1. Part 1 Introduction to optiSLang

2. 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
There is no revolution going on in virtual prototyping, but an evolution.
After introducing the optimization methodology, robustness and reliability methodology as well as combinations with optimization will be next.
Part 1&2: Introduction and Process integration

3. 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, non-smooth)
Main point is that multi disciplinary problems will have often single disciplines which are not suitable for gradient based algorithms. Therefore, genetic/evolutionary algorithms or sensitivity studies, followed with ARSM in reduced dimensions, will be very often the method of choice.
Part 1&2: Introduction and Process integration

4. 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
Every design process is taking care of uncertainties or stochasticity of input parameters. Designers are using global or partial safety factors. To ensure robustness/reliability, the safety factor or the safety distance has to be large. This results in over design and over engineering.
But the market pressure today is pushing designs to the borderline and safety distances have to be verified within the virtual product development process. This could only be done with stochastic analysis.
Therefore, the introduction of stochastic analysis is an evolutionary process (a necessary consequence) after the virtual product development for optimal designs is installed.
Part 1&2: Introduction and Process integration

5. 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.
Part 1&2: Introduction and Process integration

6. 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!
Part 1&2: Introduction and Process integration

7. Robust Design Methodology Definition
Start
Robust Design Optimization
Robust Design
Variance based Robustness Evaluation
Probability based Robustness Evaluation, (Reliability analysis)
Optimization
Sensitivity Study
Single & Multi objective
(Pareto) optimization
Very important is to explain that the reliability space is given by nature (scatter at loads, material, geometry & design variables) and the optimization space (design variables) is defined by the designer. For practical applications, the spaces may overlap, but they will never be the same.
Optimization can be treated as a black box. After an optimization, the user can judge easily by looking at the results of the optima for the success of the optimization. If an important design variable was missed, the design improvement may be pure, but still valid.
Very much in contrast to the optimization, the robustness evaluation produces statistical measurements which can only be verified with another stochastic analysis or massive testing. Again, very much in contrast to the optimization, if one important input scatter for the virtual tests (stochastic analysis) is missing, all statistical measurements are useless.
Therefore, the user really has to know something about the input scatter, has to understand the reliability domain and has to use stochastic methodology which produces reliable measurements of robustness/reliability. We strongly recommend to start with variance-based robustness evaluation.
CAE process (FEM, CFD, MBD, Excel, Matlab, etc.)
Part 1&2: Introduction and Process integration

8. Part 2 Process Integration

9. 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
Result variables:
measures from the system
The CAE process generates the results according to the inputs
The external CAE-process has to be parametric, that means that the process can be repeated with varying design vectors. Therefore, oS is not adding the feature of being parametric, it can use any kind of parametricity.
Scattering variables:
Entities that define the robustness space
Part 1&2: Introduction and Process integration

10. 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.
Connected CAE-Solver: ANSYS, ABAQUS, NASTRAN, LS-DYNA, PERMAS, Fluent, CFX, Star-CD, MADYMO, SLang, Matlab, Excel,…
Available interfaces in optiSLang
CATIA v5 interface
ANSYS workbench interface
Excel Plugin
Extraction tool kit (ABAQUS, LS-DYNA)
Madymo positioner
The CATIA interface is still beta.
ANSYS interface is very good.
The extraction tool kit was set up for our customer BOSCH and is now available for all users.
MADYMO positioner is important for passive safety applications for robustness evaluation.
Part 1&2: Introduction and Process integration

11. 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
Part 1&2: Introduction and Process integration

12. 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
Part 1&2: Introduction and Process integration

13. 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
Part 1&2: Introduction and Process integration

14. 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
Part 1&2: Introduction and Process integration

15. 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
Part 1&2: Introduction and Process integration

16. Script Writer Flow
Part 1&2: Introduction and Process integration

17. 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 .
rm "$DESIGN".tgz
exit 0
Part 1&2: Introduction and Process integration

18. 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
The parameter editor is visualizing the data flow of all variables and responses as well the constraints, objectives and limit state functions. The user friendliness of the editor is good. The parameter editor writes the oS problem file in XML-format. If we have a very large number of parameters or responses we are usually writing a script to generate the XML file.
That is a nice flexibility of oS, that the whole problem definition can be written/edited with any external program/script.
Part 1&2: Introduction and Process integration

19. 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
Part 1&2: Introduction and Process integration

20. 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
Part 1&2: Introduction and Process integration

