Part 1
Introduction to optiSLang
Challenges in Virtual Prototyping
• Virtual prototyping is necessary for cost efficiency
• Test cycles are reduced and placed late in the product development
• CAE-based optimization and CAE-based robustness evaluation
becomes more and more important in virtual prototyping
• Optimization is introduced into virtual prototyping
• Robustness evaluation is the key methodology for safe, reliable
and robust products
• The combination of optimizations and robustness evaluation will
lead to robust design optimization strategies
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Part 1&2: Introduction and Process integration
Application of Multidisciplinary Optimization
• Virtual prototyping is an interdisciplinary process
• Multidisciplinary approach requires to run different solvers in
parallel and to handle different types of constraints and objectives
• Arbitrary engineering software with complex non-linear analysis
have to be connected
• The resulting optimization problem may become very noisy, very
sensitive to design changes or ill conditioned for mathematical
function analysis (e.g. non-differentiable, non-convex, nonsmooth)
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Part 1&2: Introduction and Process integration
Application of Stochastic Analysis
• Structural models become increasingly
detailed
• Substantially more precise data is
required for the analysis, also about
uncertainties
• Optimized designs lead to high
imperfections in sensitivities
• Optimized designs tend to loose
robustness
• Virtual prototyping calls for stochastic
analysis to ensure robustness,
reliability and safety
• Variance-based robustness analysis
identifies the sensitivities and shows
the response scattering
• Reliability-based robustness analysis
(reliability analysis) quantifies product
risks
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Part 1&2: Introduction and Process integration
How to make a product safe and optimal?
Optimizing high end products may require the consideration of the
reliability or safety aspect.
Ensuring safety with global safety factors (load factors) result in
conservative designs and may need verification using tests or
simulation.
If reliability (safety) needs to be introduced into CAE-based virtual
product development, stochastic analysis is the method of choice.
Measuring reliability and introducing this measurements into the
optimization process leads to robust design optimization.
Introducing stochastic analysis is not trivial, a good balance between
Know-how of uncertainties, stochastic methodology and statistic
post processing is the success key.
DYNARDO and optiSLang are technology leaders.
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Part 1&2: Introduction and Process integration
Excellence of optiSLang
optiSLang is an algorithmic toolbox for sensitivity analysis,
optimization, robustness evaluation, reliability analysis and robust
design optimization.
optiSLang is the commercial tool that has completed the necessary
functionality of stochastic analysis to run real world industrial
applications in CAE-based robust design optimizations.
optiSLang development priority: safe of use and ease of use!
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Part 1&2: Introduction and Process integration
Robust Design Methodology Definition
Start
Robust Design Optimization
Robust Design
Optimization
Variance based Robustness
Evaluation
Sensitivity Study
Probability based
Robustness Evaluation,
(Reliability analysis)
Single & Multi objective
(Pareto) optimization
CAE process (FEM, CFD, MBD, Excel, Matlab, etc.)
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Part 1&2: Introduction and Process integration
Part 2
Process Integration
Process Integration
Parametric modeling as base for
• Customer defined optimization design space
• Naturally given robustness/reliability space
Design variables:
Entities that define
the design space
Scattering variables:
Entities that define
the robustness space
9
The CAE process
generates the
results according
to the inputs
Part 1&2: Introduction and Process integration
Result variables:
measures from
the system
optiSLang Process Integration
Arbitrary CAE-processes can be integrated with optiSLang. Default
procedure is the introduction auf inputs and outputs via ASCII file
parsing. Additionally interfaces to CAE-tools exist.
