present 14 08-2012
DESCRIPTION
Test for thesis defendTRANSCRIPT
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CFD EVALUATION OF SUITABLE SITE FOR WIND TURBINE OF THE
FLOW OVER TERRAIN
THITIPONG UNCHAI Advisor
ASST.PROF.DR.ADUN JANYALERTADUN
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∗ Introductions ∗ Objective ∗ Scope of works ∗ Research Procedure ∗ Theory, Literature reviews and Results of
∗ Potential Energy Assessments ∗ CFD simulations
∗ Pha Taem Hill ∗ Comparison of terrain and geometry shape
∗ Chart generation ∗ Discussions
Topics
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∗ Investigate wind energy potential of Ubonratchathani region.
∗ Find suitable site of wind turbine using CFD simulations.
∗ Generate suitable site chart for trapezoid hill shape.
Objective
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∗ Wind data collection at 10, 30 40 m height. ∗ Wind energy potential assessment using Weibull
distribution. ∗ CFD simulation of Pha Taem hill. ∗ CFD simulation compare of terrain and geometry
shape. ∗ Generate wind turbine suitable site chart using
extrapolation technique.
Scope of the work
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∗ Wind energy
Introduction
Source : http://www.jewishpolicycenter.org/1415/investment-opportunity-of-the-21st-century
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Wind Power Class
At a height of 10 m Height of 50 m
Wind Power
Density (W/m2)
Speed (m/s)
Wind Power Density (W/m2)
Speed (m/s)
1 0 – 100 0 – 4.4 0 – 200 0 – 5.6
2 100 – 150 4.4 – 5.1 200 – 300 5.6 – 6.4
3 150 – 200 5.1 – 5.6 300 – 400 6.4 – 7.0
4 200 – 250 5.6 – 6.0 400 – 500 7.0 – 7.5
5 250 – 300 6.0 – 6.4 500 – 600 7.5 – 8.0
6 300 – 400 6.4 – 7.0 600 – 800 8.0 – 8.8
7 400 – 1000 7.0 – 9.4 800 – 2000 8.8 – 11.9
Introduction
Source : The U.S. Dept. of Energy defined a wind power scale in the Wind Energy Resource Atlas of the United States, published in 1986.
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∗ Wind turbine site
Introduction
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∗ Wind potential site in Thailand
Introduction
Region Province Power Class Wind speed
(50 m) Wind power
Tai Rom Yen National Park Nakhon Si Thammarat 6 - 7 8.00 – 11.90 600 – 2,000
Khao Luang National Park Nakhon Si Thammarat 6 – 7 8.00 – 11.90 600 – 2,000
Khao Pu - Khao Ya National Park Phatthalung 6 – 7 8.00 – 11.90 600 – 2,000
Wong – Jao National Park Tak 6 8.00 – 8.80 600 – 800
Doi Inthanon Chiang Mai 4 7.00 – 7.50 400 – 500
Kaeng Krung National Park Surat Thani 4 – 5 7.00 – 8.00 400 – 600
Pranom-Benja National Park Krabi 6 8.00 – 8.80 600 – 800
Ranot Songkhla 4 7.00 – 7.50 400 – 500
Songkhla Lake Songkhla 5 – 6 7.50 – 8.00 500 – 700
Gulf of Pattani Pattani 4 7.00 – 7.50 400 – 500
Hua Sai Nakhon Si Thammarat 3 6.40 – 7.00 300 – 400
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∗ Alternative Energy Development Plan: 2012-2021
Introduction
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∗ Wind turbine site
Introduction
Source : http://www.acusim.com/html/apps/windTurbSiting.html
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∗ Wind turbine site
Introduction
Source : http://www.lec.ethz.ch/research/wind_energy/cfd
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∗ Wind turbine site
Introduction
Source : Paul Stangroom. CFD Modelling of Wind Flow Over Terrain. Ph.D. thesis of University of Nottingham. 2004
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∗ Wind turbine site
Introduction
Source : Keith W. Ayotte. Computational modelling for wind energy assessment. Journal of Wind Engineering and Industrial Aerodynamics. 96: 2008, 1571–1590
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∗ Wind measurement and potential energy assessment. ∗ Simulation of Pha Taem hill. ∗ Comparison of terrain and geometry shape. ∗ Generate suitable position site chart.
Research Procedure
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∗ Literature reviews
* Extrapolation from the data using the Power-Law are presented.
