thin-film CdTe solar cell have been demonstrated by NREL scientists [5]. Several deposition processes have been developed for the growth of the absorber layer. Most of deposition processes result in 10% or higher efficiency for thin film CdTe solar cell. Five of these processes have demonstrated prototype power modules: close-space sublimation, electrodeposition, spray, screen printing, and vapor transport deposition. Recent developments are under way to produce CdTe solar modules with increased cell efficiency, standardization of equipment for deposition of the absorber layer, back-contact stability, reduction of absorber layer thickness to less than 1 μm, and controlled film and junction uniformity over a large area.
Thin-film copper indium selenide (CIS) is a direct band-gap semiconductor and has a band-gap of ~0.95 eV. When gallium (Ga) is added to CIS, the band-gap increases to ~1.2 eV, depending on the amount of Ga added to the CIGS film [4]. This material has demonstrated the highest total-area conversion efficiency for any thin-film solar cells. Several major developments for CIS modules are underway to increase
the CIGS solar cell efficiency to scale up the laboratory range of 19.3% to 19.9%, prevent moisture ingress for flexible CIGS modules, decrease absorber layers to less than 1 μm, and investigate CIGS absorber film stoichiometry.
Furthermore, developing approaches for using transparent wide-band gap in CIGS and alloyed CdTe to increase the cell efficiency up to 25% are topics of ongoing research and development [6]. For CIGS’s case, the relative Ga/In and S/Se compositions play the key role in changing the thin film band-gap. CIGS thin films can be prepared by thermal co-evaporation under different uniform and sequential processes to elucidate the film formation and composition control. For CdtTe solar cells, improving cell performance by controlling chemistry and materials processing during film deposition, post-deposition treatments, and contact formation has been the main focus.
UTILITY-SCALE PHOTOVOLTAIC SYSTEMS
There has been a surge of installing large-scale flat-panel PV array systems for utility power productions in the United States and globally during the last five years [4]. The capacity of a single plant has reached as high as 60 MW in Spain. Figure 1 shows the Wyandot Solar Energy Facility, a 12.6 megawatt (DC) solar PV facility located in Upper Sandusky, Ohio. Starting power production in May 2010, the system has 159,200 ground-mounted, thin-film solar panels on a 77-acre plot of land, the largest solar project in Ohio. Projects of similar size have been completed or are under construction in California, Florida, Nevada, Illinois, and other states.
Figure 1. Wyandot Solar Energy Facility in upper Sandusky, Ohio, USA (Courtesy of juwi solar, Inc. [7])
Large-scale ground based PV (LSPV) systems face a
fundamental question of land-use impacts. Denholm and Margolis [8] investigated this issue. When deployed horizontally, the PV land area needed to meet 100% of an average US citizen’s electricity demand is about 100 m2. This requirement roughly doubles to about 200 m2 per person when using 1-axis tracking arrays. By comparison, golf courses and airports each currently occupy about 35 m2 per person in the United States, whereas land used to grow corn for ethanol
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production exceeds 200 m2 per person, although this land is concentrated in a fairly small number of states. They also pointed out another factor of disrupting local ecosystems. Deploying tightly packed PV arrays would create the most disruption but would require the least amount of land area. In contrast, the use of pole-mounted 2-axis arrays would require significantly more land but could be substantially less disruptive. Actual shading impacts of ground based PV arrays are also important. Ecosystem impacts of LSPV deployment need to be investigated to arrive at solutions for the growing shade-tolerant native and beneficial species under LSPV arrays. This also includes evaluation of best practices to minimize the use of herbicides and other chemicals and the use of installation and maintenance techniques to provide minimum impacts to the environment.
One of the alternative solutions to provide LSPV power generation systems is using concentrated photovoltaic (CPV) systems, which have a higher energy conversion efficiency than flat panels - currently 40% [9] - with potential up to 50 %. If deployed on a large scale, they will require less land to produce the same amount of energy. Although no proven commercial scale applications have been installed, the commercialization of this technology is accelerating.
Concentrated photovoltaic systems use either parabolic dish mirror systems or a large array of flat Fresnel lenses to focus energy on PV cells [4]. Dish and CPV systems are modular in nature, with single units producing power in the range of 10 to 35 kW. Thus, dish and CPV systems could be used for either distributed or remote generation applications or in large arrays of several hundred or thousand units to produce power on a utility scale. Dish and CPV systems have the potential advantage of mass production of individual units,
similar to the mass production of automobiles or wind turbines, yet they can be integrated into a utility scale solar power plant.
The key technological challenges for increasing the efficiency of CPV systems are optical elements and cell development. Since 2007, Spectrolab, Inc., has developed metamorphic multi-junction solar cells with an efficiency up to 40.7% and lattice-matched three-junction terrestrial cells with 40.1% efficiency. These efforts were partially supported by the High-Performance Photovoltaics program through DOE NREL [9]. The efficiency also benefits from high-band-gap, disordered GaInP top cells, and wide-band-gap tunnel junctions under the terrestrial solar spectrum at high concentration.
Under a contract by the US Department of Energy, SolFocus, Inc., has developed a 1.6-MW system using the technology illustrated in Figure 2 [10]. It possesses the following characteristics:
· Optical efficiency: 74% · Power unit efficiency: 27.0% · Module efficiency: 25.3% · Acceptance angle: greater than 1 deg. · Cell temperature: 50.8°C to 53.9°C · Module degradation: 1.2% per Kelvin The system uses primary and secondary mirrors (Figure
2A) and optical rods to focus the sun’s rays to highly efficient multi-junction PV cells. The combined mirrors, rod, and cells form a power unit, 20 of which are integrated into one panel. Twenty-eight such panels are then mounted and combined on a dual axis tracking support as shown in Figure 2B. Eventually, such a system can be configured to utility- scale capacity.
(A) (B)
Figure 2. Schematic of Concentrated Photovoltaic (CPV) arrays: (A) power unit, and (B) 20-unit panel and 28-panel tracker on a single pole [10]
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The target of this development is to provide an operational capacity greater than 3 MW with module manufacturing cost less than $5/W. The developed automated assembly line was validated by a third party to have exceeded the cost target and was expanded to other manufacturing partners. The projected manufacturing capacity is 100 MW by the end of 2011 [11].
Neither the dish-Stirling nor CPV systems use storage or hybrid fossil capabilities to provide a firm resource, although CPV systems could, in principle, make use of battery energy storage. However, present battery storage technology is comparatively inefficient and cost prohibitive. A grid-tie back feed capability has to be in place in order to make economic sense in the short run.
BUILDING-SCALE PHOTOVOLTAIC SYSTEMS
Buildings have a significant impact on energy consumption. Buildings account for 40% of the energy used annually worldwide. Energy efficient design and quality construction can drive the cost of powering a home down by more than 50% [4]. But to become zero energy buildings, i.e., where the net annual electricity consumption from utility is zero, buildings and residences must incorporate some type of on-site energy generation. Photovoltaic systems, as the only commercialized solar electricity production technologies for buildings, are reasonably developed to the extent that numerous companies warranty their building integrated solar products for 20 to 25 years. Most of the recent building solar energy system manufacturers combine reliability, functionality, and aesthetics to remain in the solar market competition. Photovoltaic shingles and slates are examples of these modern products.
There is an enormous potential for deploying solar energy solutions on buildings. Today, there is enough residential and commercial rooftop space to site more than 500 GW of PV capacity, equivalent to placing 4-kW PV systems on more than 125 million homes [12]. Current US electric capacity is about 1000 GW.
A PV power system mainly consists of a few essential elements: PV panels with solar cells, inverters, and disconnects. Solar cells have been explained previously. PV panels produce direct current (DC) electricity in the same way as batteries. Inverters, which can be called the most complex parts of a residential PV system, take DC electricity and convert it to AC for powering typical household appliances.
The National Electrical Code [13] requires that each piece of PV equipment have disconnect switches in addition to the main feeder disconnect, allowing service providers to disconnect the equipment from all sources of power. Disconnects may be circuit breakers or switches. Some inverters have incorporated a disconnect switch into a box attached to the inverter. The number of disconnects in grid-tied systems changes with the system’s complexity. In a simple system, there would be disconnects on both sides of the inverter. In more complex systems, there may be disconnect switches for each string of arrays, sub-array disconnects, main PV disconnects, and for each inverter, sub-panel and AC disconnects.
Because solar energy is not available all the time, the continuous usage of electricity by buildings requires power to be drawn from either an energy storage facility charged by the building solar energy system during the production period or from the conventional electricity grid. Energy storage such as battery banks is expensive and inefficient unless they are used in remote areas where the utility grid is not available. The best solution is for PV systems to have an interconnection with the grid, a scheme of net energy metering that allows a PV system to export excess electricity to the utility and import it back when needed. Essentially, grid-tied systems use the grid as a battery. Most electric utilities in the United States have adopted regulations and guidelines to help design PV systems that operate in parallel with the utility systems. The grid-connected PV systems are the most common and simplest PV systems installed in houses which have been recommended by the Department of Energy [14].
