13 Dynardo AT.Pdf

Total Page:16

File Type:pdf, Size:1020Kb

13 Dynardo AT.Pdf Workflows zur Systemanalyse und Optimierung in ANSYS anhand der Auslegung eines Elektromotors ----------------------------------------------------------------- Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang M. Schimmelpfennig, Dynardo GmbH ACUM 2016 Linz 2 © Dynardo GmbH Dynardo • Founded: 2001 • More than 60 employees, offices at Weimar and Vienna • Leading technology companies Daimler, Bosch, E.ON, Nokia, Siemens, BMW are supported Software Development CAE-Consulting • Mechanical engineering • Civil engineering & Geomechanics Dynardo is engineering specialist for • Automotive industry CAE-based sensitivity analysis, • Consumer goods industry optimization, robustness evaluation • Power generation and robust design optimization Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 3 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH Master of Design Robust Design Optimization (RDO) in virtual product development Our customized FE-consulting and software products enable you to: • Quantify risks • Identify optimization potentials • Perform variant studies • Secure resource efficiency • Ensure product quality • Improve product performance • Save time to market Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 4 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH optiSLang • is an general purpose tool for variation analysis using CAE-based design sets (and/or data sets) for the purpose of • sensitivity analysis • design/data exploration • calibration of virtual models to tests • optimization of product performance • quantification of product robustness and product reliability • Robust Design Optimization (RDO) and Design for Six Sigma (DFSS) serves arbitrary CAX tools with support of process integration, process automation and workflow generation Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 5 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH Design Understanding Investigate parameter sensitivities, Design Improvement reduce complexity and Optimize design performance generate best possible meta models CAE-Data Robust Design Measurement Data Model Calibrations Design Quality Identify important model parameter Ensure design robustness for the best fit between simulation and reliability and measurement Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 6 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH SPDM Input Workflow-Management Output 1 1 with Process Integration Input and for Output 2 Automatization 2 Input Output n m Excel Add-In ANSYS optiSLang other Solver Postprocessing Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 7 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH optiSLang as an ANSYS Workbench plugin • optiSLang modules Sensitivity + MOP, Optimization and Robustness are directly available in ANSYS Workbench Signal Processing module to work with curves inside ANSYS Workbench Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 8 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH CAX-Interfaces – the ANSYS Workbench Node • optiSLang Integrations provides the flexibility to extend the process chain • ANSYS Workbench can be coupled with different other solvers like MATLAB, SimulationX or Abaqus • External geometry or mesh generators can work together with the ANSYS Workbench node Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 9 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH Optimization of an electric motor Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 10 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH Optimization of an electric motor The motor simulation • ANSYS Maxwell 2D model • commutator principle Sensitivity analysis with optiSLang • problem understanding • identification of influential parameters • identification of tradeoffs Optimization with optiSLang • minimization of torque ripples • maximization of the efficiency “eta” η = Pout/Pin • suitable in this case: ARSM – adaptive response surface method Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 11 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH Optimization of an electric motor Commutator motor: working principle motor characteristics What creates the driving torque? • commutator principle • 12 lamellae & coils B-field from • one current branch magnets U0 = 12 V B-field from • fixed outer diameter coils OD = 78 mm https://commons.wikimedia.org/w iki/File:Kommutator_animiert.gif Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 12 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH Optimization of an electric motor The model: 2D commutator motor FE-simulation simulation details • time: 16.67 ms in 180 steps Δt = 92.6 μs • time integration: Backward Euler • ensure that stationary state is reached (not all designs will become stationary at the same time) data extraction: • key properties extracted by analyzing only the last cycle • access to output variables via Ansys Workbench ParameterSet • access to signals via Ansys Workbench or ASCII files Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 13 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH Model parametrization Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 14 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH Model parametrisation magnet_coverage: magnet coverage in percent magnet_thickness airgap gapwidth magnet_rounding: as fraction of magnet thickness rotor_borehole: diameter of motor axis HS0 wall_thickness magnet_voffset: for widening of air gap Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 15 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH Optimization of an electric motor Parametrization needs careful decisions example A: example B (used): • set rotor diameter • set motor size • set magnet thickness • set magnet thickness motor size dependent rotor size dependent the bigger the better magnet takes away (in terms of torque & power) space available for rotor and vice versa lost chance to learn real-world goal about a relevant conflict well tradeoff represented Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 16 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 17 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH Calling Maxwell from optiSLang Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 18 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH Optimization of an electric motor Method A: using Ansys Workbench in Maxwell • export scalar output variables to Optimetrics • parallel design computation with Optimetrics Parametric in the Workbench • optiSLang and Maxwell communicate through the ParameterSet parallel/distributed computation: • RSM, PBS, LSF, HPC pack • use “optiSLang inside Ansys” alternative: • optiSLang full version and a “Workbench node” Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 19 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH Optimization of an electric motor Method A: using Ansys Workbench in Maxwell • export scalar output variables to Optimetrics • parallel design computation with Optimetrics Parametric in the Workbench • optiSLang and Maxwell communicate represented inside through the ParameterSet the Workbench node parallel/distributed computation: • RSM, PBS, LSF, HPC pack • use “optiSLang inside Ansys” alternative: • optiSLang full version and a “Workbench node” Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 20 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH Optimization of an electric motor Method B: scripting and ASCII files direct coupling Maxwell and oSL Maxwell • run batch job • run Python script text file for • write transient reports into files transporting input signal data accessible parameters optiSLang • text-based batch job node • extract signal data with ETK • signal data free mathematical computations inside optiSLang parallel/distributed computation: • optiSLang spawns Maxwell batch jobs Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 21 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH Optimization of an electric motor Method B: scripting and ASCII files direct coupling Maxwell and oSL Maxwell • run batch job • run Python script the batch script • write transient reports into files signal data accessible optiSLang • text-based batch job node • extract signal data with ETK • signal data free mathematical Python computations inside optiSLang parallel/distributed computation: • optiSLang spawns Maxwell batch jobs Workflows for analysis and optimization of an electric motor with ANSYS and optiSLang 22 M. Schimmelpfennig - Dynardo GmbH Linz 2016 © Dynardo GmbH Optimization of an electric motor Method B: scripting and ASCII files direct coupling Maxwell and oSL Maxwell • run batch job • run Python script reading generated • write transient reports into files stored data signal data accessible
Recommended publications
  • Robustness & Reliability Analysis
