Part 1 Introduction to optiSLang. 2 Part 1&2: Introduction and Process integration Virtual prototyping is necessary for cost efficiency Test cycles are

Part 1 Introduction to optiSLang

2 Part 1&2: Introduction and Process integration

• 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

Challenges in Virtual Prototyping


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)


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


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 betweenKnow-how of uncertainties, stochastic methodology and statistic post processing is the success key.

DYNARDO and optiSLang are technology leaders.


Excellence of optiSLangoptiSLang 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!


Start

CAE process (FEM, CFD, MBD, Excel, Matlab, etc.)

Robust Design Optimization

Optimization

Sensitivity Study

Single & Multi objective (Pareto) optimization

Robust Design

Variance based Robustness Evaluation

Probability based Robustness Evaluation,

(Reliability analysis)

Robust Design Methodology Definition

Part 2 Process Integration


Process Integration

Design variables:Entities that define the design space

Result variables:measures from the system

The CAE process generates the results according to the inputs

Scattering variables:Entities that define the robustness space

Parametric modeling as base for • Customer defined optimization design space• Naturally given robustness/reliability space


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 interfaceANSYS workbench interfaceExcel PluginExtraction tool kit

(ABAQUS, LS-DYNA)Madymo positioner

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

optiSLang Process Integration


• 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

Workflow name and identificator


../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

optiSLang directory 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 problemparameterization file my_problem.pro (please define thename of the problem file in the parametrize workflow, werecommend 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

optiSLang file handling


• 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

How to connect the external solver?


Script Writer Flow


Distributed computingExample unix shell script using ssh:

#!/bin/shthisDIR=$PWDDESIGN=‘basename $PWD’cd ..tar czf "$DESIGN".tgz $DESIGNscp "$DESIGN".tgz compute-server:/home/projectcd $thisDIRssh compute-server ‘cd /home/project;\rm -rf ‘$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".tgzexit 0


Parametrize Editor• optiSLang reads and writes parametric data to

and from ASCII• Parameterize functionalityInput file:• Optimization variable • Robustness variable• RDO variable• Dependent variables Output file:• Response variable • Response vector• SignalsProblem definition section• Optimization Constraints• Robustness criteria• Limit state function• Multiple objectives/terms


• 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

Signals in optiSLang


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


Dependent parameters

• optiSLang allows the definition of dependencies between parameters

• Two types are supported: simple (functional) dependencies conditional (if-then) dependencies


Dependent variables

• optiSLang allows the definition of free dependent (help) variables

• Two types are supported:• simple (functional) dependencies• conditional (if-then) dependencies


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


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 —

Running Excel as optiSLang solver1. Input and output parameters in marked lines 2. Import dynardo excel macro3. Write ASCII input file4. Modify and run Dynardo Jscript to generate output.txt5. Parameterize ASCII input output with optiSLang

Running Excel as solver


Excel Data Import

Exporting Excel Data to optiSLang 1. Install the Dynardo Excel plugin 2. Start plugin3. Define inputs/outputs/design numbers4. Write optiSLang binary (*.bin) or ASCII format (*.csv)5. Post process the data with optiSLang

Excel plugin via [email protected]


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


optiPlugSoS - Statistics on StructureETK - Extraction Tool Kit

optiSLang Integration Environment


Process integration with ANSYS workbench & optiSLang

ANSYS WorkbenchStructural Mechanics - Fluid Dynamics - Heat Transfer - Electromagnetics

An adaptable multi-physics design and analysis system that integrates and coordinates different simulation tasks

CAD / PDMCAD / PDM

Sensitivity Robustness Optimization Robust DesignReliability


Parameter Manager

Parameter & Responses

optiPlug - ANSYS Workbench optiSLang Interface

OptiSLang-Plugin:

just click to integrate workbench in

optiSLang


optiPlug Export

• Automatic generation of • Workbench input and

output files• optiSLang problem

definition• Workbench batch

run start scripts

• User has to choose/create the optiSLang project directory


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


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



• Operations with scalar, vector and signal objects

• Definition of optiSLang output parameters



• 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



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


CATIA optiSLang Interface

Generation of the optiSLang project

optiSLang plug-in with export

feature


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


Post Processing

• History of the • Parameters • Objectives• Terms, objectives,..

• Histograms• Anthill plots• Correlation CoD/CoI• Prognosis quality CoP• Pareto Frontier• Parallel Coordinate Plot


Post Processing and Data Extraction• Design Table

• Structured table of active optiSLang design data• Overview, parameter, responses, constraints, objectives• Multiple export options• Sorting


- 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

SoS – Statistics on Structures

[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]

The post processor for Statistics on finite element Structures

Documents

Part 1 Introduction to optiSLang. 2 Part 1&2: Introduction and Process integration Virtual prototyping is necessary for cost efficiency Test cycles are