Ground-mounted PV systems may be applicable for buildings with large land holdings mostly located in rural areas. Commonly, solar ground-mounted systems involve steel or aluminum frames attached to a concrete foundation. The lower edge of ground-mounted arrays should be high enough to clear vegetation and accumulated dirt or snow.
Either crystalline or thin-film PV technologies can be utilized in these systems. In cold climates of northerly latitudes, sun-tracking systems are used to maximize the solar energy harvest of ground mounted arrays. It is only worth installing trackers in regions with mostly direct sunlight. In diffuse light, tracking has no noticeable effect on solar gain by PV panels.
BUILDING INTEGRATED PHOTOVOLTAICS (BIPVs) Building-integrated photovoltaics are photovoltaic
materials that are used to replace conventional building materials in parts of the building envelope such as the roof, skylights, or facades [16].
BIPVs are separated into roof-mounted and non-roof-mounted BIPV systems. Roof-mounted BIPVs include cement tile systems, thin-film PV laminate for standing seam metal roofs and shingle systems [4]. Ground-mounted PV systems are more suitable with respect to rooftop systems for high-wind regions.
A cement tile system is the most common type of BIPV system. In this system, some cement tiles are replaced by PV panels that are sized and mounted with the same overall dimensions (Figure 3). Usually, PV panels are lighter than the cement tiles they replace; therefore, structural assessments to add any supporting structure should be conducted before installing PVs. Photovoltaic panels follow the contour of the roof in exactly the same way as the cement tiles. Since PV tiles are replacing roofing materials, they should be compliant with local and national roofing regulations. Electrical connections are made between each tile. In some cases, a single module will replace a set of three or four tiles, reducing the number of connections.
Thin-film PV laminate for standing seam metal roofs is common and easy to install with no additional structural
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support required. Solar laminate PV is installed on metal raised-seam roofs between the seams as shown in Figure 4. It is directly attached with glue during installation of the roof without replacing the metal on the roof. The roof ridge, as an easily accessible place for maintenance, is used for electrical wiring and connections. Since they have no glass components, they have higher durability than other types of BIPVs. Thin-film PV laminates are approximately half as efficient as the mono- or multi-crystalline modules, but also currently cost about half as much.
Figure 3. Cement tile BIPV system [15]
Figure 4. Thin-film PV laminate for standing seam metal roofs [15]
Photovoltaics can replace shingles in two ways. In the first method, shingled roofs take advantage of thin-film PV. This lightweight plastic replacement for shingles comes in relatively long strips to replace courses of asphalt shingles and reduce the number of electrical connections. In the second method roof, shingles powered by solar energy (PV shingles) serve to replace ordinary shingles. Electrical lead wires extend from the underside of each shingle and pass through the roof
deck, allowing interior roof space connections. The sun’s heat helps bond the shingles together, forming a weather-resistant seal.
South-facing walls have the potential to generate electricity using BIPVs. They can be covered in PV vertically or can be slanted to act as PV and window shading at the same time. A filtered window or filtered skylight can be made by setting thin-film PV between two sheets of tempered glass. This type of PV system may cost the owner three times more than regular PVs, but it can make a powerful architectural impact.
Mono- or poly-crystalline PV can also be set between sheets of glass to create a dappled effect, blocking the majority of sunlight to make electricity, but allowing shaded light through. This reduces solar gain to the interior of the building while producing electricity.
UTILITY- AND BUILDING-SCALE PHOTOVOLTAIC SYSTEMS COMPARISONS
The cost per watt of installing and operating distributed PV systems is higher than the costs of similar utility systems because of the economics of scale. In the case of a new, large building where the builder might choose to integrate and install PV systems at the time of construction installation, costs may be low, but, in general, there are added costs associated with rooftop systems; especially for retrofits, there can be permitting, architectural, structural, and electrical issues. On the other hand, distributed systems avoid transmission losses through delivering power where it is needed, and residential and commercial systems can be financed along with the rest of a building [4].
Results of NREL-conducted research [15] show that the installation and operation of PV systems for buildings may cost more than twice the installation and operation cost in utility approaches with the same capacity. Figures 5 and 6 illustrate a comparison of rooftop and field PV systems. Concentrating PV has often been presented as a lower-cost approach to utility scale power and could be a major player in this market.
The kilowatt rating in Figure 5 was determined from DC electricity generated at 1000 W/m2 for flat-plate or 850 W/m2 for concentrator systems. The AC rating was assumed to be 85% of the DC STC rating. There are several reasons for lower performance of fixed rooftop systems with respect to CPV systems. In most rooftop systems, PV panels are installed in a horizontal configuration that is not an optimal position for year-round sunlight. The efficiency of a solar cell decreases by increasing its temperature. If the temperature of the panel increases because of poor ventilation from close contact with the roof, performance of the panel is negatively affected. In practical cases, panels may be shaded by a nearby building or tree.
In Figure 6 relative costs, kWh electricity output per installed kWac, and kilowatt-hour electricity output per dollar spent was compared for rooftop and field PV systems. In this analysis, tracked PV systems represent utility-scale solar systems. Results show that the field PV systems generated nearly twice as many kWh per dollar spent, compared with the
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rooftop PV systems. Therefore, given a fixed budget, the field approach results in higher electricity generation in comparison to the retail building-based approach. This also implies that public support for utility market incentives would yield a higher rate of return for the investment.
Figure 5. kWhs generated per installed kWac for single-axis tracked flat-plate, concentrator, and fixed rooftop
systems sited in Arizona [15]
Figure 6. Comparison of flat-plate rooftop and field system costs and average annual production for recent
installations in Arizona [15]
SOLAR COLLECTORS
The main component of each solar thermal power generation system is the solar collector that collects heat by absorbing solar radiation. The key parameter to select a solar collector for a solar system is the required temperature for that specific application. Solar collectors can be identified in three main categories: low temperature with the output temperature less than 85 °C, medium temperature with the output temperature below 130-150 °C, and high temperature solar collectors with the output temperature higher than 150 °C [2]. Low and medium temperature solar collectors are mainly used for heating purposes. They can also be employed in Organic Rankine Cycles (ORCs) to generate electricity for building-scale applications. The main types of solar collectors are described below.
A flat plate collector is a low temp solar collector. A typical flat-plate collector is a metal box with a glass or plastic cover (called glazing) on top and a dark-colored absorber plate (typically copper or aluminum) on the bottom. The sides and bottom of the collector are usually insulated to minimize heat loss [17]. Sunlight passes through the glazing and strikes the absorber plate, which heats up, changing solar energy into
heat energy. The heat is transferred to liquid passing through pipes attached to the absorber plate.
An evacuated tube collector is in the category of low- and medium-temperature solar collectors. The collectors are usually made of parallel rows of transparent glass tubes. Each tube contains a glass outer tube and metal absorber tube attached to a fin. The fin is covered with a coating that absorbs solar energy well, but which inhibits radiative heat loss. Air is removed, or evacuated, from the space between the two glass tubes to form a vacuum, which eliminates conductive and convective heat loss [17].
A compound parabolic concentrator (CPC) collector is in the category of medium- and high-temperature collectors. Because they are not focusing (non-imaging), they are a natural candidate to bridge the gap between the lower temperature solar application field of flat-plate collectors to the higher-temperature application field of focusing concentrators [4]. As shown in Figure 7, an ideal CPC collector can concentrate isotropic radiation incident on an aperture “a” within a solid angle θ, with the normal to this aperture without losses. The minimal possible aperture “b” on to which the radiation can be delivered is shown in Figure 7.
(A) (B)
Figure 7. Aperture of a CPC concentrator: (A) flat absorber parallel to the flat entrance aperture a and (B) acceptance angle as a function of half acceptance angle
θ [18] A full CPC can achieve the maximum concentration Cmax
within the angle θ, where Cmax = 1/sin(θ). In practice, cost reduction often yields a design of CPC with truncated configuration to eliminate the upper part of the mirrors. Studies have been underway since 1970 to achieve a high temperature between 150°C and 200°C. The most recent studies utilized evacuated tubes with CPC reflectors.
A parabolic trough collector is a high temperature solar collector. It is constructed as a long parabolic mirror (usually coated silver or polished aluminum) with a receiver tube running its length at the focal point. Sunlight is reflected by the mirror and concentrated on the tube. Heat transfer fluid (usually oil) runs through the tube to absorb the concentrated sunlight. This can increase the temperature of the fluid close to 400°C.