    DYNARDO • Robust Design Optimization Workshop • © Dynardo GmbH 2011 Robustness & Reliability Analysis 1 DYNARDO • Robust Design Optimization Workshop • © Dynardo GmbH 2011 Start Robust Design Optimization Robust Design Optimization Variance based Robustness Evaluation Sensitivity Study Probability based Single & Multi objective Robustness Evaluation, (Pareto) optimization (Reliability analysis) CAE process (FEM, CFD, MBD, Excel, Matlab, etc.) Robust Design Optimization Methodology 2 DYNARDO • Robust Design Optimization Workshop • © Dynardo GmbH 2011 What is necessary for successful implementation? 1. Introduction of realistic scatter definitions Distribution function Correlations Random fields 2. Using of reliable stochastic methodology Variance-based robustness evaluation using optimized LHS 3. Development of reliable robustness measurements Standardized post processing Significance filter Reliable variation and correlation measurements Acceptance of method/result documentation/communication! 3 DYNARDO • Robust Design Optimization Workshop • © Dynardo GmbH 2011 Definition of Uncertainties 4 DYNARDO • Robust Design Optimization Workshop • © Dynardo GmbH 2011 Uncertainties and Tolerances • Design variables Property SD/Mean % • Material, geometry, loads, constrains,… Metallic materiales, yield 15 • Manufacturing Carbon fiber rupture 17 • Operating processes (misuse) Metallic shells, buckling 14 strength • Resulting from Deterioration Bond insert, axial load 12 • … Honeycomb, tension 16 Honeycomb, shear, compression 10 Honeycomb, face
    [Show full text]
  • Optislang Connects
    Title Story // Process Integration Process Integration ABAQUS | ADAMS | COMSOL | EXCEL | FLOEFD | GT POWER | MATLAB | LS DYNA MOTOR CAD | NASTRAN | PYTHON | ROCKY | SIEMENS NX | ZEMAX ... Product Data Product Statistical Connect PLM/SDM Connect PLM/SDM Analysis TITLE STORY // PROCESS INTEGRATION Parametric Variation Analysis Calibration Sensitivity Analysis Optimization OPTISLANG CONNECTS Robustness Evaluation Metamodeling Data-based ROMs optiSLang enables the building of transparent CAE tool chains and combines them with parametric algorithms for a broader usage of CAE techniques. Fig. 1: Simulation Workfl ow Management with optiSLang One of the major challenges of today’s CAE engineers is the gineers. Also, customers are aware of how many mistakes includes interfaces to all major software tools used in the maintain complex tool chains. The modular process inte- increasing complexity of processes while results have to might occur during this process. When different variants virtual product development. For example, there are inte- gration approach of building programmable nodes provides be delivered in shorter times. At the same time, within the have to be compared for fi nding an optimal design, it can grations of CAD parametric modeling with CATIA, Siemens a very economical way to standardize and automatize CAE engineering process, multiple disciplines like NVH, thermal- lead to an unsorted crowd of data. If simulations are done NX and ProE. CAE parametric modeling is supported by link- workfl ows. There are many examples of successful projects mechanical-electrical analyses, safety evaluation, tolerance by different engineers, it can be very hard to compare the ing ANSYS, ABAQUS, ADAMS, COMSOL, FLOEFD, GT POWER, with Dynardo’s customers like Bosch, Daimler, et al.
    [Show full text]
  • Dynardo Gmbh
    dynamic software & engineering Software & Consulting Sensitivity Analysis Multidisciplinary Optimization Multiobjective Optimization Robustness Evaluation Reliability Analysis Parameter Identifi cation Model Calibration Dynardo GmbH optiSLang SoS ETK LEONARDO DA VINCI dynamic software & engineering Without reason, no eff ect is produced in nature; understand the reason and you will not need experience. from Leonardo da Vinci’s „Codice Atlantico“ DYNARDO GMBH The Dynardo GmbH develops software for CAE-based sensitivity analyses, robustness evaluations and Robust Design Optimizations. Additionally Dynardo offers, in cooperation with worldwide distributors, an extensive support and training program. The range of services also includes consulting and simula- Tasks, tion services in virtual product development. Ideas, Solutions History & Expertise The Dynardo GmbH was founded in 2001 as a company for Dynardo’s engineering expertise and software products software development and engineering computing services enable fast and effi cient solutions of your tasks. Through with three partners and two employees in Weimar. With close cooperation with renowned research institutions and unique features such as complex non-linear fi nite element international partners, Dynardo has an extensive network computations in geomechanics and civil engineering as well of experts to respond fl exibly to worldwide customer re- as RDO in virtual product development, Dynardo has suc- quirements. cessfully established itself in the CAE-market. The fi rst op- tiSLang version was released in 2002 and has since evolved into a leading CAE software platform. Today, Dynardo runs 3 branches in Weimar, Vienna and San Francisco to help you meet the challenges of virtual product development. Philosophy Leonardo da Vinci was not only a great artist, but also a tion enables an appropriate analysis of system properties for visionary engineer and scientist.
