Sensitivty Analysis, Optimization
and Robust Design with optiSLang and
ANSYS Workbench 12
- Part II Optimization of a bearing angle
Dynamic Software and Engineering GmbH, Weimar, Germany
Agenda
• 1. Model background
• 2. Set-up of the ANSYS workbench simulation
• Parametrization of the Geometry Values
• Setting boundary conditions in ANSYS Workbench
• Meshing in ANSYS Workbench
• Solver settings in ANSYS Workbench
• Parametrization of simulation results
• ANSYS Parameter Manager
• 3. Introduction in optiPlug
• Workaround optiPlug and optiSLang
• File system in optiSLang created by optiPlug
• Exporting the simulation to optiSLang
• Default settings
• Special features of optiPlug
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
Agenda
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• 7.
Performing of a sensitivity analysis (DoE)
Checking the parametrization in optiSLang
Updating the parameter range
Starting a new Design of Experiments
Postprocessing of the sensitivity study
Optimization of the model
Reducing the number of necessary parameters
Defining an suitable obective function
Optimization of the model with the method of adaptive
response surfaces
Read-in the best design in ANSYS Workbench
Introduction in other optimization algorithms
Basics of a robustness analysis
Stochastic scatter of parameters
Write out a new robustness task with optiPlug
Update the parametrization for a robustness analysis
Summary and conclusion
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
Content
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Model background
ANSYS workbench simulation
optiPlug
Sensitivity analysis
Optimization
Robustness analysis
Summary and conclusion
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
1. Model background
• The bearing angle is part of a test bench for chains.
• In this test bench, a load of 12.6 kN is set on chains.
• The load is set in longitudinal direction.
• A load cell is fixed to the angle. This measures the load and send
it to the computer system
• Therefore the whole load of 12.6 kN is set on the winding, where
the load cell is fixed to the bearing angle
• Thus, the problematic variables are the v.Mises stress in the whole
structure and especially at the connection between the rib and the
angle short below the winding.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
1. Model background
• Workflow of a robust design optimization
Basic design
Sensitivity analysis
Optimization
Robustness analysis
Is the design robust ?
No
Yes
Robust design
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2. ANSYS Workbench simulation
• 1. Model background
• 2. ANSYS workbench simulation
• 2.1 Overview – Parametrisation in ANSYS Workbench
• 2.2 Parametrization of the geometry
• 2.3 Simulation and parametrization of the results
• 2.4 Summary - simulation
• 3. optiPlug
• 4. Sensitivity analysis
• 5. Optimization
• 6. Robustness analysis
• 7. Summary and conclusion
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.1 CAE Integration within ANSYS Workbench
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.1 Overview – Parametrization in ANSYS Workbench
• The basic of the parametrization in ANSYS is the Parameter Set on
the project page:
• Geometryparameters
(from CAD or
Design Modeler)
• Materialparameters/
Simulationparameters
• Simulationresults
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.1 Overview – Parametrization in ANSYS Workbench
• The Parameter section summarizes the
Parameters of each component
• CAD Parameters:
CAD System (external)
or ANSYS Design Modeler
• Material Parameter:
Engineering Data
• Simulation results:
Simulation
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.2 Parametrization of the geometry
• Start a new project in ANSYS Workbench
• Create a new „Static Structural (ANSYS)“ analysis
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.2 Parametrization of the geometry
• Attach the geometry file „angle.agdb“ to the project
• The geometry is already prepared but not yet parametrized
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.2 Parametrization of the geometry
• To complete the parametrization, right-click on the geometry and
select „Edit“
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.2 Parametrization of the geometry
• Mark the single parameters by clicking in the checkbox.
• The parameter dialog opens.