Available interfaces in
optiSLang
CATIA v5 interface
ANSYS workbench
interface
Excel Plugin
Extraction tool kit
(ABAQUS, LS-DYNA)
Madymo positioner
Connected CAE-Solver: ANSYS, ABAQUS, NASTRAN, LS-DYNA,
PERMAS, Fluent, CFX, Star-CD, MADYMO, SLang, Matlab, Excel,…
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Part 1&2: Introduction and Process integration
optiSLang Process Integration
• optiSLang offers simple-to-use predefined workflows with robust
default settings
• Script flow and parameterization editor for process integration
• Flows for sensitivity, optimization, robustness and reliability
• Post processing flow, revaluation flow
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Part 1&2: Introduction and Process integration
Workflow name and identificator
• Workflow name is used as name in the workflow tree
• Workflow identificator is used as part of the name of the working
directory and of appropriate files
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Part 1&2: Introduction and Process integration
optiSLang directory handling
../tutorial1/
project directory
../tutorial1/DirInOutputFiles/
directory of the solver input and
output files
../tutorial1/bin/
directory of the start scripts running
solver evaluations
../tutorial1/opti_problems/
directory of the problem
parameterization files
../tutorial1/Gradient_based_optimization_OPTGRAD/
workflow directory
../tutorial1/Gradient_based_optimization_OPTGRAD/Design_0001/
optiSLang creates design subdirectories
for every run, copies all parameterized
input files into that directory and starts
the external solver there
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Part 1&2: Introduction and Process integration
optiSLang file handling
• optiSLang will ask You to define the WorkflowIdentificator.
This name will be used by optiSLang when storing
result file [Save_WorkflowIdentificator_EA.bin]
replay file [Replay_WorkflowIdentificator_EA.bin]
• optiSLang will ask You to enter the name for the problem
parameterization file my_problem.pro (please define the
name of the problem file in the parametrize workflow, we
recommend to use *.pro extension)
• optiSLang will save algorithm settings from dialogs in .set files
• optiSLang writes an report file Report.htm (here all workflow
settings and problem definitions are reported)
• optiSLang writes an protocol file Protocol.txt where all data
operations are logged
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Part 1&2: Introduction and Process integration
How to connect the external solver?
•
•
•
•
optiSLang runs external CAE-processes via command line or script
optiSLang supports scripting via script writer flow
optiSLang will create design directories for all external solver runs
Using central solver control script (main flow)
• All input files including parameters will be copied to the
executing directory
• Additional input files have to be copied within the central script
• Within the script, all solvers and postprocessing/service
programs have to be called
• Specify which data shall be removed
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Part 1&2: Introduction and Process integration
Script Writer Flow
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Part 1&2: Introduction and Process integration
Distributed computing
Example unix shell script using ssh:
#!/bin/sh
thisDIR=$PWD
DESIGN=‘basename $PWD’
cd ..
tar czf "$DESIGN".tgz $DESIGN
scp "$DESIGN".tgz compute-server:/home/project
cd $thisDIR
ssh compute-server ‘cd /home/project;\
rm -rf ‘$DESIGN’;\
tar xzf ‘$DESIGN’.tgz;\
cd ‘$DESIGN’;\
cp /home/project/problem/*.inp .;\
cp /home/project/problem/target_values.txt .;\
ansys -b -i input_file.inp -o console.out;\
rm file.*;\
cd ..; rm ‘$DESIGN’.tgz;’
scp compute-server:/home/project/"$DESIGN"/objdat.txt .
cd ..