    [Show full text]
  • •Full Support Limited Capability Requires More Than 1 Product
    • Full Support ▲ Limited Capability ▪ Requires more than 1 product / CONTENTS STRUCTURES FLUIDS Vibrations .................................................................................................................... 3 General Solver Capabilities ........................................................................22 Webservice ..............................................................................................................33 High Frequency Electromagnetics ....................................................44 Wave Hydrodynamics ...................................................................................... 3 Single Phase, Non-Reacting Flows ......................................................22 General .......................................................................................................................34 Power and Signal Integrity Board Additional Physics ................................................................................................ 3 Heat Transfer ..........................................................................................................23 Designing Optimization and Parameter Simulation Capabilities ................................................................................. 47 Composite Materials ......................................................................................... 5 Particle Flows (Multiphase) ........................................................................23 Identificaton (Calibration) ..........................................................................34
    [Show full text]
  • Sensitivity Analysis, • Consumer Goods Industry Optimization, Robustness Evaluation • Power Generation and Robust Design Optimization
    DYNARDO • © Dynardo GmbH 2012 Founded: 2001 (Will, Bucher, CADFEM International) More than 40 employees, offices at Weimar and Vienna Leading technology companies Daimler, Bosch, Eon, Nokia, Siemens, BMW, are supported by us Software Development CAE-Consulting Our expertise: •Mechanical engineering • Civil engineering & Geomechanics • Automotive industry Dynardo is your engineering specialist for CAE-based sensitivity analysis, • Consumer goods industry optimization, robustness evaluation • Power generation and robust design optimization. 2 DYNARDO • © Dynardo GmbH 2012 Premium Consultancy and Software Company for CAE-based Robustness Evaluation, Reliability Analysis and Robust Design Optimization using Stochastic Analysis Dynardo is the consulting company which successfully introduced stochastic analysis into complex CAE-based virtual product development processes. Recently, it is applied in the power generation industry, automotive industry and high-level consumer goods industry DYNARDO Field of Excellence 3 DYNARDO • © Dynardo GmbH 2012 Introduction of optiSLang 4 DYNARDO • © Dynardo GmbH 2012 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 5 DYNARDO • © Dynardo GmbH 2012 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.
    [Show full text]
  • Daniel Krätschmer Friederike Loerke
    VIRTUAL ECU ANSYS BEST PRACTICE SESSION: „SIMULATION DRIVEN PRODUCT DEVELOPMENT FOR EVERYONE“ DANIEL KRÄTSCHMER FRIEDERIKE LOERKE CASCON 2019 Simulation Process Management: Virtual ECU Motivation System: How to establish a robust data / information Vehicle exchange between product-independent design Load derivation from a system perspective 0 Level element assessments & product-specific load Sub-System: RB system derivation and load path design? (e.g. injection Level 1 Level system) Manufactured Motivation Item: Only with standardized load path methods Level 2 Level ECU transparent requirements for design elements can be derived ? Assembly Level 3 Level Standardized load path methods as enabler for Cut out design element load / load tool based, automatized evaluation of design for product integration capacity level Component elements in mechanical architectures Reliability Assessments 4 Level geometry-independent stress / strength Material, design rule, e.g. 휎 ≤ 휎푚푎푥 level Level 5 Level Interface 4 Automotive Electronics | AE/EBS | 2019-08-07 © Robert Bosch GmbH 2017.2019. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights. Simulation Process Management: Virtual ECU Motivation System: Democratization in CAE-based product Vehicle Load derivation from a system perspective 0 Level development means to empower non- Sub-System: experts to take advantage from simulation RB system technology (e.g. injection Level 1 Level system) CAE experts could put the power of Automated setup of ECU models to feed Manufactured Item: simulation safely into non-experts hands to reliability models with needed information Level 2 Level ECU overcome the general lack of CAE engineers Assembly Level 3 Level Cut out design element load / load for product integration capacity level Democratization ? Component Hosting of detailed reliability models 4 Level geometry-independent stress / strength Material, design rule, e.g.