21. Dependent parameters
optiSLang allows the definition of dependencies between parameters
Two types are supported:
simple (functional) dependencies
conditional (if-then) dependencies
Part 1&2: Introduction and Process integration

22. Dependent variables
optiSLang allows the definition of free dependent (help) variables
Two types are supported:
simple (functional) dependencies
conditional (if-then) dependencies
Part 1&2: Introduction and Process integration

23. 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
Part 1&2: Introduction and Process integration

24. Is Your input/response parameter valid?
variable type
real
integer
string
continuous optimization variable
expected (possible)
not recommended —
discrete optimization variable
possible
binary optimization variable
stochastic variable with continuous distribution type
stochastic variable with discrete distribution type
single response variable
response variable vector
Part 1&2: Introduction and Process integration

25. Running Excel as solver
Running Excel as optiSLang solver
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

26. Exporting Excel Data to optiSLang
Excel Data Import
Excel plugin via
Exporting Excel Data to optiSLang
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
Part 1&2: Introduction and Process integration

27. 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
Part 1&2: Introduction and Process integration

28. optiSLang Integration Environment
optiPlug
SoS - Statistics on Structure
ETK - Extraction Tool Kit
Part 1&2: Introduction and Process integration

29. CAD / PDM ANSYS Workbench
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
Robustness
Optimization
Reliability
Robust Design
Part 1&2: Introduction and Process integration

30. optiPlug - ANSYS Workbench optiSLang Interface
OptiSLang-Plugin:
just click to integrate workbench in optiSLang
Parameter Manager
Parameter & Responses
Part 1&2: Introduction and Process integration

31. 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
Part 1&2: Introduction and Process integration

32. 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
Nutzer – Interaktion: Completion of optimization/robustness problem with optiSLang
Part 1&2: Introduction and Process integration

33. Extraction Tool Kit (ETK)
Originally, the tool was developed for ABAQUS applications at BOSCH reading binary format *.odb.
Therefore, the extraction tool kit is very powerful for all optiSLang-ABAQUS applications.
Today, we also support PERMAS, LS-DYNA and NASTRAN ASCII-based output files.
Part 1&2: Introduction and Process integration

34. 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
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
Originally, the tool was developed for ABAQUS applications at BOSCH reading binary format *.odb.
Therefore, the extraction tool kit is very powerful for all optiSLang-ABAQUS applications.
Today, we also support PERMAS, LS-DYNA and NASTRAN ASCII-based output files.
Part 1&2: Introduction and Process integration

35. Extraction Tool Kit (ETK)
Originally, the tool was developed for ABAQUS applications at BOSCH reading binary format *.odb.
Therefore, the extraction tool kit is very powerful for all optiSLang-ABAQUS applications.
Today, we also support PERMAS, LS-DYNA and NASTRAN ASCII-based output files.
Operations with scalar, vector and signal objects
Definition of optiSLang output parameters
Part 1&2: Introduction and Process integration

36. Extraction Tool Kit (ETK)
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
Originally, the tool was developed for ABAQUS applications at BOSCH reading binary format *.odb.
Therefore, the extraction tool kit is very powerful for all optiSLang-ABAQUS applications.
Today, we also support PERMAS, LS-DYNA and NASTRAN ASCII-based output files.
Part 1&2: Introduction and Process integration

37. 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 (beta-version)
uses Catia design table for input parameters
input parameters are automatically parsed
creates the basic structure for optiSLang including runscript, and DoE workflow
Part 1&2: Introduction and Process integration

38. CATIA optiSLang Interface
optiSLang plug-in with export feature
We have a beta version of the interface for CATIA V5 R14
The interface is using scripting language in CATIA, the user has to define some special things in CATIA: optiSLang Parameter name, directory,..
If you have customer, please ask us for the status of the interface
Generation of the
optiSLang project
Part 1&2: Introduction and Process integration

39. 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
The post processing of oS follows the concept of showing the most important results via an interactive mode.
Part 1&2: Introduction and Process integration

40. Post Processing History of the Parameters Objectives
Terms, objectives,..
Histograms
Anthill plots
Correlation CoD/CoI
Prognosis quality CoP
Pareto Frontier
Parallel Coordinate Plot
The post processing of oS follows the concept of showing the most important results via an interactive mode.
Part 1&2: Introduction and Process integration

41. Post Processing and Data Extraction
Design Table
Structured table of active optiSLang design data
Overview, parameter, responses, constraints, objectives
Multiple export options
Sorting
The post processing of oS follows the concept of showing the most important results via an interactive mode.
Part 1&2: Introduction and Process integration

42. 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
[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