• Insert a reasonable parameter name.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.2 Parametrization of the geometry
• Parametrized dimension appear yellow
in you model
• After defining the parameter name,
a „D“ appears in the checkbox
• Repeat this also for extrusions and blends
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.2 Parametrization of the geometry
• Repeat this procedure for all of your desired parameters:
• XY_plane sketch 2:
• H14: DS_Rib_length
• H5: DS_Blade_thickness_vertical
• H6: DS_Blade_length_horizontal
• V7: DS_Blade_thickness_horizontal
• V8: DS_Blade_length_vertical
• XY_plane sketch 3:
• V12: DS_Rib_height
• Extrude1: DS_Blade_breadth
• Extrude2: DS_Rib_breadth
• Outer_blend: DS_Outer_Blend
• Blend_Fixing: DS_Blend_Fixing
• Blend_Bore: DS_Blend_Bore
• Rib_Blend: DS_Rib_Blend
• Edge_Blend: DS_Edge_Blend
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.2 Parametrization of the geometry
• For the blade and Rib-breadth we have to do a modification,
because of symmetry
• 1. Open the parameter section of the Design Modeler
• 2. Double the value of the parameter „ DS_Blade_breadth“ and „
DS_Rib_breadth“ in „Design Parameters“
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.2 Parametrization of the geometry
• 3. Now click on „Parameter/Dimension Assignments“:
Here, you can modify each of the parameters so that they
depend another parameters. You may also insert formulas here.
• 4. Modifiy the dimension assignment so that the value of the
parameter is divided by 2.
• 5. You can check your parametrization easily by clicking on
„Check“.
• Save the project as Angle_v12 locate in a local directory.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.3 Simulation and parametrization of the results
• Close the design modeler and start a new simulation by click
RMB on „Model“ and select „Edit“.
• See the „Parameter Set“ Box. This indicates that you are
working with parameters in ANSYS Workbench.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.3 Simulation and parametrization of the results
• Make sure that the units are switched to „mm, kg, N, °C, s“
• Insert a Refinement with a ratio of 2 on the shown faces.
• The highest stress level is expected here.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.3 Simulation and parametrization of the results
• Set the other
Mesh settings
like shown
below
• Generate the
Mesh
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.3 Simulation and parametrization of the results
• Now, stet up the static structural analysis settings
• Put a fixed support on the 4 holes.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.3 Simulation and parametrization of the results
• In addition to that set a displacement of „0“ in Y-Direction on the
ground plate. This prevents the structure to lift from the
imaginary bearing
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.3 Simulation and parametrization of the results
• Set a force in negative X-direction with a magnitude of
12.6 kN on the winding
• As results, we need the total deformation and the equivalent
Stress click solve
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.3 Simulation and parametrization of the results
Stress: 133.44 MPa
Deformation: 0.0836 mm
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.3 Simulation and parametrization of the results
• For the following analysis we need 3 simulations parameters:
• Mass (material: structural steel)
• Maximum deformation
• Maximum equivalent stress
• Parametrization just by clicking in the checkbox in the outline tree.
A „P“ indicates a successful parametrization
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.3 Simulation and parametrization of the results
• Save the project and close the mechanical simulation.
• Check the parametrization by opening the parameter section by
doubleclicking on „Parameter Set“
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.3 Simulation and parametrization of the results
• The parameters are listet like in an Excel Sheet
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
2.4 Summary Simulation
• The geometrie has been opened and parametrized
• Static-structural analysis with parametrized results
• Calculation time is about 1.5 min
• Check of the parametrization in the parameter set
• Save now the project.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
3. optiPlug
• 1. Model background
• 2. ANSYS workbench simulation
• 3. optiPlug
• 3.1. Introduction
• 3.2. File system optiPlug - optiSLang
• 3.3. Export an ANSYS project to optiSLang
• 3.4. Export the project bearing angle to optiSLang
• 4. Sensitivity analysis
• 5. Optimization
• 6. Robustness analysis
• 7. Summary and conclusion
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
3.1 Introduction
• Bidirektional Interface between optiSLang and ANYS Workbench.
• Extraction of results and input of external input-parameters to the
ANSYS parametermanager
• Starting of the Workbench using Python-skripts (ANSYS v12),
former by Java-scripting (ANSYS v11)
• optiPlug is now located on the project page in ANSYS Workbench,
therefore it is now possible to cope with different simulation types in
one optiSLang project!
• Basic feature is to write the optiSLang input and output file and
generates pre-defined basic workflows.
• optiPlug generates the complicated starting script for starting the
workbench automatically by optiSLang
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
3.1 Introduction
Preparings in ANSYS workbench:
• Definition of all parameters (design-, material-, simulation
parameters) in ANSYS Workbench
• Save your project.