    [Show full text]
  • OPTISLANG - READY for DIGITAL TWIN to BRIDGE Fig
    Title Story // Process Integration & Automation DOE Virtual Product manuell HiL Calibration Simulation = Hardware Connect PLM Digital Twin Connect PLM autom. complex CAx workflow Laboratory Development parametric studies, RDO, … CAx rd party Product CAE-Data 3 Production Statistical Database ROM TITLE STORY // PROCESS INTEGRATION & AUTOMATION Analysis OPTISLANG - READY FOR DIGITAL TWIN TO BRIDGE Fig. 1: Integration of optiSLang within and beyond CAx Processes, Product Lifecycle Management and Hardware-in-Loop INTO PRODUCTION 4.0 & INTERNET OF THINGS optiSLang is capable to bridge the gap between modern Product Lifecycle Management (PLM) systems and individual product development hubs. Motivation The new generation of process automation within Industry should be noted, however, that simulation technology is 4.0 has transformed the scope of Business Process Models not an IoT product itself but is proving to be a pivotal driver (BPM) for leading global manufacturers. With tools such as of IoT processes in the future. Virtual Manufacturing Execution Systems, Cloud Computing and Big Data Analytics, the Internet of Things (IoT) is becom- In the various phases of Product Development Processes ing a modern and efficient umbrella of planning, processing, (PDPs), optiSLang is capable to play a vital role to shorten pro- feedback and control systems, paving the way for the next duction cycles, deliver enhancements and resolve conflicts generation of smart designs and manufacturing systems. with respect to quality, product robustness and costs. Apart from optimizing individual process workflows within various Within the scope of Industry 4.0, there has been a wide- stages of PDPs, optiSLang could be used to bridge the gap be- CAx 3rd party spread development of standalone tools, each offering tween modern Product Lifecycle Management (PLM) systems special solutions to underlying problems and requiring and individual product development hubs.
    [Show full text]
  • Sensitivity Analysis, • Consumer Goods Industry Optimization, Robustness Evaluation • Power Generation and Robust Design Optimization
    DYNARDO • Robust Design Optimization Workshop • © Dynardo GmbH 2011 Founded: 2001 (Will, Bucher, CADFEM International) More than 35 employees, offices at Weimar and Vienna Leading technology companies Daimler, Bosch, Eon, Nokia, Siemens, BMW, are supported by us Software Development CAE-Consulting Our expertise: • Mechanical engineering • Civil engineering & Geomechanics • Automotive industry Dynardo is your engineering specialist for CAE-based sensitivity analysis, • Consumer goods industry optimization, robustness evaluation • Power generation and robust design optimization. 2 DYNARDO • Robust Design Optimization Workshop • © Dynardo GmbH 2011 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 3 DYNARDO • Robust Design Optimization Workshop • © Dynardo GmbH 2011 Challenge of RDO – reliable input Successful RDO needs a balance between – Reliable input = know how and definition of input uncertainties – Reliable analysis = reliable stochastic analysis methodology – Reliable post processing = use of stochastic/statistic results in the design process Let’s derive the functionality
    [Show full text]
  • ANSYS Optislang Brochure
    dynamic software & engineering CAE-Software Sensitivity analysis Multiobjective optimization Multidisciplinary optimization Robustness evaluation Reliability analysis Robust Design Optimization (RDO) optiSLang optiSLang multiPlas ETK SoS ANSYS optiSLang ANSYS optiSLang Combining powerful parametric model capabilities with Robust Design Optimization ANSYS Workbench provides leading technology for parametric and persistent CAD and CAE modelling for simulation driven product development. optiSLang focuses on effi ciency and automation of RDO methods for complex non-linear analysis models with many parameters, including stochastic variables. ANSYS Workbench project screen with the three optiSLang drag and drop modules sensitivity analysis, optimization and robustness evaluation to defi ne an RDO analysis. This includes robust handling of design failures and solver noise. ANSYS optiSLang offers several modes of interoperability • using parametric ANSYS Workbench models with the In addition ANSYS optiSLang provides eral cores with ANSYS Remote Solve Manager and the use • using the optiSLang Workbench plugin to expose optiSLang GUI with the help of the optiSLang ANSYS • Flexible text-based interfacing tools that can connect of ANSYS HPC Pack Parametric Licenses for simultaneous optiSLang technology within the ANSYS Workbench GUI Workbench integration node ANSYS products, or any scriptable products, that may be computing of different designs are fully integrated. With • using text-based interfacing between ANSYS and optiS- part of your engineering
    [Show full text]