rm "$DESIGN".tgz
exit 0
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Part 1&2: Introduction and Process integration
Parametrize Editor
• optiSLang reads and writes parametric data to
and from ASCII
• Parameterize functionality
Input file:
• Optimization variable
• Robustness variable
• RDO variable
• Dependent variables
Output file:
• Response variable
• Response vector
• Signals
Problem definition section
• Optimization Constraints
• Robustness criteria
• Limit state function
• Multiple objectives/terms
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Part 1&2: Introduction and Process integration
Signals in optiSLang
• Motivation: numerous scripts were written for extraction,
processing and visualization of time or frequency signals
• Now signals are available in optiSLang (pre processor, solver, post
processor)
• Definition at parametrize editor (multiple channel signal
objects)
• Response parameters can be extracted via signal processing
• Response parameters and signals are available for post
processing
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Part 1&2: Introduction and Process integration
Success string definition
• Success string option
will check result files
for defined strings
• Success string handling is context sensitive:
• Gradient-based optimization: Stop when no success
• Evolutionary strategy: Stop if >= 50 % of generation fails
• DOE/Robustness analysis: no action, non-successful runs are
reported in report file and post processing
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Part 1&2: Introduction and Process integration
Dependent parameters
• optiSLang allows the definition of
dependencies between parameters
• Two types are supported:
 simple (functional) dependencies
 conditional (if-then) dependencies
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Part 1&2: Introduction and Process integration
Dependent variables
• optiSLang allows the definition of
free dependent (help) variables
• Two types are supported:
• simple (functional) dependencies
• conditional (if-then) dependencies
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Part 1&2: Introduction and Process integration
Restrictions
• Use C format declarations
• Use only formats which are successfully identified by the
parameterize editor
• Windows writes E-format with 3 Exponent characters !!!!
• Do not use Tabs in the ASCII files, optiSLang may fail to locate
the variable
• Do not use spaces (blancs), slashes and umlauts in names
• The name strings are limited to 32 characters
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Part 1&2: Introduction and Process integration
Is Your input/response parameter valid?
variable type
real
integer
string
continuous optimization variable
expected (possible)
not recommended
—
discrete optimization variable
possible
possible
possible
binary optimization variable
possible
possible
possible
stochastic variable with
continuous distribution type
expected (possible)
not recommended
—
stochastic variable with discrete
distribution type
possible
possible
possible
single response variable
expected (possible)
not recommended
—
response variable vector
expected (possible)
not recommended
—
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Part 1&2: Introduction and Process integration
Running Excel as solver
Running Excel as optiSLang solver
1.
2.
3.
4.
5.
Input and output parameters in marked lines
Import dynardo excel macro
Write ASCII input file
Modify and run Dynardo Jscript to generate output.txt
Parameterize ASCII input output with optiSLang
Excel Data Import
Excel plugin via
support@dynardo.de
Exporting Excel Data to optiSLang
1.
2.
3.
4.
5.
Install the Dynardo Excel plugin
Start plugin
Define inputs/outputs/design numbers
Write optiSLang binary (*.bin) or ASCII format (*.csv)
Post process the data with optiSLang
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Part 1&2: Introduction and Process integration
Optimal translation of scattered variables
- measurement of scattering variables can be easily imported and
optimal statistic translation (distribution function and correlation) can
be fitted using Excel and optiSLang
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Part 1&2: Introduction and Process integration
optiSLang Integration Environment
optiPlug
SoS - Statistics on Structure
ETK - Extraction Tool Kit
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Part 1&2: Introduction and Process integration
Process integration with ANSYS workbench & optiSLang
CAD / PDM
ANSYS Workbench
Structural Mechanics - Fluid Dynamics - Heat Transfer - Electromagnetics
An adaptable multi-physics design and analysis system that
integrates and coordinates different simulation tasks
Sensitivity
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Robustness
Optimization
Part 1&2: Introduction and Process integration
Reliability
Robust Design
optiPlug - ANSYS Workbench optiSLang Interface
Parameter
Manager
OptiSLang-Plugin:
just click to integrate
workbench in
optiSLang
Parameter &
Responses