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UTILITY-SCALE SOLAR THERMAL POWER SYSTEMS
Currently, two types of major solar thermal power systems have been in utility-scale operation. One is parabolic trough (or trough in short) technology, and the other is the tower configuration [4]. Parabolic dish with Stirling engine is another technology that has not been developed enough yet to be available for large-scale operation. PARABOLIC TROUGH SOLAR POWER TECHNOLOGY
Although many solar technologies have been demonstrated, parabolic trough solar thermal electric power plant technology represents one of the major renewable energy success stories of the last two decades. Trough technology is recognized as one of the lowest cost solar-electric power options available today and has significant potential for further cost reduction [4]. Nine parabolic trough plants, totaling over 350 megawatts (MW) of electric generation, have been in daily operation in the California Mojave Desert for up to 18 years [19]. These plants provide enough solar electricity to meet the residential needs of a city with 350,000 people. Several new parabolic trough plants have been built or are currently under construction or in the early stages of operation in support of solar portfolio standards in Nevada and Arizona and a solar tariff premium in Spain.
Parabolic trough power plants use concentrated sunlight, in place of fossil fuels, to provide the thermal energy required to drive a conventional power plant. These plants use a large field of parabolic trough collectors, made of low-cost, non-optical mirrors, which track the sun during the day and concentrate the solar radiation onto a receiver tube located at the focus of the parabolic shaped mirrors. A heat transfer fluid (HTF) passes through the receiver and is heated to temperatures required to generate steam and drive a conventional Rankine cycle steam power plant. The largest collection of parabolic systems in the world is the Solar Energy Generating Systems (SEGSs) I through IX plants in the Mojave Desert in southern California [19]. The SEGS plants were built between 1985 and 1991. All of the SEGS plants are “hybrids,” using fossil fuel to supplement the solar output during periods of low solar radiation. Each plant is allowed to generate 25% of its energy annually using fossil fuel. With the use of the fossil hybrid capability, the SEGS plants have exceeded 100% capacity factor (based on peak hours) for more than a decade, with greater than 85% from solar operation. The capacity factor of a power plant is the ratio of the actual output of the power plant and its full nameplate capacity over a period of time.
SOLAR POWER TOWER TECHNOLOGY
Power tower systems (also known as heliostat power plants) consist of a field of thousands of sun-tracking mirrors which direct insolation to a receiver atop a tall tower. A molten salt heat-transfer fluid is heated in the receiver and is piped to a ground based steam generator. The steam drives a steam turbine-generator to produce electricity [4]. Because
trough and power tower systems collect heat to drive central turbine generators, they are best suited for large-scale plants, 50 MW or larger [19]. Trough and tower plants, with their large central turbine generators and balance of plant equipment, can take advantage of economies of scale for cost reduction, as cost per kilowatt goes down with increased size. Additionally, these plants can make use of thermal storage or hybrid fossil systems to achieve greater operating flexibility and dispatchability. This provides the ability to produce electricity when needed by the utility system, rather than only when sufficient solar insolation is available to produce electricity, for example, during short cloudy periods or after sunset. This capability has significantly more value to the utility and potentially allows the owner of the solar power plant to receive additional credit, or payment, for the electric generating capacity of the plant.
In the summer of 2009, the first and only commercial power tower plant in North America, the Sierra SunTower Plant, commenced operations and interconnected to the grid. It is a 5 MW, commercial facility located in Lancaster, California. There are several more power tower systems around the world that have been built or were under construction during 2009-2010 with general confidence that uncertainty in the cost, performance, and technical risk of this technology is decreasing. A 2004 predictive analysis [20] shows that, assuming the technology improvements are limited to currently demonstrated or tested systems and a deployment of 2.6 GWe of installed capacity by the year 2020, tower costs could drop to approximately 5.5¢/kWh, or better than trough systems (which is predicted to be 6.2 ¢/kWh). However, the data to confirm this prediction has yet to come.
PARABOLIC DISH AND STIRLING ENGINE TECHNOLOGY
Parabolic dish systems use a dish-shaped arrangement of mirror facets to focus energy onto a receiver at the focal point of the collector. A working fluid such as hydrogen is heated in the receiver and drives a turbine or Stirling engine. Most current dish applications use Stirling engine technology because of its high efficiency [4]. The Sandia National Laboratories (SNL) of the US Department of Energy has been pursuing the aggressive deployment of 25-kW dish-Stirling systems for bulk power. The immediate objectives of the development are to: improve reliability and reduce cost of dish/engine
components and systems; test, evaluate, and improve performance of dish/engine
components and systems; and develop tools for industry to characterize their systems
and components. In 2009, the SNL development team, along with many
industrial partners, continued to operate, maintain, and improve the Stirling Energy Systems six-dish model power plant, cataloging more than 100 development areas. They developed a real-time mirror characterization prototype system for 100% inspection of mirrors on assembly line, engine simulator for development of modern engine control hardware and software. SNL and Infinia together further
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conducted optical and systems design of a 3-kW free piston Stirling engine dish system, which is hermetically sealed and requires no maintenance. As of today, no commercially proved units or systems are available for wide deployment yet.
BUILDING-SCALE SOLAR THERMAL POWER SYSTEMS
Because of the limitation of solar irradiation and efficiency of collectors, the conventional Rankine cycle is economically feasible only for large scale power plants. The Organic Rankine cycle (ORC) is a substitutive technology which is applicable for small-scale power generation. The main difference between ORC and the conventional Rankine cycle is in the working fluid. The boiling point of the working fluid in ORC is much lower than steam, hence ORC can be driven at lower temperatures than the Rankine cycles that use water. The selection of working fluid and working conditions of the Organic Rankine Cycle (ORC) has a great effect on the system operation and its energy efficiency and impact on the environment.
In the authors’ previous works [2-3,21], eleven fluids were suggested to be used in solar ORCs designed for low- or medium-temperature solar collectors. The system requirements needed to maintain the electricity demand of a building have been compared for the eleven suggested fluids for two temperature levels of 85°C and 130°C. The building is a one-story commercial building with 395 m2 floor area located in downtown Pensacola, Florida, and is served by grid power.
The simulation results show that the best collector-temperature combination for supplying the building power is the low temperature evacuated tube solar collector. Cyclohexane and Isopentane with respectively 722.54 m2 and 728.16 m2 and Benzene and R245ca with 742.96 m2 required collector area are the best working fluids to be employed in the ORC system in order to maintain the power demand of the building in Pensacola. Isopentane is a more optimal choice for working fluid in comparison to Cyclohexane, Benzene, and R245ca when considering environmental and health issues
The lowest required collector areas to maintain the power demand of the building and the associated collector expenses are still high for a 395 m2 commercial building. The land price to establish the solar field should be added to the solar expenses of the system. Using Compound Parabolic Concentrating (CPC) collectors can be a solution to reduce the required collector area. CPC collector products have not been commercialized for public use as of yet. For this reason the high price of CPC collectors is the main barrier of entry for residential or commercial building applications.
PHOTOVOLTAIC AND SOLAR THERMAL POWER SYSTEMS COMPARISONS
Utility-scale. Among all the large-scale solar power systems, parabolic trough systems and power tower systems have proven to be best suited for large-scale plants of 50 MW or larger. Trough and tower plants, with their large central turbine generators and balance of plant equipment, can take advantage of economies of scale for cost reduction, as cost per
kilowatt goes down with increased size. Additionally, these plants can make use of thermal storage or hybrid fossil systems to achieve greater operating flexibility and dispatchability, accounting for situations in which sufficient solar insolation is unavailable. Large-scale flat panel PV array systems for utility power productions are also widely used because they are easy to scale up or down. When tied to the electric grids, they provide flexible, variable power generation options. However, their costs are higher compared with parabolic trough systems. Concentrating PV systems and parabolic dish systems with Stirling engine technology have relatively high efficiency. Dish and CPV systems are modular in nature, and a single unit could produce power in the range of 10 to 35 kW. They could be used for either distributed or remote generation applications, or in large arrays of several hundred or thousand units for a utility scale application. They also have the potential advantage of mass production of individual units.
The largest group of solar systems in the world is the SEGSs I through IX parabolic trough plants in the Mojave Desert in southern California, built between 1985 and 1991, that have a total capacity of 354 MW. These plants have generally performed well over their 15 to 20 years of operation. There are no operating commercial power tower or dish-Stirling power plants, although some commercial purchase agreements have been in place to pursue those options [1].
Building-scale. Rayegan [2] performed an economic comparison study between the low temperature solar ORC and PV panel system that maintains the electricity demand of a building located in Pensacola of Florida. Table 1 shows the required area and total cost for the suggested solar ORC system (employing low-temperature evacuated tube and Isopentane as working fluid) and PV panel system to maintain the power demand of the building in Pensacola. It can be seen for the suggested ORC system the required collector area to maintain the power demand of the building is more than 60 percent less than required PV panel area to maintain the same amount of power. The total cost to establish the suggested solar ORC system is more than 50 percent less than total cost of running a PV panel system to maintain the power demand of the building in Pensacola.