Settings in optiPlug
• Choose the analysis type (optimization / stochastic)
• Default settings
optiSLang
• Modification of the variation space
• Definition of objectives and constraints
• Execution of the desired optimization / analysis runs
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
3.2 File system optiSLang - optiPlug
• optiPlug saves the files into a
subdirectory of your ANSYSproject directory:
• bin – folder:
here is the starting script
located
• opti_problems: here is the
input and output file and the
problem file located
• workflows: here you can find
all the executed workflows in
XML format
• logfiles: logfiles of optiSLang
runs
• The projectfile *.fgpr
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
3.2 File system optiSLang - optiPlug
• Files in the folder
opti_problems:
• Angle_v12_doe.pro:
problemdefinition
In the subfolder:
• Angle_v12_doe.dat :
all input variables are
saved in an ASCII format
text file
• Angle_v12_doe.dat :
all output variables are
saved in an ASCII format
text file
• The name is according to your
ANSYS project file name
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
3.3 Export an ANSYS project to optiSLang
• Start optiPlug by clicking on the „optiPlug“ Button on the project
page.
• Then, the optiPlug dialogue opens.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
3.3 Export an ANSYS project to optiSLang
• Overview optiPlug Dialogue:
• Write or read
• Problem type
• Stochastic
• Optimization
• Start Variations space
• Modify/overwrite of an
existing optiSLang project
• Save ANSYS Data*
• Show ANSYS GUI
* If you choose this option, make
sure that you have enogh space on
your harddrive for storaging a large
amount of data
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
3.3 Export an ANSYS project to optiSLang
• Default settings:
• Parameter range defaults:
• +/- 20% for
Optimizationproblems,
(suitable for first basic
simulations)
• Variationcoefficient of 5%
for stochastic analysis,
standard deviation 1σ
• Update mode:
- Warn if the optiSLang
files already exist
- Update the existing files
- Overwrite everything
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
3.4 Export the project bearing angle to optiSLang
• Click on the optiPlug Button to export the project.
• The export dialogue opens.
• You do not need to make any changes here.
• Confirm the export with OK.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4. Sensitivity analysis
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Model background
ANSYS workbench simulation
optiPlug
Sensitivity analysis – Design of Experiments (DoE)
4.1 Introduction – Sensitivity analysis
4.2 Import the project bearing angle
4.3 Modification of the parameter settings
4.4 Sampling
4.5 Performing a sensitivity analysis
4.6 Postprocessing of a sensitivity analysis
4.7 Summary
Optimization
Robustness analysis
Summary and conclusion
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.1 Introduction – Sensitivity analysis
Sensitivity analysis
Analysis of parameter
sensitivity means investigating
the effect of variability of
certain parameters on the
variability of design-relevant
response quantities.
Using stochastic sampling
methods such as
plain Monte Carlo simulation
latin hypercube sampling
with statistics to evaluation for
sensitivity calculation:
histogram, anthill plots
linear and quadratic correlation
coefficients
correlation matrix, confidence
intervals
principal component analysis
detection of most
sensitive/relevant input
variables
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.2 Import the project bearing angle
• Start optiSLang
• To import the project that you created with optiPlug, click
on „flowGuide“ and choose the „Project manager“
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.2 Import the project bearing angle
• Now, choose „Import project“
• Browsen for the project by clicking on the
button
• Choose the flowGuide project file xyz.fgpr in the defined directory
and confirm the selection with „Select“
• Conform the creation of the project with „Apply“ and close the
Project Manager with „Close“
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.3 Modification of the parameter settings
• Now, we have to modify the variation space of each Parameter.
• To do this, choose the current project and double-click on
„Parametrize_problem“ then choose the predefined „…_modify_1“
workflow.
• Confirm with „Start“ to start the parametrization.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.3 Modification of the parameter settings
• Unfold the parameter tree
by clicking „Tree“  „Unfold Tree“
• To modify a parameter, double-click
on it in the unfolded parameter tree
• Alternative: right mouse button
on one parameter and select
„Show Dialog“
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.3 Modification of the parameter settings
• In the „Parameter Settings“-dialogue
all of parameter settings are listed
• Modify at „Optimization“ the lower and
upper bounds for the parameter as shown
below
• Format type and parameter type are
already correctly predefined.
