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VisualDOC: A Practical Software
Integration and General Purpose Design
Optimization Solution SuiteOptimization Solution Suite
Ashwin P Gurnani, Juan Pablo Leiva, Garret N. Vanderplaats
Vanderplaats Research & Development, Inc.
Colorado Springs, CO, USA
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Outline of Presentation
• Introduction
• VisualDoc Framework
• VisualDoc Grapical Interfaces and Key Windows
• Examples
• Summary
VisualDOC: A Practical Software Integration and General Purpose Optimization Solution Suite 2
• Summary
• Conclusions
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Introduction
• Design of large scale products and processes require interaction
from several different disciplines.
• These disciplines utilize computationally expensive analyses codes,
for example, FEA, CFD etc.
• With rapidly increasing costs, never before has there been a need
for producing optimally designed products.
VisualDOC: A Practical Software Integration and General Purpose Optimization Solution Suite 3
for producing optimally designed products.
• This paper presents a product design solution suite that provides
engineers the capability to integrate multiple analyses software
AND optimize designs over these multiple analyses codes.
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VisualDOC – Framework
GUIVisualScript
Integration Utility
• Database is the central component of the software.
• Saved as platform independent .vdb file
VisualDOC: A Practical Software Integration and General Purpose Optimization Solution Suite 4
User Defined
Analysis Program3rd Party
Analysis
Database
Response Surface
Optimization
Gradient-based
Optimization
Design of
Experiments
Evolutionary
Optimization…
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VisualDOC Introduction
• VisualDOC GUI
Inputs
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Responses
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Optimization Modules
Select Optimization Module
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Gradient Based Optimization
Constrained
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Unconstrained
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Design of Experiments
• Create an experimental
design
Select Sampling Method
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Select DOE Action
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Design of Experiments
• Approximate responses
Select Approximation Model
Select Sampling Method
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Select DOE Action
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Design of Experiments
• RS
coefficients
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• Actual vs.
Predicted
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Response Surface Optimization
Select the Approximation Model Order
Co
nvers
ion
of
Ch
em
ical P
rocess (%
)
Co
nvers
ion
of
Ch
em
ical P
rocess (%
)
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Select the Starting Strategy
Co
nvers
ion
of
Ch
em
ical P
rocess (%
)
Co
nvers
ion
of
Ch
em
ical P
rocess (%
)
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Non-Gradient Based Optimization
Select Optimization Algorithm
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Select Starting Strategy
Define Algorithm related parameters
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Probabilistic Analysis and Optimization
Set up random Inputs and Responses
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Select Probabilistic Analysis Method
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Probabilistic Analysis and Optimization
Pro
ba
bili
ty Constraint
Reliability OptimizationControl the probability of failure
Robust OptimizationControl the variability of a response
VisualDOC: A Practical Software Integration and General Purpose Optimization Solution Suite 14
Pro
ba
bili
ty
Probability of failure
y0
ConstraintBound
x1x2
∆ y1
∆ y2
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Direct Interface --Excel
• Prepare Excel
spreadsheet
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• Define inputs and
responses in
VisualDOC
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Direct Interface -- Excel
• Create VisualDOC-
Excel interface
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• Use the Interface as the analysis program
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Direct Interface -- Matlab
• Prepare Matlab M-file
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• Define the Inputs and Responses in VisualDOC
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Direct Interface -- Matlab
• Create a task
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• Specify the Matlab M-file as the analysis program
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Coupling Third Party Software Through VisualScript
Create analysis workflow in VisualScript
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Extract the Inputs from the input fileCall third party analysis programExtract the Outputs from the output file
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VisualDOC API
• VisualDOC C/C++ Application Programming Interface (API) functions allow users to embed all the capabilities of VisualDOC into users’ own program.
• These capabilities include
– Direct Gradient-based Optimization (DGO)
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– Direct Gradient-based Optimization (DGO)
– Response Surface Approximate Optimization (RSA)
– Design Of Experiments (DOE)
– Non-gradient-based Optimization (NGO)
– Single Analysis
– Probabilistic Optimization.