Table 1. Required area and total cost for the suggested
solar ORC system (employing low-temperature evacuated tube and Isopentane as working fluid) and PV
panel system to maintain the power demand of a building in Pensacola
System Required area [m
2]
Collector/ PV expense
[ x 1000 USD]
ORC package/Inverter
expense [ x 1000 USD]
Total cost
[ x 1000 USD]
Low Temperature Solar ORC
728.16 257.81 75 332.81
PV 1839.00 671.24 33.2 704.44
ASME 2011 Early Career Technical Journal - Vol. 10 85
MAJOR FINDINGS
In this paper, a review of different solar power system technologies and the pros and cons of each technology have been presented. Major findings of the review can be listed as follows: By far, PV-driven applications are dominant in the present
markets in terms of annual productions of cells, panels, and systems and the number of installations in buildings and other applications.
The cost per watt of installing and operating distributed PV systems is higher than the costs of similar utility systems because of the economics of scale. On the other hand, distributed systems avoid transmission losses through delivering power where it is needed.
Among all the large-scale solar power systems, parabolic trough systems and power tower systems have proven to be best suited for large-scale plants of 50 MW or larger.
Large-scale flat panel PV array systems for utility power productions are also widely used because they are easy to scale up or down.
PV panels provide flexible, variable power generation options when tied to the electric grids. However, their costs are much higher compared with parabolic trough systems.
Concentrating PV systems and parabolic dish systems with Stirling engine technology have relatively high efficiency. Dish and CPV systems are modular in nature, and a single unit could produce power in the range of 10 to 35 kW.
Comparison between building-scale photovoltaic and solar thermal power system shows that there is a good potential for commercializing ORC systems for distributed power generation.
REFERENCES [1]http://www.renewableenergyworld.com/rea/news/article/2009/11/thin-films-share-of-solar-panel-market-to-double-by-2013. Retrieved September 1, 2011. [2] Rayegan, R., “Exergoeconomic Analysis of Solar Organic Rankine Cycle for Geothermal Air Conditioned Net Zero Energy Buildings,” Ph.D. Dissertation, Florida International University, 2011. [3] Rayegan, R., Tao, Y.X., “Analysis of Solar Organic Rankine Cycle for a Building in Hot and Humid Climate,” ASME International Mechanical Engineering Congress & Exposition, Denver, Colorado, November 2011. [4] Tao, Y. X., Rayegan, R., “Solar Energy Applications and Comparisons,” in Energy and Power Generation Handbook, Editor: K. R. Rao, Publisher: ASME Press, 2011 [5] Wu, X., 2004, “ High-efficiency polycrystalline CdTe thin-film solar cells,” Solar Energy, Vol.77(6), pp 803-814.
[6] Shafarman, W., McCandless, B., 2008, “Development of a Wide Bandgap Cell for Thin Film Tandem Solar Cells: Final Technical Report,” 6 November 2003 — 5 January 2007. 40 pp., NREL Report No. SR-520-42388. [7] Juwi solar Inc., http://www.juwisolar.com/wyandot-solar. Retrieved September 1, 2011. [8] Denholm, P., Margolis, R. M., 2008, “Impacts of Array Configuration on Land-Use Requirements for Large-Scale Photovoltaic Deployment in the United States,” SOLAR 2008 American Solar Energy Society (ASES), 3-8 May 2008, San Diego, California, pp. 1-7. NREL Report CP-670-42971. [9] King, R. R., 2010, “Ultra-High-Efficiency Multijunction Cell and Receiver Module, Phase 1B: High Performance PV Exploring and Accelerating Ultimate Pathways, Spectrolab, Inc., Subcontract Final Report,” NREL Report SR-520-47602. [10] Horne, S., McDonald, M., Hartsoch, N., Desy, K., 2009, “Reflective Optics CPV Panels Enabling Large Scale, Reliable Generation of Solar Energy Cost Competitive with Fossil Fuels, NREL Report” SR-520- 47310. [11] http://www.solfocus.com/en/news-events/press-releases/2009-09-09.php. Retrieved October 28, 2011. [12] Solar America Initiative–In Focus: The Building Industry, 2007,pp., NREL Report DOE/GO-102007-2389. [13] Wiles, J., 2005, “Photovoltaic Power Systems and the 2005 National Electrical Code: Suggested Practices.” [14] Baechler, M., Gilbride, T., Ruiz, K., Steward, H., Love, P., 2007, “High-Performance Home Technologies: Solar Thermal & Photovoltaic Systems,” Volume 6 Building America Best Practices Series. 159 pp., NREL Report No. TP-550-41085. [15] Kurtz, S., Lewandowski, A., Hayden, H., 2004, “Recent Progress and future potential for concentrating photovoltaic power systems: preprint.9,” pp. NREL Report No. CP-520-36330. [16] http://www.wbdg.org/resources/bipv.php . Retrieved September 1, 2011. [17]http://www.eere.energy.gov/basics/buildings/water_heaters_solar.html Retrieved September 1, 2011. [18] IEA., 2008, “Process Heat Collectors, State of the Art Within Task 33/IV,” edited by Weiss, W., and Rommel, M., Report by Solar Heating and Cooling Executive Committee of the International Energy Agency (IEA), published by AEE INTEC, Gleisdorf, Feldgasse 19, Austria, 2008. pp. 1-55. [19] DOE NREL (2006). Parabolic Trough Solar Thermal Electric Power Plants. Publication FS-550-40211; DOE/GO-102006-2339. [20] Sargent and Lundy LLC. (2003). Assessment of Parabolic Trough and Power Tower Solar Technology Cost and Performance Forecasts, NREL Report SR-550-34440. [21] Rayegan, R., and Tao, Y. X., 2011, “A Procedure to Select Working Fluids for Solar Organic Rankine Cycles (ORCs),” Renewable Energy, Vol. 36 (2), pp. 659-670.
ASME 2011 Early Career Technical Journal - Vol. 10 86
ASME Early Career Technical Journal 2011 ASME Early Career Technical Conference, ASME ECTC
November 4 – 5, Atlanta, Georgia USA
FUZZY-LOGIC HEALTH MONITORING ROBOTS FOR THE ELDERLY
Wuqayan Alwuqayan, Sabri Tosunoglu Department of Mechanical and Material Engineering
Florida International University Miami, Florida, U.S.A
ABSTRACT
This research paper ventures through a cutting-edge
technology behind personal service robots that are aimed
towards the elderly population. The technology uses sensors
that measure several characteristics about the habits of elderly
people and provide vital feedback on their health. The sensors
are specially designed to fulfill their objectives which may
range from face recognition and/or detecting people’s motion
all the way to measuring their pulse and body temperature. The
technology will enable the service robots to create personal
health profiles of their owners. Through pattern recognition the
robots then can detect any abnormality or irregularity in their
owners’ health or habits and act accordingly to prevent
potential health mishaps.
Key element about this new breed of robots is their
controller system which allows them to mimic human
reasoning in handling complex nonlinear systems. There are
number of methods to conduct such kind of controllers.
However when dealing with imprecise input signal in real-time
operations, fuzzy-logic controller proven to be the best suit for
the job. A detailed research work in fuzzy-logic controller is
compiled by a number of researchers and there have been many
books and articles on the subject as reported in [1-4]. For
simulation purposes a fuzzy logic controller was constructed
using Fuzzy Logic Toolbox provided by MATLAB software, a
performance test of the system was carried out, and the results
were analyzed. Simulation and analysis provide designers with
information on how the fuzzy-logic controller can affect the
output speed and stability of a system with multivariable inputs.
Thus, designers can proceed with a larger and more complex
system. The objective behind this technology is to improve life
quality of the elderly.
INTRODUCTION
The population of people age 65 and older is increasing
rapidly. According to the 2010 U.S. census 13% of the
population is over 65, The U.S. Census Bureau projects that
number will increase to 19.7% in 2030. Current living
conditions for the majority of elderly people are already
alarmingly unsatisfactory. According to US Department of
Health and Human Services, nearly 9% of non-institutionalized
persons 70 years of age and over were unable to perform one or
more activities of daily living such as bathing, dressing, using
the toilet, and getting in and out of bed [5]. At the same time,
the health-care sector is undergoing a dramatic cost increase,
according to US Department of Health and Human Services
current nursing home costs range between $30,000 and $60,000
a year which has been doubled over the last decade. Therefore,
an alternative ways of providing quality care for elderly people
have to be considered. Such ways must not only lower the cost,
but they must also increase the comfort of living while
approaching the elderly with a level of privacy and dignity that
they deserve.