• Click on „OK“ to close the „Parameter
Settings“-dialogue.
• „Go to parameter“ causes a jump of a
marker to the parameter in the input /
output file.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.3 Modification of the parameter settings
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Modify the lower and upper
bounds of all parameters
according to the tabular.
The variation of the blends
will remain on the default
setting of +/- 10% as
predefined with optiPlug
We have got 13 design
paramters to deal with in
our sensitivity analysis
Parametername
Ref.Wert
Wertebereich
DS_Blade_thickness_vertical
24
(15-30)
DS_Blade_length_horizontal
180
(150-200)
DS_Blade_thickness_horizontal
20
(15-25)
DS_Blade_length_vertikal
160
(140-170)
DS_Rib_height
90
(50-90)
DS_Blade_breadth
80
(60-100)
DS_Rib_breadth
15
(7-20)
DS_Rib_length
40
(10-100)
DS_Blend_Edge
3
--
DS_Blend_Rib
3
--
DS_Blend_Bore
1
--
DS_Blend_fixing
1
--
DS_Outer_blend
1.5
--
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.3 Modification of the parameter settings
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•
To close the parametrization, click on „File“  „Exit“
Confirm the following dialogue boxes with „Yes“ and „OK“
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.3 Modification of the parameter settings
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Finally, you get a table of all you parameters and lower and upper bounds.
Please check your parametrization carefully.
You can also make final changes here
Close the table with „OK“.
Now the parametrization is finished.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.4.3 Sampling Summary
• State-of-the-art
of today is to
generate the
samples by
Latin Hypercube
Sampling in a
DoE!
• In our case, we
have about 4050 Samples for
a DoE
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.5 Performing a sensitivity analysis
• The Workflow of a sensitivity analysis has been predefined by optiPlug
• Already filled in:
•Worflow Identificator
(Name)
•Problem specification
file (Parametrization)
•Start script
(by optiPlug)
• Start the DoE by clicking on
“Start”
• Now the DoE dialogue opens
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.5 Performing a sensitivity analysis
• Choose “Latin
hypercube” as sampling
method
• Definine the desired
number of designs to
calculate (e.g. 40)
• Confirm with “Apply”
Then, all of the designs
will be created
40
• Start the DoE with
“OK”
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.5 Performing a sensitivity analysis
• The sampling overview window opens. In this design overview, you can
determine correlations between input parameters and check the distribution
of the parameters.
• In a good sampling,
you will only see green
boxes in the linear
correlation matrix.
• Start now the DoE
by click on “Continue”
and confirm with “Yes”
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.6 Postprocessing
• To start the postprocessing, you have to define a postprocessing workflow
• Double-click on
“Result monitoring”
• Browse for the desired
“*.bin” file in the related
directory
• Select ist and start the
postprocessing with
“start”
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.6.1 Postprocessing of a sensitivity analysis - overview
The postprocessing of a DoE gives us the following results:
•Correlation matrix – linear and quadratic:
•Shows correlations (strong and weak) between:
Input-Input, Input-Output und Output-Output
•Coefficients:
•Coefficient of Determination (CoD)
•Coefficient of Importance (CoI) – Importance of a parameter
•Regarding the CoD / CoI also leads us to reduce the parameter
space by determining and deactivating unimportant parameters
•Linear correlationcoefficient – correlation between parameters
•Principal Component Analysis:
•Another way to display the relation between inputs and outputs
•Histograms:
•Scatter of the parameters. It is also possible to determine areas
of failed designs / critical areas
•Anthill plots:
•Graphical illustration of the design space of two (2D) or three
(3D) parameters
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.6.2 Postprocessing – Evaluation of the results
• First, have a look at the linear
correlation matrix
• And the confidence levels for
0.7(-0.05 0.05) and
0.5(-0.075 0.075)
• Green stands for few / no
correlation
• Orange - red: strong
positive correlation
• Light-blue – darkblue: strong
negative correlation
• The first impression, we got from here is that we have only few but strong
correlations between some input parameters and the output parameters
• Now we have a detailed look at the Coefficients of Importance
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.6.2 Postprocessing – Evaluation of the results
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Coefficient of determination (R²) is excellent
The model can be described completely with linear relations
2 parameters a large influence on the mass
Dominating parameter: DS_Blade_breadth
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.6.2 Postprocessing – Evaluation of the results
• Have a look at this correlation in the anthill plot:
• Click in the linear correlationmatrix on the box which indicates
the highest correlation between input and output
• Alternative: Select the parameters in the pull-down menu
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.6.2 Postprocessing – Evaluation of the results
• Strong correlation between the parameter DS_blade_Breadth
and the Mass becomes quite clearly regarding the anthill plot.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.6.2 Postprocessing – Evaluation of the results
• Coefficient of determination (R²) is very good
• We can determine 2 parameters with a large influence on the stress
• Most important parameter here is: DS_Blade_thickness_vertical
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.6.2 Postprocessing – Evaluation of the results
• Quadratic correlation becomes also evident regarding the anthill
plots.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.6.2 Postprocessing – Evaluation of the results
• To calculate the COI with respect to this monotonic nonlinear
behavior we use the rank order transformation via Spearman
correlation.