Wind measurement
Researchers Heights Periods
A. Keyhani et al. 10 m 11 years
Ramazan Kose 10, 30 m 20 months
Meishen Li 10 m 5 years
Murat Gokcek et al. 10 m 5 months
Murat Gokcek et al. 6 – 12 m (11 stations) 3 years
W. Al-Nassar et al. 10, 30, 60 m* 46 years
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Parameters Details
Location Khong Chiam, UBN
Height from sea level 123 m
Sampling Period 1 hr
Data collected January 1, 2008 to December 31, 2010
Wind measurement
Anemometer
Anemometer
Wind Vane
Data Logger
Anemometer
Thermometer &
Barometer
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∗ Wind speed
Wind measurement
0.0
1.0
2.0
3.0
4.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Win
d Sp
eed
(m/s
)
Month
2008 2009 2010 Average
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Wind measurement
0.0
1.0
2.0
3.0
4.0
0:00
2:00
4:00
6:00
8:00
10:0
0
12:0
0
14:0
0
16:0
0
18:0
0
20:0
0
22:0
0
Win
d Sp
eed
(m/s
)
Hour of Day
10 m 30 m 40 m
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∗ Wind direction
Wind measurement
0%
5%
10%
15% N
NNE NE
ENE
E
ESE
SE SSE
S SSW
SW
W
NW
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∗ Power of the wind
∗ Wind power density developed by Weibull distribution
where k is shape factor and c is scalar factor
Potential energy assessment
3
21 AVP ρ=
∫∞
+
Γ==0
33 321)(
21
kkcdVVfV
AP ρρ
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∗ Weibull distribution ∗ Graphical method
∗ Approximated method
Potential energy assessment
086.1−
=
mVk σ
( )kVc m
/11+Γ=
−= kb
ec
( )( )[ ] ( ) ( )00 lnln1lnln VkckVVF +−=≤−−
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Month Vm
Meteorological Weibull approximated Weibull graphical
P/A (W/m2)
E/A (kWh/m2)
P/A (W/m2)
E/A (kWh/m2)
P/A (W/m2)
E/A (kWh/m2)
January 3.91 36.61 27.24 85.97 63.96 82.23 61.18 February 3.72 31.53 21.19 62.39 41.92 59.79 40.18 March 3.64 29.54 21.98 64.05 47.65 61.31 45.61 April 3.31 22.21 15.99 103.17 74.28 93.59 67.39 May 3.23 20.64 15.36 78.47 58.38 88.11 65.55 June 3.29 21.81 15.70 111.25 80.10 97.45 70.17 July 3.50 26.26 19.54 171.73 127.77 122.22 90.93 August 3.53 26.94 20.04 136.13 101.28 123.13 91.61 September 2.83 13.88 10.00 97.52 70.21 91.76 66.07 October 3.46 25.37 18.88 100.86 75.04 94.96 70.65 November 4.19 45.06 32.44 113.08 81.42 80.58 58.01 December 3.73 31.79 23.65 104.42 77.69 107.07 79.66
Annual 3.53 331.65 242.00 1,229.03 899.71 1,102.20 807.01
Potential energy assessment
Note : Details of wind power density calculations are indicated in Appendix B
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Pha Taem hill
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Geographic Information System
∗ Geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present all types of geographical data.
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∗ Literature reviews
Geographic Information System
Source : Atsushi Yamaguchi, Takeshi Ishihar and Yozo Fujino. Experimental study of the wind flow in a coastal region of Japan. Journal of Wind Engineering and Industrial Aerodynamics 91 (2003) 247–264
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∗ Literature reviews
Geographic Information System
Source : Paul Stangroom. CFD Modelling of Wind Flow Over Terrain. Ph.D. thesis of University of Nottingham. 2004
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Contour Terrain topography
Pha Taem hill
Parameters Details
Resolution 1 : 50,000
height 240 m
Front slope 20.06°
Rear slope 1.66°
Ground Rocky flat plate
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Applying the fundamental laws of mechanics to a fluid gives the governing equations for a fluid. The conservation of mass equation
𝜕𝜕𝜕𝑡
+ 𝛻 ∙ 𝜕𝜌 = 0
and the conservation of momentum equation
𝜕𝜕𝜌𝜕𝑡
+ 𝜕 𝜌 ∙ 𝛻 𝜌 = −𝛻𝛻 + 𝜕�⃗� + 𝛻 ∙ 𝜏𝑖𝑖
Computational Fluid Dynamics
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The term 𝜏𝑖𝑖 in the equation is the Reynolds stress for Reynolds Averaged Navier-Stokes equations
𝜏𝑖𝑖 = −𝑢𝑖′𝑢𝑖′ and the subgrid-scale stress for Large Eddy Simulation
𝜏𝑖𝑖 = 𝑢�𝑖𝑢�𝑖 − 𝑢𝑖′𝑢𝑖′
Computational Fluid Dynamics
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Researcher Article Topics Knowledge
Ove Undheim
Comparison of turbulence models for wind evaluation in complex terrain
• Use k-l and k-ε model
• k-l model indicated more inaccuracy
Pual Carpenter and Nicholas Locke
Investigation of wind speeds over multiple 2D hills
• Use k-ω, k-ε model • Imported model
from GIS data
• k-ω model improved k-ε
Keith W. Ayotte
Computational modelling for wind energy assessment
• Comparison LES simulation with European Wind Atlas
• Wind Atlas using extrapolation method to predict speed in vertical and horizontal
D.D. Apsley and M.A. Leschziner
A new low-Reynolds-number nonlinear two-equation turbulence model for complex flows
• low-Reynolds-number flow
• Use RSM , k-ε model
• In low-Reynolds-number flow, RSM is better than k-ε
Turbulence Models dependence
• Literature reviews
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∗ Standard k-ε model ∗ The two-equation k-ε turbulence model and its variants
are most commonly used in wind energy researches.