One approach would be a tailor made service robot that
will open the gate for new generation of artificial intelligence
robots. These new service robots are powered with Fuzzy-
Logic Controller (FLC) system that gives them the ability to
imitate humans in decision making when dealing with
multivariable inputs. Integrated with specially designed high
tech sensors, these robots are capable of creating health profiles
of their owners through pattern recognition thus sensing any
potential risk ahead of time. Furthermore they can detect and
report sudden break downs. Their fuzzy-logic controller is
proven to be far more superior to the commonly used
Proportional-Integral-Derivative (PID) controller [6]. FLC has
more robust against disturbances and faster transient response
with less overshoot and oscillation than the PID controller.
Robotic technology is going through major revolution and now
would be the time to take advantage of such revolution and find
an optimum solution for a potentially growing problem.
FUZZY THEORY
Fuzzy-logic control system can replace many of today’s
complicated control systems. It simplifies the control design
which can save time and money. The main feature of fuzzy
logic is that it is able to deal with imprecise linguistic
information. Compared with traditional logic, fuzzy-logic is
more suitable for analyzing complex nonlinear systems, such as
the ones that involve humans. Fuzzy algorithms can be
implemented with software on standard hardware such as
ASME 2011 Early Career Technical Journal - Vol. 10 87
microprocessors. However, for complex and real-time control
systems hard-wired fuzzy algorithms are required (Fuzzy
hardware); one type of such hardware is the fuzzy-logic
controller. The fuzzy-logic controller is a rule based system (see
Fig 01) where fuzzy control rules and membership functions
are stored in the knowledge base. Control operations then can
be divided into three parts. First part is fuzzification, which
converts crisp input data into linguistic values (fuzzy values).
The second part is fuzzy inference which executes the fuzzy
approximate reasoning. Third and final part is defuzzification
which yields a none-fuzzy crisp data action as the output. In
depth analysis on FLC construction is reported in [7,8].
Figure 01 Fuzzy Logic-Based Control
PROPOSED METHOD
Utilize fuzzy-logic controller to design a state-of-the-
art personal service robot. The model involves specially
developed sensors that enable face recognition, motion
detection, and monitoring vital characteristics such as pulse and
respiration [9]. These high tech motion sensors can be installed
throughout the owner’s home; i.e. doorways, mattress, showers,
chairs, etc. The sensors send their signals to the robot’s control
panel. The collected data are then used to create a personal
heath profile via fuzzy-logic controller embedded within the
service robot which enables it to detect potential medical alerts.
Furthermore, by incorporating the gathered data the robot will
be able to distinguish between similar sounds that belong to
different sources; i.e. owner’s cry for help vs. a loud sound
from a TV set, which will reduce the number of false alarms. In
order to construct a fuzzy logic controller inputs and outputs
variables must be defined. The membership functions can then
be created via set of rules and equations. Figure 02
demonstrates a general design scheme of a fuzzy logic
controller.
CONTROL SYSTEM CONSTRUCTION
In order to simulate the robot’s performance, a fuzzy-
logic controller had to be constructed. For simplicity, a system
of three inputs and two outputs was chosen (Figure 03). The
input variables were defined as Pulse Rate, Body Temperature
and Room Temperature, each of which was assigned five levels;
Alarmingly Low, Low, Normal, High, and Alarmingly High.
The corresponding values for each level were picked according
to nursing practice [10]. Output variables were defined as
Robot’s Mode and Temperature Control. The Robot’s Mode has
four levels; Normal, Standby, Alert, and Emergency. For each
input variable a set of membership functions were constructed
(Figure 04). Table 01 and 02 contain the values of input and
output variables respectively.
Table 01 Input Membership Functions variables
Table 02 Output Membership Functions variables Robot's Mode Factor
Normal 0-0.3
Standby 0.31-0.5
Alert 0.51-0.75
Emergency 0.76-1.00
Figure 02 fuzzy-logic controller general design scheme [7]
ASME 2011 Early Career Technical Journal - Vol. 10 88
Figure 03 Control System Outline
The inputs and outputs membership functions were
defined, a set of membership functions rules were created using
the IF-THEN rule base. As mentioned, one of the important
features of fuzzy-logic is the ability to efficiently handle
multiple-state logic as demonstrated below:
1. IF pulse is Alarmingly Low OR body temp is Alarmingly
Low THEN it is an Emergency.
2. IF pulse is Alarmingly High OR body temp is Alarmingly
High THEN it is an Emergency.
3. IF pulse is Normal AND body temp is Normal THEN it is
Normal.
4. IF pulse is Normal AND body temp is High THEN it is an
Alert.
5. IF pulse is Normal AND body temp is Low THEN it is an
Alert.
6. IF room temp is Low AND body temp is Low THEN it is a
Standby.
7. IF room temp is High AND body temp is Low THEN it is
an Alert.
8. IF room temp is Low AND pulse is Alarmingly Low OR
body temp is Alarmingly Low THEN it is an Emergency.
Figure 04 Input Variables Membership Functions
Membership functions rules are very essential for any
fuzzy logic controller. They provide the FLC with more options
to choose from for each possible scenario. However, having too
much set of membership functions rules can affect the
performance of the controller which may yield some
inconsistence output results. It can also slow the system
response speed which is a very vital aspect of the controller.
Therefore an optimum set of membership functions rules must
be sought after.
When the fuzzy logic controller is constructed, the
controller guides the robot to perform tasks and make decisions
based on the specified membership rules to incorporate a preset
commands. The controller enables the robot to mimic human-
like reasoning when handling none-linear multivariable inputs.
The output responses would be predefined as well. For
instance, an “Emergency” output means a 911 call, while an
“Alert” output means a call to a doctor or relative. On the
other hand a “Cool” output would require the robot to cool
down the air condition unit. By interface with specially
developed sensors, the controller can read the transmitted
signals from the sensors and gather all the data to create a
health profile of the robot’s owner. The controller then can
compare trends and/or detect potential health risks. The robot’s
response will be according to the FLC membership functions
rules (see Figure 05).
Figure 05 Membership rules
ASME 2011 Early Career Technical Journal - Vol. 10 89
RESULTS SIMULATION The assigned inputs and outputs variables were run
through a fuzzy logic controller to simulate the outcome based
on the specified rules. The system showed that it can handle a
wide range of possibility combinations and the ability to
process nonlinear variables. Some set-value modifications were
needed to reach optimum results. A performance test of the
system was conducted and the results were plotted (Figures 06
and 07). The performance plot shows a generally coherent and
smooth surface with no significant spikes or breakouts which is
an indication of the system success.
CONCLUSION With the rapidly growing number of elderly population
and the dramatically cost increase of nursing homes and home-
care services a problem arises. In an effort to control such a
problem a cutting-edge service robot is proposed that can
mimic human control via set of specially developed sensors and
a fuzzy logic controller embedded within the robot. For
simulation purposes a fuzzy logic controller was constructed
and its performance was tested. The results show that the
system can handle multiple inputs and outputs variables. It also
shows that the system can be easily modified to reach optimum
results.
REFERENCES [1] H. J. Zimmermann, 1991, Fuzzy Set Theory and Its
Applications. Boston, MA: Kluwer
[2] Ahmad, M. Ibrahim, 2003, Fuzzy Logic for Embedded
Systems Applications, Elsevier Science, Burlington, MA
[3] Kevin, M. Passino, Stephen, Yurkovich, 1998, Fuzzy
Control, Addison Wesley Longman Inc, Menlo Park, California
[4] H. Bandemer and S. Gottwald, 1995, Fuzzy Sets, Fuzzy
Logic, Fuzzy Methods With Applications. New York: Wiley
[5] Nicholas, Roy, Gregory, Baltus, Dieter, Fox, Francine,
Gemperle, Jinnifer, Goetz, Tad, Hirsch, Dimitris, Margaritis,
Mike, Montemerlo, Joelle, Pineau, Jamie, Schulte, and
Sebastian, Thrun, 1999,”Towards Personal Service Robots for
the Elderly,” Computer science and Robotics Carnegie Mellon
University
[6] Herbert, Eichfield, Thomas, Kiinemund, and Manfred,
Menke, 1996,“A 12b general fuzzy logic controller chip,” IEEE
Transaction on Fuzzy systems, vol 4, no 4
[7] Faizal, A. Samman, and Eniman, Y. Syarnsuddin, 2002
“Programmable Fuzzy Logic Controller Circuit on CPLD
Chip,” IEEE 0-7803-7690-0/02/$17.00 02002
[8] Sheroz Khan, Salami Femi Abdulazeez, Lawal Wahab
Adetunji, AHM Zahirul Alam,Momoh Jimoh E. Salami, Shihab
Ahmed Hameed, Aisha Hasan Abdalla and Mohd Rafiqul
Islam, 2008, “Design and Implementation of an Optimal Fuzzy
Logic Controller Using Genetic Algorithm,” Journal of
Computer Science 4 (10): 799-806
[9] Glascock, Anthony,; Kutzik, David,; “The Impact of
Behavioral Monitoring Technology on the Provision of Health
Care in the Home” Journal of Universal Computer Science
2006, 12:59-72.