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Parallel Analysis
• Put multiple analysis elements in the Parallel Container
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• Add Parallel Container to the workflow
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Remote Run
• Analysis – Remote computers
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• VisualDOC –
local computer
Define remote connection properties
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Create and Run Task
• Create an optimization task
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• Run the optimization task and graphically visualizing
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Post Process the Optimization Results
Plot Objective
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Plot Design Variables
Plot Responses and show the constraints
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“What-If?” Study Tool
Original optimization result
New optimization result
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Relax the displacement constraint
Run Approximate Optimization with new constraint
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Examples
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Problem Description
• LS-Dyna Optimization
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Optimization Problem
• Objective– Maximize Internal Energy
• Design Variables– Thickness of the shell elements
– Shape changes of the tube
– Linking to ensure symmetry
Original Shape
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S1
Perturbation - S13Perturbations except S13
S2
S3 ~ S6
S7 ~ S10
S11
S12
S13
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VisualScript
• Used to define the interaction between VisualDOC and LS-Dyna
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VisualScript
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Design VariablesResponses
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VisualDOC
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Results
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Initial Design Optimal Design
Internal Energy = 10407.20 Internal Energy = 31795.60
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Results
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Case Study – Turbomachinery System Design
AxSTREAMAxSTREAM
Preliminary Design
Meshing
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GENESIS / VisualDOCGENESIS / VisualDOC
Structural and CFD
Analysis and Optimization
Blades, Disks, Complete SystemsBlades, Disks, Complete Systems
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Case Study – Turbomachinery System Design
Axis of Rotation
(-z)
TIP/SHROUDSpan Layer 100%
(r = 5)
HUBSpan Layer 0%
(r = 1)
• The blade geometry is divided into
5 constant radius layers evenly
distributed along the span
(0.00% - Hub and 100 % - Shroud)
• At each span layer, the mean
(camber) line is generated using a
4-point Bezier control polygon+ Z (M’)
b
CP1, LEs = 1
CP2, s = 2
CP4, TEs = 4
CP3, s = 3
VisualDOC: A Practical Software Integration and General Purpose Optimization Solution Suite 35
• Each Bezier Control Point (CP) is
specified as a (M’r|s, b) coordinate
pair:
(1 ≤ r ≤ 5) – Hub to Shroud Index
(1 ≤ s ≤ 4) – LE to TE Index
ZZZRRM ∂=∂⋅∂+∂⋅∂=∂
∂
∂=
'tan
Ma
θβ
R
MM
∂=∂ ' ∫ ⋅∂=
s
dsMM0
''
S - Fractional distance along the curve 10 ≤≤ S
100*'
''
|
|
|
=
TEr
sr
sr
M
MM
• LE Edge Sweep (qr) indicated at
each Span Layer�A radial interpolation between theairfoils at the each layer to generatethe 3D blade Shape
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Case Study – Turbomachinery System Design
Objective:
Maximize efficiency
NEWNEWDESIGNDESIGN
VARIABLESVARIABLES
OPTIMIZEROPTIMIZER(VISUALDOC)(VISUALDOC)
GENERATE BLADE GEOMETRY
[CFX-BLADEGEN]
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RESULTSRESULTS
BLADE PASSAGE CFD ANALYSES
[CFX-BLADEGEN(PLUS)]
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Case Study – Turbomachinery System Design
Blade Results:
• An average of 9% increase in total efficiency was obtained
• Robustness of the approach evidenced in its ability to yield
significant efficiency gains in spite of the noisy objective function.
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Case Study – Engine Head Gasket
2-D Z-Flow engine cooling system (Block, Head, and Gasket)
Objective: Maximize the fluid velocity averaged at 10 locations
Design variables: Holes’ diameter and location (offset from center)
(12 design variables total)
Constraints: Minimum velocity at 10 locations
Analysis: FLUENT CFD program
VisualDOC: A Practical Software Integration and General Purpose Optimization Solution Suite 38
Velocity_Head_Point_1
Velocity_Block_Point_1
VBP_2 VBP_3 VBP_4VBP_5
VHP_2
VHP_3 VHP_4
VHP_5
Inlet Velocity
(8.577 m/s)
BLOCK
HEAD
Hole 1 Hole 2 Hole 4 Hole 5 Hole 6Hole 3
850 mm
Gasket
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Case Study – Engine Head Gasket
• GAMBIT (mesher) journal file
parameterization
• Block / Head mesh is unchanged
during optimization
Blockhead.msh
HoleZone.msh
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• Hole regions mesh is changed by
VisualDOC using GAMBIT
journal fileBlockhead.msh
GasketGasket
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Case Study – Engine Head Gasket
Initial design
Velocity constraint not satisfied at 2 locations initially
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Optimal design
-- 2424 FLUENT calls
- Constraints satisfied after 15 15 FLUENT calls
- Average velocity increase: 2020%%
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Case Study – Integration with MoldFlow
• VisualDOC has been integrated with MoldFlow’s MoldFlow Plastics Insight
(MPI) software to determine Optimal gate location.
• Optimal gate location will be the geometry that minimizes the maximum
pressure at the end of the filling.
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Case Study – Integration with MoldFlow
• The Flat Plate Below is Comprised of Four Different Thickness
Regions
• VisualDOC Will Locate the Three Gates so that the Maximum
Pressure at the End of Filling will be Minimized
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Initial Gate Locations
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Case Study – Integration with MoldFlow
Optimization Process Sequence
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Case Study – Integration with MoldFlow
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Optimal Gate Locations
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Case Study – Integration with MoldFlow
• During 15 iterations, injector X and Y coordinates for each gate migrated to
plate locations that minimized the maximum pressure at the end of fill time.
• Total 22 analyses were performed for this optimization.
VisualDOC: A Practical Software Integration and General Purpose Optimization Solution Suite 45
• VisualDOC optimization parameters may be set to reduce total number of
analyses to achieve “better” rather than “best” design.
• A better guess for the initial gate locations will also reduce total number of
analyses.
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Summary
Set up Input and Output
Set Optimization Module and Respective Options
Integrate with Analysis
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Integrate with Analysis
Create and Run Task
Post-processing
“What-If?” Study
![Page 47: VisualDOC: A Practical Software Integration and General ......s = 1 CP2, s = 2 CP4, TE s = 4 CP3, s = 3 VisualDOC: A Practical Software Integration and General Purpose Optimization](https://reader033.vdocuments.mx/reader033/viewer/2022051805/5ff53579bcd2aa03df2f3d1f/html5/thumbnails/47.jpg)
Conclusions
• This paper presents a solution suite for complete product design optimization,
namely VisualDOC.
• VisualDOC has the capability to wrap design and optimization algorithms
around any ASCII based 3rd party analysis software.
• VisualDOC can be used within parallel computing clusters where different
analysis are run concurrently on remote machines. This allows for solution of
VisualDOC: A Practical Software Integration and General Purpose Optimization Solution Suite 47
analysis are run concurrently on remote machines. This allows for solution of
large scale optimization problems that involve several computationally
intensive analysis codes.
• VisualDOC’s optimization routines have proven to improve product designs
and product design processes.