𝜇𝑡 = 𝐶𝜇𝜕𝑘2
𝜀
where k is turbulence kinetic energy ε is dissipation rate
Turbulence Models
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∗ Standard k-ω model ∗ One of the advantages of the k-ω formulation is the near
wall treatment for low-Reynolds number computations.
𝜇𝑡 = 𝜕𝑘𝜔
where k is turbulence kinetic energy ω is specific dissipation rate
Turbulence Models
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∗ Reynolds Stress Model ∗ Using differential transport equations for calculation of
the individual Reynolds stresses, 𝑢𝑖′𝑢𝑖′. The individual Reynolds stresses are then used to obtain closure of the Reynolds-averaged momentum equation.
Turbulence Models
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Properties Coarse Medium Fine
Dimension 2,000×1,000 m 2,000×1,000 m 2,000×1,000 m
First grid cell size 0.25493 m 0.04181 m 0.00709 m
Increasing rate 8% 10% 12%
Largest cell size 70.20 m 85.64 m 96.59 m
Grid dependence
Coarse Medium Fine
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∗ Grid resolution sensitivity
Grid dependence
0 1 2 3 4
x/H = 3
0 1 2 3 4
Wind Speed
x/H = 1
1.00
1.02
1.04
1.06
1.08
1.10
0 1 2 3 4
Hei
ght (
Z/H
)
x/H = 0 Coarse
Medium
Fine
Measure
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∗ Literature reviews
Initial and boundary conditions
Researcher Article Topics
T. Takahashi et al., Turbulence characteristics of wind over a hill with a rough surface
• Increased porous fences from 0% to 100%.
Bert Blocken
CFD simulation of the atmospheric boundary layer : wall function problems
• Simulated problem of wall function.
• Irrespective of the wall functions and near wall grid resolution.
D.M. Hargreaves and N.G. Wright
On the use of the k–ε model in commercial CFD software to model the neutral atmospheric boundary layer
• Shear stress applied to the top boundary of the domain.
O. Undheim 2D simulations of terrain effects on atmospheric flow
• The influence of a roughness change spreads upwards in the boundary layer downstream from the roughness change.