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Part 1&2: Introduction and Process integration
optiPlug Export
• User has to choose/create the optiSLang project directory
• Automatic generation of
• Workbench input and
output files
• optiSLang problem
definition
• Workbench batch run
start scripts
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Part 1&2: Introduction and Process integration
optiPlug Procedure
• Optimization parameter and stochastic parameter definition is
realized within the WB parameter module
• Response values are defined within WB
• Workbench-addin generates optiSLang project with all necessary
ascii files (ascii-parameter and response sets, scripts for
automatic Workbench runs, default workflows)
• Completion of optimization/robustness problem with optiSLang
• Run the optimization/robustness workflow controlled by optiSLang
• Re-import of single designs in Workbench after
optimization/robustness evaluation
new Version optiPlug 3.0 for WB 12
• Update mechanism for existing optiSLang projects
• Default: workbench batch mode
• copy all workbench files into Design directory
• Parallel job distribution supported
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Part 1&2: Introduction and Process integration
Extraction Tool Kit (ETK)
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Part 1&2: Introduction and Process integration
Extraction Tool Kit (ETK)
•
Extraction toolkit to replace the
scripting for result extraction
and processing
•
GUI interface for extraction and
processing
Batch execution mode
Creates optiSLang *.pro file
•
•
•
•
•
•
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Full functional support of Abaqus
*.odb and ANSYS binary files
(RST, RTH,RMG, RFL)
Support of Adams XML format
Support of ASCII output for
MADYMO
Available on Windows/Linux
Part 1&2: Introduction and Process integration
Extraction Tool Kit (ETK)
•
•
35
Operations with scalar, vector
and signal objects
Definition of optiSLang
output parameters
Part 1&2: Introduction and Process integration
Extraction Tool Kit (ETK)
•
•
•
•
36
Output objects are written
in additional ASCII text file
Parametrization of the
outputs is done by ETK
Definition of objectives and
constraints has to be done
by hand
Integration of ETK in solver
batch script is necessary
Part 1&2: Introduction and Process integration
Plugins in ABAQUS
• Optiqus -Abaqus – Pro/E plug in
• Abaqus – Catia plug in
• creates a command script which can be
executed by the optimization program
• uses associative interfaces to update
the geometry in Abaqus/CAE
• creates Abaqus input files for the CAE
models
• Additional in Abaqus – Catia plugin (betaversion)
• uses Catia design table for input
parameters
• input parameters are automatically
parsed
• creates the basic structure for optiSLang
including runscript, and DoE workflow
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Part 1&2: Introduction and Process integration
CATIA optiSLang Interface
optiSLang plugin with export
feature
Generation of the
optiSLang project
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Part 1&2: Introduction and Process integration
Pre and Post Processing
• The Pre Processing
• Open architecture, user
friendly parametrize editor
and one click solution for
ANSYS workbench support
simulation flow setup
• Solving the RDO Task
• Easy and safe to use flows
with robust default settings
allows the engineer to
concentrate on his
engineering part and let
optiSLang do the job of
finding the optimal design.
• Post Processing
• The Interactive case
sensitive multi document
post processing offers the
important plots as default
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Part 1&2: Introduction and Process integration
Post Processing
• History of the
• Parameters
• Objectives
• Terms, objectives,..
• Histograms
• Anthill plots
• Correlation CoD/CoI
• Prognosis quality CoP
• Pareto Frontier
• Parallel Coordinate Plot
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Part 1&2: Introduction and Process integration
Post Processing and Data Extraction
• Design Table
• Structured table of active optiSLang design data
• Overview, parameter, responses, constraints, objectives
• Multiple export options
• Sorting
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Part 1&2: Introduction and Process integration
SoS – Statistics on Structures
The post processor for Statistics on finite element Structures
-
-
Statistic Measurements
- Single Designs
- Differences between
Designs
- Variation interval
- Minimum/Maximum
- Mean Value
- Standard deviation
- Coefficient of variation
- Quantile (± 3 σ)
Correlation & CoD
- Linear correlation & CoD
- At nodal/element level
Process quality criteria Cp,
Cpk process indices
Random field generation
- Scatter shape extraction
and visualisation
42
[Will, J.; Bucher, C.;
Ganser, M.; Grossenbacher, K.: Berechnung und
Visualisierung statistischer Maße auf FE-Strukturen für
Umformsimulationen; Proceedings Weimarer
Optimierung- und Stochastiktage 2.0, 2005]
Part 1&2: Introduction and Process integration