Reliability analysis and robust design optimization using ANSYS and optislang. Dr.-Ing. Johannes Will, Dynardo GmbH, Weimar, Germany

Transcription

1 Reliability analysis and robust design optimization using ANSYS and optislang Dr.-Ing. Johannes Will, Dynardo GmbH, Weimar, Germany

2 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 Dynardo is your engineering specialist for CAE-based sensitivity analysis, optimization, robustness evaluation and robust design optimization. CAE-Consulting Our expertise: Mechanical engineering Civil engineering & Geomechanics Automotive industry Consumer goods industry Power generation 2

3 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! 3

4 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 CAE process (FEM, CFD, MBD, Excel, Matlab, etc.) 4

5 Sensitivity Analysis Borrowed by with courtesy of ANSYS, Inc ANSYS, Inc. All rights reserved.

6 Optimization Algorithms Gradient-based algorithms Start Local adaptive RSM Natural Inspired Optimization Genetic algorithms, Evolutionary strategies & Particle Swarm Optimization Meta model of optimal Prognosis(MOP) Global adaptive RSM Multi objective (Pareto) Optimization

7 How choosing the right algorithm? Optimization Algorithms: Evolutionary Algorithm Sensitivity Analysis allows best choice! Gradient- Based Algorithms Which one is the best? Pareto Optimization Adaptive Response Surface global Response Surface Borrowed by with courtesy of ANSYS, Inc ANSYS, Inc. All rights reserved.

8 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 CAE process (FEM, CFD, MBD, Excel, Matlab, etc.) 8

9 Definition of Uncertainties 1) Translate know how about uncertainties into proper scatter definition Yield stress Correlation of single uncertain values Distribution functions define variable scatter Tensile strength Correlation is an important characteristic of stochastic variables. Spatial Correlation = random fields 9

10 How to Define Robustness of a Design Intuitively: The performance of a robust design is largely unaffected by random perturbations Variance indicator: The coefficient of variation (CV) of the objective function and/or constraint values is smaller than the CV of the input variables Sigma level: The interval mean+/- sigma level does not reach an undesired performance (e.g. design for six-sigma) Probability indicator: The probability of reaching undesired performance is smaller than an acceptable value 10

11 Sensitivity of Uncertainties

12 Reliability Analysis Algorithms Gradient-based algorithms = First Order Reliability algorithm (FORM) ISPUD Importance Sampling using Design Point Adaptive Response Surface Method Monte Carlo Sampling Latin Hypercube Sampling Directional Sampling X2 X1

13 How choosing the right algorithm? Robustness Analysis provide the knowledge to choose the appropriate algorithm Robustness & Reliability Algorithms

14 Robust Design Optimization Start Robust Design Optimization Robust Design Variance based Robustness Evaluation Probability based Robustness Evaluation, (Reliability analysis) Optimization Sensitivity Study Single & Multi objective (Pareto) optimization CAE process (FEM, CFD, MBD, Excel, Matlab, etc.) 14

15 Robust Design Optimization Robustness in terms of constraints Robustness in terms of the objective Safety margin (sigma level) of one or more responses y: Reliability (failure probability) with respect to given limit state: Performance (objective) of robust optimum is less sensitive to input uncertainties Minimization of statistical evaluation of objective function f (e.g. minimize mean and/or standard deviation): 15

16 Robust Design Optimization Adaptive Response Surface Evolutionary Algorithm Pareto Optimization Borrowed by with courtesy of ANSYS, Inc ANSYS, Inc. All rights reserved.

17 Challenges of RDO in Virtual Prototyping With improvements in parametric modeling, CAE (software) and CPU (hardware) there seems to be no problem to establish RDO (DfSS) product development strategies by using stochastic analysis There are many research paper or marketing talks about RDO/DfSS. But why industrial papers about successful applications are so rare? Where is the problem with RDO? 17

18 Challenge of RDO reliable input Successful RDO needs a balance between three main pillars 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 of an RDO process/package to support real world industrial RDO tasks Reliable input scatter definition all possible important input scatter sources have to be included to be able to estimate output scatter and input scatter importance many scattering variables (in the beginning) of an RDO task not only optimization parameter scatter! for best translation of input scatter a suitable variety of distribution functions are necessary correlations between scattering inputs needs to be considered 18

19 Challenge of RDO - reliable analysis Reliable CAE-based stochastic analysis if single design evaluation needs significant CPU it is a challenge to balance between number of solver runs spend on Robustness Estimation and Reliability Analysis and the reliability of the scatter measurements itself Efficient and reliable methodology to sort out important/unimportant input scatter and estimate variance based output scatter ranges (mean values, standard deviation)= Robustness Evaluation Efficient and reliable methodology to estimate probabilities = Reliability Analysis Efficient and reliable methodology to combine optimization and Robustness/Reliability analysis Because all RDO algorithms will estimate robustness/reliability measurements with minimized number of solver runs the proof of the reliability of the final RDO design is absolutely mandatory! 19

20 Challenge of RDO - reliable post processing Reliable post processing Stochastic Analysis and statistical post processing estimates variation of response values Reliable quantification of input scatter variable importance Reliable estimation of variation using fit of distribution functions Provide error estimation of reliability measurements (probabilities) Filter of insignificant/unreliable results Easy and safe to use 20

21 Robust Design Optimization - RDO Robust Design Optimization combines optimization and Robustness Evaluation. From our experience it is often necessary to investigate both domains separately to be able to formulate a RDO problem. optislang offers you either iterative or automatic RDO flows. Define safety factors Robustness evaluation Robust design optimization Sensitivity analysis Robustness proof!

22 Parameterization RDO Centrifugal Compressor Parametric geometry definition using ANSYS BladeModeler (17 geometric parameter) Model completion and meshing using ANSYS Workbench 22 by courtesy of

23 RDO Centrifugal Compressor Fluid Structure Interaction (FSI) coupling Parametric fluid simulation setup using ANSYS CFX Parametric mechanical setup using ANSYS Workbench 23 by courtesy of

24 RDO Centrifugal Compressor Optimization goal: increase efficiency Constraints: 2 pressure ratio s, 66 frequency constraints, Robustness Input Parameter 21 Output Parameter 43 Constraints 68 Tolerance limit 1.34<Π T <1.36 ~13% outside Initial SA ARSM I EA I ARSM II ARSM III Total Pressure Ratio Efficiency [%] #Designs by courtesy of

25 RDO Centrifugal Compressor Robust Design Optimization with respect to 21 design parameters and 20 random geometry parameters, including manufacturing tolerances. Robust Design was reached after =650 design evaluations consuming. Robustness evaluation RDO optimization 25 Sensi + first optimization step by courtesy of Robustness proof using Reliability Analysis

26 optiplug - ANSYS Workbench optislang Interface Parameter Manager OptiSLang-Plugin: just click to integrate workbench in optislang Parameter & Responses 26

27 optislang inside ANSYS Workbench The Workbench Effect easier to use Fully parametric Easy parametric set up of complex simulations easy use of best praxis automated flows inside ANSYS

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