[10] McDermott, M., 1996, Central venous pressure. Nursing
Standard 9(35): Cardiol-30
Figure 06 Output Variables Membership Functions
Figure 07 Performance Plot of FLC
ASME 2011 Early Career Technical Journal - Vol. 10 90
ASME Early Career Technical Journal 2011 ASME Early Career Technical Conference, ASME ECTC
November 4 – 5, Atlanta, Georgia USA
DESIGN OF CONTROLLERS FOR A LASER BEAM STABILIZER USING PID AND OBSERVER-BASED STATE FEEDBACK CONTROL
Kwabena A. Konadu, Sun Yi North Carolina Agricultural and Technical State
University Greensboro, North Carolina, USA
ABSTRACT This paper presents the design and implementation of a
Proportional Integral Derivative (PID) controller and an
observer-based state feedback controller to regulate a laser
beam position. In the presence of noise and active disturbance,
the two different control strategies are compared regarding
performance and design procedures. The PID controller
estimates the Root-Mean-Square (RMS) of the signal on the
Position Sensing Device (PSD) and dynamically computes
control commands. However, the PID controller can become
unstable if filters are not selected carefully. Thus, a Low-Pass-
Filter (LPF) and sampling rate should be analyzed and selected
carefully to reject disturbances avoiding destabilization.
Alternatively, the state feedback method stabilizes the laser
beam by continuously observing the state of the laser beam
system (LBS). Since all the state variables in the model of the
LBS are not directly measurable, an observer is designed to
estimate the state of the plant. The estimated state is used to
complete state feedback control. Simulations and experiments
conducted on the LBS are presented to demonstrate the
effectiveness of the two controllers on stability and
performance.
INTRODUCTION Laser beam stabilization experiment is used to correct or
minimize dynamic laser beam pointing errors in application
systems. Representative examples of applications that utilize
laser beams are [1]:
Medical: a variety of surgeries are performed by using
LBSs,
Military: most firearms applications use LBSs as a tool to
enhance the targeting of weapon systems and a target
designator in aircraft,
Industrial and commercial: cutting, preening, welding,
marking of metals and other materials are done by using
LBSs,
Electronics and data communications: LBSs are used for
optical communications over optical fiber and free space as
well as storage of data in optical discs. Also, applications
include nuclear fusion, microscopy, laser cooling, material
processing, photochemistry, etc.
In these applications, errors are introduced by auxiliary
devices such as fans, external light sources from fluorescents,
computers, pumps and any device that introduces high-
frequency signals to measurement. The errors have the effect of
deviating the laser beam from its accurate intended target [2].
Controllers are designed to reduce these errors or deviation that
is introduced on the laser beam. This is achieved by using an
active Fast Steering Mirror (FSM) (known as the Voice Coil
Actuator) to eliminate beam motion by sampling a small
percentage of the beam and using feedback from Position
Sensing Detectors (PSD) to compensate or control these errors
[3]. A controller that stabilizes a laser beam on the PSD in the
presence of noise and active disturbance needs to be designed
and built. The designed controller uses feedback from a
position sensing device to calculate the amount of voltage that
rotates the voice-coil actuator so that the incident laser beam on
its mirror is reflected or directed accurately to the middle of the
position sensor even in the presence of noise and active
disturbance. The stability of the closed-loop system when using
the PID controller and filters will be analyzed carefully because
the PID controller could become unstable if filters are not
selected appropriately [4].
Recent literature on control of laser beams showed that
work has been done on the control of laser beams using the PID
approach [2, 5]. However, not much work has been done on the
control of laser beams using observer-based state feedback [6].
In most laser beam control systems, bi-axial sensors, such as,
optical position sensor (OPS) charge-coupled devices (CCDs)
or quad-photo detectors, are used to determine the coordinated
location of the beam projection on a plane [7], which is fed
back into a two-input/two-output controller [6]. This paper
considers the LBS as a linear time-invariant (LTI) system with
single-input single-output (SISO). The PID design method was
presented in [3], the design method is followed for a PID
controller of different design specifications. An alternative
control scheme is the use of observer-based state feedback. The
ASME 2011 Early Career Technical Journal - Vol. 10 91
response of the system when using the PID is compared with
the state feedback controller.
The rest of the paper is organized as follows. The next
section involves designing the PID controller, followed by
simulations to test the stability of the response. A controller
using an observer-based state feedback is introduced in the
subsequent section. Simulations of controlled systems and
comparison of the two design strategies are then presented,
followed by Conclusion.
DESIGN OF A PID CONTROLLER The purpose is to design a proportional-integral-derivative
controller (Figure 1.) that uses all these three terms to
compensate for any error. This controller will determine the
right amount of voltage that will steer the actuator in a way that
the beam is always reflected directly to the center of the PSD
even in active disturbance.
Figure 1. Block Diagram of a Closed-Loop of a Laser Beam Stabilization System with a PID Controller
Design of RMS Estimator
The RMS estimator (Figure 2.) is used to determine the
actual position of the laser on the PSD. It records the deviation
of the beam from the middle of the PSD (the reference center)
and displays the value digitally. The aim is to estimate signal
values that are close to the ideal or theoretical [3].
The performance of the estimator when tested showed that
the effect of noise and disturbance alters the accuracy of its
values. A second-order low-pass filter is then introduced in the
estimator to reduce these errors.
Figure 2. RMS Estimator Model with Low-Pass Filter
Derivation of the Ideal PID Gains for the Closed-Loop Laser Beam Stabilizer System
The block diagram that describes the operation of the LBS
is shown in Figure 1 in closed-loop. The intended target of the
beam is the middle of the PSD, thus the reference point is zero.
The ideal PID gains without filtering and noise is first derived
by comparing the second-order characteristic equation to the
denominator of the transfer function equation of the system in
Laplace domain. The ideal gains are then used to select desired
filter parameters before the actual practical PID gains are
obtained.
Assumptions made in designing PID 1) There is no actuator saturation and amplifier offset,
2) Vc,amp = Vc,
where Vc(s) is the Laplace Transform of the voice-coil
digital-to-Analog voltage and Vc,max is the maximum
voltage that can be supplied to the voice-coil by the power.
The transfer function (T.F.) of the closed loop disturbance-to-
position of the system, Gx,d, is given as;
(1)
where X(s) is the Laplace Transform of the position measured
by the PSD, D(s) is the Laplace Transform of the disturbance.
The LBS utilizes the unity negative feedback in the closed-loop
system as;
(2)
the plant transfer function is;
(3)
the PID controller, C, is given as;
(4)
where is the proportional control gain, is the derivative
control gain and is the integral control gain. Equations (2),
(3) and (4) are substituted into equation (1) to obtain the closed
loop transfer function, , as;
(5)
is the natural frequency, is the damping ratio and K is the
open-loop steady-state gain.
Finding the Ideal PID Gains
For the Ideal PID gains, the denominator of equation (5),
(closed-loop transfer function) is compared with the third-order
characteristic Equation below;
ASME 2011 Early Career Technical Journal - Vol. 10 92
(6)
where is the zero location. Comparing the coefficients in
Equations (6) to the denominator of equation (5) yields the
control gains;
(7)
(8)
(9)
System specification of PID controller 1) The damping ratio of the ideal PID controller is set to 1
.
2) The closed-loop gain should not exceed 0.05;
, the disturbance frequency,
3) The zero location specification is at 0.5
Substituting the closed-loop system specification parameters
into the gain Equations, the ideal closed-loop proportional gain,
, is , the derivative gain, , is 0.0021
, and the integral gain, , is /s
Obtaining natural frequency of system The natural frequency of the LBS is obtained by
substituting the gains and parameters of the PID specification
into the closed-loop transfer function equation to obtain the
frequency response of the system. The natural frequency is
found from the magnitude of the frequency response as;
(10)
Substituting the specified damped frequency, the system gain,
and time constant into equation (10) yields the natural
frequency, 563 rad/s. Hence, the natural
frequency, of the system is obtained as 563 rad/s.
Practical PID Controller Because the LBS is prone to capturing noise, these noise
turns to magnify after taking the derivative of the signal. It is
important to include a filter in the design to remove this noise
from the system. In this design of practical PID controller, a
filter (Low-pass filter) is used to obtain the displacement of the
PSD signal.