• COI is larger
• DS_Rib_height is the most important parameter now
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.6.2 Postprocessing – Evaluation of the results
• Coefficient of Importance (R²) is also very good
• 2 Parameters have a large influence on the deformation
• Most important parameter here also is: DS_Blade_thickness_vertical
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.6.2 Postprocessing – Evaluation of the results
• Coefficient of Importance (R²) is also very good
• 2 Parameters have a large influence on the mass
• Most important parameter here also is: DS_Blade_breadth
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.6.2 Postprocessing – Evaluation of the results
Mass
Influence
DS_Blade_breadth
53%
DS_Bladethickness_vertical
16%
Stress
DS_Bladethickness_vertical
41%
DS_Rib_height
33%
Deformation
DS_Bladethickness_vertical
41%
DS_Rib_height
27%
• Table – most important parameters
• These parameters will be optimized now.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
4.7 Sensitivity analysis - summary
• A sensitivity analysis leads us to a better understanding of the correlations
in our model.
• We could specify the connections between the parameters in a detailed way.
• We were able to determine the most important parameters in our model.
• Therefore, the number of parameters could be reduced to three:
• DS_Blade_breadth
• DS_Bladethickness_vertical
• DS_Rib_height
• Therefore we can apply an effective optimization method that can deal
perfectly with a limited number of parameters – the adaptive response
surface method.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5. Optimization
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• 7.
Modelbackground
ANSYS workbench simulation
optiPlug
Sensitivity analysis – Design of Experiments (DoE)
Optimization
5.1 Definition of objectives and constraints
5.2 Optimization with adaptive response surfaces
5.3 Read-in the optimized model in ANSYS Workbench
5.4 Optimization with evolutionary algorithms
5.5 Other optimization algorithms
5.6 Summary
Robustness analysis
Summary and conclusion
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5. Optimization
• After performing a sensitivity analysis we take the won knowledge to
optimize our model.
• The optimization improves a model due to defined objectives.
• If you choose the right end conditions, you always get a better model
during an optimization.
• According to the desired optimization aim, you can choose the suitable
algorithm.
• An optimization can include several objectives, some even can deal with
conflicting objectives
• After optimizing a model, it has usually be checked concerning its
robustness against small variations, e.g. tolerances.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.1 Definition of objectives and constraints
• You can use the predefined problem file of the sensitivity analysis for the
optimization
• Necessary modifications:
• Deaktivate the unimportant parameters
• Define a suitable Objective
• Include Constraints if necessary
• Our optimization should follow the following aim:
• Reduce the mass
• The equivalent stress should not exceed 225 MPa.
• We use a compromise result as start solution for optimization
• In this case design 31
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.1 Definition of objectives and constraints
• To adapt the
problem-files
chose
“Parametrize
Problem” in
optiSLang and
click on “create a
copy and modify
it”.
• Browse for the
predefined File of
the sensitivity
with
and
insert a new
name (without
path!) and
• Choose reference
design
• confirm with
“Start”.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.1 Definition of objectives and constraints
• Define the best design of
the sensitivity analysis as
reference design
• In this case Design 31
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.1 Definition of objectives and constraints
• Creating an objective:
• Double-click on “Objective section”.