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Parameters Type Solver Segregated Formulation Implicit Space 3D Time Unsteady Velocity Formulation Absolute Unsteady Formulation 1st Order Implicit Turbulent Model - standard k-ε model
- standard k-ω model - Reynolds Stress Model
Near Wall Treatment Enhance Wall Treatment Model Constant Cμ , Cε1 , Cε2 , σk , σε Air Density 1.225 kg/m3 Time Step 0.1 second Max Iteration / Time Step 20,000 Inflow boundary Velocity inlet Outflow boundary Pressure outlet Top boundary Zero-gradient Ground boundary Wall
Initial and boundary conditions
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∗ Wind velocity profile at reference station
Results
0
0.05
0.1
0.15
0.2
0 0.5 1 1.5 2 2.5 3 3.5 4
Hei
ght (
Z/H
)
Wind speed (m/s)
k-epsilon k-omega Reynolds stress measure
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∗ Comparison of increasing speed at hill top
Results
0.00
0.05
0.10
0.15
0.20
-20% -10% 0% 10% 20%
Hei
ght (
Z/H
)
Wind speed increase (%)
k-epsilon k-omega Reynolds stress measure
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∗ Comparison of velocity in longitudinal direction
Results
0
100
200
300
400
500
0 1 2 3 4
Hei
ght (
Z/H
)
x/H = -1.0
0 1 2 3 4
x/H = 0
0 1 2 3 4
x/H = 1.0
0 1 2 3 4
x/H = 2.0
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∗ Comparison of turbulence kinetic energy in longitudinal direction
Results
0
100
200
300
400
500
0.00 0.05 0.10 0.15 0.20
Hei
ght (
m)
TKE (m2/s2)
x/H = -1.0
0 0.05 0.1 0.15 0.2TKE (m2/s2)
x/H = 0
0.00 0.05 0.10 0.15 0.20TKE (m2/s2)
x/H = 1.0
0.00 0.05 0.10 0.15 0.20TKE (m2/s2)
x/H = 2.0
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Comparison of Pha Taem geography and geometry shape
0
0.5
0 1 2 3
Hei
ght
Distance
Geometry Geography hill
y = 0.3653x - 0.0352 R² = 0.9549
0.0
0.2
0.4
0.0 0.5 1.0
Hei
ght
Distance
y = -0.029x + 0.3994 R² = 0.9543
0
0.2
0.4
1.0 2.0 3.0 4.0Distance
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∗ Comparison of increasing speed
Results
1.0
1.1
1.2
-20%-10% 0% 10% 20%
Ver
tical
hei
ght (
Z/H
)
k-epsilon model
1.0
1.1
1.2
-20%-10% 0% 10% 20%k-omega model
1.0
1.1
1.2
-20%-10% 0% 10% 20%Reynolds Stress model
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∗ Comparison of increasing turbulence kinetic energy
Results
1.0
1.1
1.2
0% 10% 20% 30%
Ver
tical
hei
ght (
Z/H
)
k-epsilon model
1.0
1.1
1.2
0% 10% 20% 30%k-omega model
1.0
1.1
1.2
0% 10% 20% 30%Reynolds Stress model
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Parameters Details
Hill angle 5°, 10°, 15°, 20°, 25°, 30°, 35°, 40°, 45°
Wind speed 2, 4, 6, 8, 10 m/s
Roughness height 0.0001, 0.001, 0.01, 0.1 m
Wind turbine suitable site chart Land Cover Types
Typical Roughness Length (m)
Farmland and grassy plains Many trees and hedges, a few buildings Scattered trees and hedges Many hedges Few trees (summer) Crops and tall grass Isolated trees Few trees (winter) Snow-covered cultivated farmland Large expanses of water Flat desert Snow-covered flat ground Mud flats and ice
0.002-0.30 0.30 0.15 0.085 0.055 0.050 0.025 0.010 0.002 0.0001-0.001 0.0001-0.001 0.0001 0.00001-0.00003
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Wind turbine suitable site chart
0.00
0.05
0.10
0.15
0.20
0.0 0.1 0.2 0.3 0.4 0.5
heig
ht (
Tim
e of
hill
hei
ght)
length ( Time of hill height)
10°
20°
30°
40°
10
20
30
40
Suitable site chart for trapezoid terrain (Roughness 0.0001)
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Wind turbine suitable site chart
0.00
0.05
0.10
0.15
0.20
0.0 0.1 0.2 0.3 0.4 0.5
heig
ht
length
10°
20°
30°
40°
10
20
30
40
Suitable site chart for trapezoid terrain (Roughness 0.001)
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Wind turbine suitable site chart
0.00
0.05
0.10
0.15
0.20
0.0 0.1 0.2 0.3 0.4 0.5
heig
ht (
Tim
e of
hill
hei
ght)
length ( Time of hill height)
10°
20°
30°
40°
10
20
30
40
Suitable site chart for trapezoid terrain (Roughness 0.01)
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Wind turbine suitable site chart
0.00
0.05
0.10
0.15
0.20
0.0 0.1 0.2 0.3 0.4 0.5
heig
ht
length
Suitable site chart for trapezoid terrain (Roughness 0.1)
10°
20°
30°
40°
10
20
30
40
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∗ Wind energy potential of UBN regions is about 1,102 – 1,229 W/m2
∗ The simulation results of Pha Taem hill indicated that RSM method is most similar with measurements.
∗ Comparison of hill and geometry are 95.46% matching terrain with 9.72% of difference results.
∗ Extrapolation technique have been used to generated wind turbine suitable site charts.
Concluding Remarks
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∗ More measure station with more frequency and more sensitive instruments should be provided to collect most accurate wind data.
∗ The measurement data from the station in single row do not indicated that same particle of the air.
∗ The 1:50,000 scales of data include much error into the simulations because GIS data non-included any obstacles.
Suggestion
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Thank you