Specifications of filter The transfer function of the second-order band-pass filter is
of the form;
(11)
is the cutoff frequency and is the damping ratio of the
filter. The bandwidth is obtained from equation (12)
(12)
The cross-over frequency is the frequency at which the
magnitude of the Transfer Function is 1 or 0 dB. The cross-over
frequency, , is obtained from the magnitude of frequency
response as;
(13)
Table 1. Filter Specifications
Filter parameter Specification
Filter bandwith
Phase margin Cut-off frequency
Selecting filter specifications for LBS
Figure 3. shows a set of filter parameters for different
damping ratios. The parameters are compared with the
specifications to find the set that best meet the given
requirement. The white markers show the parameters for
corresponding damping ratios that are rejected and the black
markers are filter parameters for corresponding damping ratios
that meet the requirements.
Figure 3. Plot of Filter Specifications against Damping
Ratio
From Figure 3., the damping ratio that results in a bandwidth
greater than 6752rad/s and a PM greater than 75degrees is
determined as 0.5 and 0.6 but a damping ratio of 0.5 is selected
0
20
40
60
80
100
120
140
160
180
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0.3 0.4 0.5 0.6 0.7
Ph
ase
Mar
gin
(⁰)
Fre
qu
en
cy (r
ad/s
)
Damping ratio (ζ)
bandwidth
Cross-over frequency
Phase Margin (PM)
ASME 2011 Early Career Technical Journal - Vol. 10 93
as the choice for designing the filter because its phase margin is
closer to the desired specification.
Controller of Laser Beam Stabilization System The filter with its desired parameters has been selected,
now, the PID controller is built in Simulink and its performance
is tested to determine if the specified gain requirement for the
LBS is met. Figure 4. shows a block diagram of the designed
laser beam controller. The PID gains after applying the low-
pass filter is: = 0.722 V/mm; = 0.002 V.s/mm; = 0.360
V/mm/s.
Figure 4. Block Diagram of the Laser Beam Controller
Experimental Set-up The LBS experiment set up (Figure 5.) consists of four
main components: PC, LBS component, Quanser Personality.
Intelligent Data acquisition board (QPID) and a Peripheral
Component Interconnect (PCI) express board. These are inter-
connected and act as a hardware-in-the-Loop (HIL). The PCI
board is inserted into the CPU and connected to the QPID
terminal board through analog cables. The terminal board is
then connected to the LBS component through analog and
encoder cables before the system is powered. Experiments are
run on this system by generating real-time codes from Simulink
models that runs on a real-time kernel of the processor of the
PC. After designing the appropriate controller, the design is
built and tested in Simulink on the computer.
Fast steering mirror
DC motor for active disturbance
Laser Source PSD
QPIDAmplifier
Laser beam
Figure 5. Schematic Diagram of a Laser Beam
Stabilization Experimental Set-Up
The LBS is subjected to active disturbance by increasing
the disturbance voltage in Simulink which causes the motor to
slide back and forth, thereby displacing the beam from the
middle of the PSD. The controller intended to stabilize the
system is then turned on and the response is tested.
System gain of LBS after switching to closed-loop
After building the design, the controller is tested to
determine if the design specification has been met. Figure 6.
shows that the system maintains a desired gain below 0.05 for
all disturbance frequencies when the system is switched from
open-loop to closed-loop. Therefore the gain requirement is met
for this design.
Figure 6. Plot of Closed-Loop System Gain against Disturbance Frequency When Using the PID Controller
System response after switching from open-loop to closed-loop
Figure 7. is a plot of the LBS in open-loop that is switched
to closed-loop after 12.5 seconds. Observation shows that there
is an amount of error that is introduced immediately when the
controller is initially switched from open-loop to PID. This
error however is seen to decrease over time and gradually
approaches steady state at zero.
Figure 7. Plot of Change in Response from Switching
From Open-Loop to Closed-Loop
Stability analysis of controller The Phase Margin (PM) is the amount of phase that
exceeds -180 degree at the cross-over frequency. The more it
exceeds -180 degrees, the more stable the system is. The bode
diagram shows the PM for the transfer function of the loop. The
introduction of filter and sampling has an effect of delay and
shift in the stability of the system. It is, therefore, necessary to
0
0.01
0.02
0.03
0.04
0.05
0.06
8 9 10 11 12 G
ain
=X(m
m) /
D(W
d)
Disturbance Frequency (Hz)
System Gain
Specified System Gain
11.5 12 12.5 13 13.5 14-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
time (s)
Positio
n M
easu
re
d o
n th
e P
SD
x (
mm
)
Open loop
Closed- loop
ASME 2011 Early Career Technical Journal - Vol. 10 94
select a sampling interval such that the stability of the system is
not affected.
Figure 8. Margin Plot for Practical PID Controller
Figure 8. shows a Bode diagram for crossover frequencies
of the loop transfer function of the controller with a filter. For a
signal, it is shown that the phase margin of the practical loop
transfer function has 73.4 degrees. Although the phase margin
is not close to 90 degrees (ideal), the system is still considered
to be stable because it is greater than the desired PM of 60
degrees. The reduction in PM can be accounted for as the effect
of the filter.
Figure 9. Margin Plot for Practical PID Controller after Sampling
Figure 9. shows that after sampling, the PM has reduced to
67.5 degrees meaning the stability of the system has reduced.
Even though there is a reduction in PM, the system is
considered to be stable because the resulting PM is still greater
than 60degrees.
DESIGN OF STATE FEEDBACK CONTROLLER Alternatively, Figure 10. shows a block diagram of the
LBS that utilizes state feedback observer for control. The real
plant of the LBS is modeled in its state space form and
considered as a linear time-invariant (LTI) with single-input
single output (SISO) system.
Figure 10. Block Diagram of the LBS Using a State
Feedback Observer for Control
Assumptions made in designing state feedback controller 1) Not all the state variables, x, of the LBS is available for
measurement.
2) Sensors are not available and they are expensive to obtain
all the physical conditions (initial), , for the LBS
3) There is an amount of error due to estimation.
Because information about the dynamics of the LBS is limited,
the design of an observer that computes an estimate of the
entire state vector with limited information from the output of
the LBS for control is proposed.
Modeling of LBS (Plant) and Observer The dynamics of the plant is obtained by modeling the
LBS in the form;
(14)
(15)
where A and B are system and input matrices respectively,
and are actual state vectors, is the output matrix. [8, 9].The
observer is constructed from the model of the LBS dynamics
as;
(16)
(17)
where is the estimate of the actual state . Since the exact
initial condition, of the actual LBS is not available, the
observer will be used to provide that information. However,
because the observer gives estimated but not exact information
about the LBS, a continuous increase in error may occur if a
poor estimate for is made, this will cause the observer to
give erroneous information about the true state of the LBS.
Error introduced, e, is;
(18)
This divergence in estimated state from the actual LBS state,
can be eliminated and the error made to disappear very fast by
controlling the error with feedback. The difference between the
actual measured LBS outputs and the estimated outputs are
taken and fed back into the observer to continuously correct for
this error as shown in Figure 11.
-200
-150
-100
-50
0
50
Mag
nitu
de (d
B)
101
102
103
104
105
106
107
-270
-225
-180
-135
-90
Phas
e (d
eg)
Bode Diagram
Gm = 15.5 dB (at 5.74e+003 rad/sec) , Pm = 73.4 deg (at 1.02e+003 rad/sec)
Frequency (rad/sec)
-200
-150
-100
-50
0
50
Mag
nitu
de (d
B)
101
102
103
104
105
106
107
-6.912
-4.608
-2.304
0x 10
4
Phas
e (d
eg)
Bode Diagram
Gm = 12.1 dB (at 4.48e+003 rad/sec) , Pm = 67.5 deg (at 1.02e+003 rad/sec)
Frequency (rad/sec)
ASME 2011 Early Career Technical Journal - Vol. 10 95
Figure 11. Block Diagram of the Observer Using Error
Feedback for Accuracy
The difference in measured output and estimated output is;
(19)
Adding the error to the observer gives;
where L, is the observer gain. Re-writing the equation for the
observer gives;
(20)
Deriving the State Space model The plant transfer function for the laser beam is given in
equation (3) where K, is the open-loop steady state gain is
2200mm/(V.s), , the open-loop time constant, is 0.005 s
(21)
Taking the Laplace inverse of equation (21), the equation of
motion of LBS is obtained as;
(22)
From the equation of motion, the System matrix A, and Input
matrix B is derived as
System Matrix
Input Matrix
Output Matrix Control Matrix
Writing the equation of motion in state space form gives
, where , is the state vector and is the input
vector [8, 9].