• Set a suitable name for the objective and click
on “New”.
• Now insert the name of
the term
(Attention: The name
mustn’t be identical to
any another name!).
• As a funktion insert the
parameter that has to be
minimized. In this case it
is “Volumenkoerper_mass”
Confirm it with “Enter”.
• Close the dialogue with “OK”
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.1 Definition of objectives and constraints
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•
•
•
Creating a boundary condition – a stress-constraint:
double-click on “Constraint section”
click on “New” at “Inequality 0<=“
The formula of the constraint is: 0<= 225 – Equivalent_Stress_Maximum
• Insert the name
(Attention: not identical
to any other name)
• As a constraint, insert
the following formula:
225-Equivalent_Stress…
and confirm with Enter.
• Close the dialogue
with “OK”
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.1 Definition of objectives and constraints
• Save the tree and exit the parametrization.
• In the overview table, set the unimportant parameters as constant
by clicking in the constant checkbox.
• Set the parameters shown right as constant.
•
•
•
•
3 parameters remain active:
DS_Bladethickness_vertikal
DS_Blade_breadth
DS_Rib_height
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.1 Definition of objectives and constraints
• Now we have:
• Deactivated the unimportant parameters
• Defined a suitable objective
• Defined a necessary constraint
• Click through the different cards to check you settings
• Close the Parametrization with “OK” and confirm any changings.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.2 Optimization with adaptive response surface methods
•
•
•
•
ARSM provides a local linearization (local DoE) in the design space.
Therefore for each subspace of our 3 Parameters, only 6 design points are
necessary if we choose the D-optimal linear scheme for the local DoE
Usually, the ARSM converges after 10-20 iterations.
So we need
60 – 120
FE-Simulations
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.2.1 Setup the ARSM
• Start the ARSM Workflow by a double-click on Adaptive_...
• Set a Workflow Identificator (Name)
• Browse for the adapted problem file with
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.2.1 Setup the ARSM
• Choose „Run a script file“ and browsen with
for the start script
in the \bin folder of your project or copy it from the DoE dialogue.
• Choose the number of parallel runs and idle time according to your
hardware.
• Start the ARSM with „Start“.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.2.1 Setup the ARSM
• You can now make some expert settings.
• Click „OK“ to start the optimization.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.2.2 Postprocessing ARSM
• 1. Iteration History:
• Here you can see the history of your objective for each iteration
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.2.2 Postprocessing ARSM
• 2. Response Data:
• Here you can see the results (outputs) of your design.
• As default the best design is chosen, so that you can now get an
information how much the algorithm could improve your model.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.2.2 Postprocessing ARSM
• 3. Design Parameter:
• These are the CAD parameter values of your optimization.
• The default setting is also the best design.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.2.3 Summary ARSM
• Performing an optimization with ARSM, a significant design
improvement could be achieved.
• The mass was reduced by 36 % from 4,89 to 3,11 kg
• The stress increased by um 69 % to 225 MPa.
• The number of design evaluation is 40 + 57 = 97
• The stress remains in the given constraints
• Therefore we can say that regarding the boundary conditions, the
part has been optimized as good as possible.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.3 Read-in the optimized model in ANSYS Workbench
• Now we want to read in our best design in Workbench to have a
look at the geometry and for further analysis.
• To do this, just open the project and simulation in workbench again.
• The geometry read-in will be done by clicking on the optiPlug
button on the project page.
• Now, the optiPlug dialogue opens again.
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.3 Read-in the optimized model in ANSYS Workbench
• Select „Read calculated design“
• Browse for the correct best design
• In our case it is the Design 57 (this is different usually for each
optimization run!) and confirm your selection.
• If you want to calculate your model again, just update the results
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.3 Read-in the optimized model in ANSYS Workbench
• Calculating the results of the initial and optimized design
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.3 Read-in the optimized model in ANSYS Workbench
• Mesh refinement of the optimized design to prove the maximal
stress
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.3 Read-in the optimized model in ANSYS Workbench
• Generate the new mesh
• Press solve
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench
5.3 Read-in the optimized model in ANSYS Workbench
• Only a small increasing of the stresses
Sensitivity analysis, optimization and robust design with optiSLang and ANSYS Workbench