  • Introduction to Parametric Optimization and Robustness Evaluation with Optislang
    Introduction to Parametric Optimization and Robustness Evaluation with optiSLang Dynardo GmbH 1 © Dynardo GmbH 1. Introduction 2. Process to optiSLang integration 6. Training 3. Sensitivity analysis 5. Robustness 4. Parametric analysis Optimization Introduction to the parametric optimization and robustness evaluation with 2 optiSLang © Dynardo GmbH 1. Introduction 2. Process to optiSLang integration 6. Training 3. Sensitivity analysis 5. Robustness 4. Parametric analysis Optimization Introduction to the parametric optimization and robustness evaluation with 3 optiSLang © Dynardo GmbH Dynardo • Founded: 2001 (Will, Bucher, CADFEM International) • More than 50 employees, offices at Weimar and Vienna • Leading technology companies Daimler, Bosch, E.ON, Nokia, Siemens, BMW are supported Software Development CAE-Consulting • Mechanical engineering • Civil engineering & Dynardo is engineering specialist for Geomechanics CAE-based sensitivity analysis, • Automotive industry optimization, robustness evaluation • Consumer goods industry and robust design optimization • Power generation Introduction to the parametric optimization and robustness evaluation with 4 optiSLang © Dynardo GmbH Robust Design Optimization (RDO) in virtual product development optiSLang enables you to: • Identify optimization potentials • Improve product performance • Secure resource efficiency • Adjust safety margins without limitation of input parameters • Quantify risks • Save time to market Introduction to the parametric optimization and robustness evaluation with 5 optiSLang
    [Show full text]
  • Robustness & Reliability Analysis
    DYNARDO • © Dynardo GmbH 2013 Robustness & Reliability Analysis 2 DYNARDO • © Dynardo GmbH 2013 Start Robust Design Optimization Robust Design Optimization Variance based Robustness Evaluation Sensitivity Study Probability based Single & Multi objective Robustness Evaluation, (Pareto) optimization (Reliability analysis) CAE process (FEM, CFD, MBD, Excel, Matlab, etc.) Robust Design Optimization Methodology 3 DYNARDO • © Dynardo GmbH 2013 How to define Robustness of a Design • Intuitively: The performance of a robust design is largely unaffected by random perturbations • Using Variance indicator: The coefficient of variation (CV) of the response, constraint, objective function values is smaller than the CV of the input variables • Using Sigma level: The interval mean+/- sigma level does not reach an undesired performance (e.g. design for six-sigma) • Using Probability indicator: The probability of reaching undesired performance is smaller than an acceptable value 4 DYNARDO • © Dynardo GmbH 2013 Robustness Evaluation / Reliability Analysis • Variance-based robustness evaluation usually estimate product – safety and reliability for 1 & 2 (3) Sigma levels and identifies the most sensitive stochastic variables – high number of stochastic variables is no problem • Probability-based robustness evaluation (reliability analysis) estimate product – safety and reliability for rare events (3,4,5,6-Sigma) with small number of variables – Most reliability algorithms suffer on high number of stochastic variables 5 DYNARDO • © Dynardo GmbH 2013 Successful
    [Show full text]
  • Product Brochure Optislang
    dynamic software & engineering CAE-Software Model Calibration Sensitivity Analysis Optimization Robustness Evaluation Robust Design Optimization Metamodeling optiSLang Product Overview PRODUCT OVERVIEW Since its market launch in 2002, optiSLang has developed into one of the leading universal software platforms for CAE-based optimization in virtual prototyping. Based on design variations or measurement and observation points, effi cient variation analyses can be performed with minimal user input and few Typical workfl ow using sensitivity analysis, optimization, meta-models and validation of the best design solver calls. optiSLang supports the engineer with: Metamodeling with optiSLang • Calibration of virtual models to physical tests signs to the boundaries of tolerable stresses, deformations optiSLang’s expands parametric design studies to impact of input variability to the responses. For the identifi ca- • Analysis of parameter sensitivity and importance or other critical responses. As a result, the product behavior metamodeling based on a set of virtual design points, experi- tion process, optiSLang’s Coeffi cient of Prognosis (CoP) quanti- • Metamodeling may become sensitive to scatter with regard to material, mental design responses or fi eld observations. The software fi es the prognosis accuracy of the meta-model regarding the • Optimization of product performance geometric or environmental conditions. Subsequently, a automatically identifi es the corresponding input parameters response variation of a data set. The result is the Metamodel
    [Show full text]