The state equation of the LBS can be written as:
(23)
where is the displacement and is the velocity of the beam Checking For System Controllability
The first step to determine if the system is controllable is to
compute the controllability matrix [10, 11]. The controllability
matrix, is derived from Matlab using ctrb (A, B) as
Let be vectors of columns 1 and 2 of matrix
respectively. If are scalar, then
Then are linearly independent of each other, thus
columns 1 and 2 are linearly independent. Since columns 1 and
2 of controllability matrix are linearly independent of each
other, it shows that the rank of controllable matrix is 2. Since
the size of the state vector is 2and the rank of the controllable
matrix is 2, then the system is controllable.
Checking for system observability Observability matrix, is derived from Matlab using obsv
(A, C) as
Since columns 1 and 2 of matrix are linearly independent,
the rank of observability matrix is 2. The system is observable
because the rank of the observable matrix is equal to the size of
the state vector, the system is observable.
Deriving the Desired Pole Locations and Gains Finding the Pole Locations
In order to achieve a desired response, poles are selected so
that the system response to disturbance is dominated by the
dynamic characteristics of the observer and not the control law.
Poles are locations in closed-loop system where desirable
response is achieved when control effort is applied. Placing the
poles are important because the location of the poles
correspond directly to the eigenvalues of the system, which
control the characteristics of the response of the system [12,
13]. If the selected poles are not desirable it will require a larger
effort to control the system making the design expensive [12].
Pole locations are theoretically derived by finding the
eigenvalues of the characteristic equation of the system from
the denominator of the closed-loop response of the LBS. The
equation for the closed loop response is
(24)
From the denominator, we got
ASME 2011 Early Career Technical Journal - Vol. 10 96
Therefore the desired poles are theoretically obtained as
But observation showed that there is a lot of noise in the
response of the system, new desired poles are then selected to
be . The control gain K, is derived
from Matlab using Ackermann formula as
K=acker(A,B,p) (25)
P is the desired pole locations. The control gain, K is [-196
20]. The Estimator Gain, L, is obtained from the Ackermann
formula using Matlab as
L’=acker(A',C', ) ' (26)
where A' and C' are the transpose of system matrix and the
output matrix respectively is desired observer pole location.
For a faster decay of the estimator error, the desired Estimator
Pole location is chosen by a factor of 5 [12].
The observer gain, L, is obtained as
The model of the LBS (plant) and its observer is built in
Simulink (Figure 12.) and simulations are performed to
determine the response of the system when using an observer-
based feedback for controlling the LBS.
Figure 12. Simulink Block Diagram of the Designed State
Feedback for Stabilizing the Laser Beam
Response of the System
Figure 13. shows the response of the LBS in Simulink that
utilizes the designed observer for control. The closed-loop
system maintains a desired response throughout for a sine
input.
Figure 13. Plot of Response of the Designed State Feedback Controller
Figure 14. shows the response of the LBS in Simulink without
a state observer. For a step input, it is observed that the beam is
displaced from the reference point and maintains the distance
throughout whiles the velocity keeps on increasing infinitely.
Figure 15. shows the response of the LBS in Simulink with a
state observer. It is observed that even after displacing the beam
from the reference point with a step input, the beam returns
back to its original position after 2 seconds onto the reference
point whiles the velocity is stabilized after 1.5 seconds.
Figure 14. Plot of the Unstable Response of the LBS
Model without State Observation
Figure 15. Plot of the Stabilized Response of the Lbs
Model with a State Observer
Comparing the Response of the State Feedback Observer and the PID Controller
The Table 2 below shows a summarized comparison of the
two controllers designed for LBS to regulate the position of the
laser beam. One of the main concerns in using PID control is
X Y
Y^
Xdot
X^ dot X^
K*uU
Sine Wave
Scope1
Scope
K*u
L
1
s
Integrator1
1
s
Integrator
K*u
C1
K*u
C
K*u
B
K*u
A1
K*u
A
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
Time (seconds)
Dis
pla
ce
me
nt(
mm
) a
nd
Ve
locit
y(m
m/s
)
displacement
velocity
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Time (seconds)
Dis
pla
ce
me
nt(
mm
) an
d V
elo
cit
y(m
m/s
)
Displacement
Velocity
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Time (seconds)
Dis
pla
ce
me
nt(
mm
) an
d V
elo
cit
y(m
m/s
)
Velocity
displacement
ASME 2011 Early Career Technical Journal - Vol. 10 97
obtaining the derivative of the output by differentiating
measured response.
Table 2. Comparison of Controllers
State Observer PID
No filter for controllers is
required. One does not need to
obtain the signal for D action in
PID.
A filter is required when taking
the derivative of the signal to be
multiplied with kd.
Since the model is expressed in
matrix-vector form, the
calculation is friendly to software
packages (e.g., MATLAB). Thus,
even if systems have a large
number of state variables, design
processes can be performed in a
relatively simple way.
Design of control requires a
relatively complicated process. It
handles scalar multi-variable
models, and requires designing
extra filters for tuning.
The state variables are obtained
through estimation. There is no
effect of an additional filter.
Difficult to get the dynamics of
the actual signal because the filter
alters the shape of the actual
signal
However, sensors are supposed to add noise to responses in
measuring, differentiating the signals can amplify the noise and
the derivative cannot be used for PID control. Thus, one should
add filters in the control loop, and the filters change the
dynamics of the system. In some cases, the filters induce
instability.
CONCLUSION This paper presented the design of a PID controller that
uses feedback from a position sensing device to rotate the
voice-coil actuator. The incident laser beam on its mirror is
reflected or directed accurately to the middle of the position
sensor even in the presence of noise and active disturbance.
Simulations results and real-time implementation of the
Laser Beam System when using the PID under dynamic
disturbances, demonstrated the necessity for a more effective
and robust design method. The paper also proposed a state
feedback method for controlling the LBS by modeling the LBS
as a linear time-invariant plant and observing the state of the
plant at all conditions, even at high frequencies. Simulations
demonstrate that the proposed state feedback method is more
effective regarding design procedure with satisfactory
responses of the system.
In future, simulations and experiments of real-time
implementation of the LBS using the state feedback observer
under dynamic disturbances needs to be conducted to confirm
the effectiveness and robustness of the method by the Authors.
This will be achieved by replacing the real plant in the
Simulink block diagram of the state feedback controller with
the LBS. The performance of the controller in real-time when a
time-delay is introduced to the system will also be analyzed.
REFERENCES [1] Duarte, F. J., 2009, Tunable Laser Applications, CRC, New
York.
[2] Ying, B., Hanqi, Z., and Roth, Z. S., 2005, "Fuzzy logic
control to suppress noises and coupling effects in a laser
tracking system," Control Systems Technology, IEEE
Transactions on, 13(1), pp. 113-121.
[3] Quanser, 2010, "Laser Beam Stabilization Instructor
Manual," Quanser Speciality Experiment series: LBS
Laboratory Workbook, Quanser, ed.
[4] Tsao, T.-C., Gibson, S., Chiu, K. C., and Chen, S.-J.,
"Adaptive control for focusing of optical drive read/write
heads," Proc. American Control Conference (ACC), 2011, pp.
588-593.
[5] Kawaji, S., and Matsunaga, N., "Design of fuzzy model
following servo control systems," Proc. Fuzzy Systems, 1995.
International Joint Conference of the Fourth IEEE International
Conference on Fuzzy Systems and The Second International
Fuzzy Engineering Symposium., Proceedings of 1995 IEEE
International Conference on, pp. 1763-1768 vol.1764.
[6] Perez-Arancibia, N. O., Gibson, J. S., and Tsu-Chin, T.,
"Laser beam pointing and stabilization by intensity feedback
control," Proc. American Control Conference, 2009. ACC '09.,
pp. 2837-2842.
[7] Hara, K., Maeyama, S., and Gofuku, A., "Navigation path
scanning system for mobile robot by laser beam," Proc. SICE
Annual Conference, 2008, pp. 2817-2821.
[8] Krokavec, D., and Filasová, A., 2007, "Pole assignment in
robust state observer design," Robust and Adaptive Control,
PLUS 2, pp. 75-78.
[9] Luenberger, D. G., 1964, "Observing the State of a Linear
System," Military Electronics, IEEE Transactions on, 8(2), pp.
74-80.
[10] Chen, C.-T., 1999, Linear System Theory and Design,
Oxford University Press, New York.
[11] Haddad, W. M., and Bernstein, D. S., 1992, "Controller
design with regional pole constraints," Automatic Control,
IEEE Transactions on, 37(1), pp. 54-69.
[12] Franklin, G. F., Powell, J. D., and Emami-Naeini, A., 1995,
Feedback Control of Dynamic Systems, Addison-Wesley,
Reading, MA.
[13] Ogata, K., 2002, Modern Control Engineering, Prentice
Hall Inc., Upper Saddle River, New Jersey.
ASME 2011 Early Career Technical Journal - Vol. 10 98
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