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Comparing Uncertainty Quantification with Polynomial Chaos and Metamodel-Based Strategies for Computationally Expensive CAE Simulations and Optimization Applications
Technical Paper
2015-01-0437
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Robustness/Reliability Assessment and Optimization (RRAO) is often computationally expensive because obtaining accurate Uncertainty Quantification (UQ) may require a large number of design samples. This is especially true where computationally expensive high fidelity CAE simulations are involved. Approximation methods such as the Polynomial Chaos Expansion (PCE) and other Response Surface Methods (RSM) have been used to reduce the number of time-consuming design samples needed. However, for certain types of problems require the RRAO, one of the first question to consider is which method can provide an accurate and affordable UQ for a given problem. To answer the question, this paper tests the PCE, RSM and pure sampling based approaches on each of the three selected test problems: the Ursem Waves mathematical function, an automotive muffler optimization problem, and a vehicle restraint system optimization problem. Results of the UQ are compared thoroughly and recommendations based on the empirical results are made as the design guidelines to engineers.
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Citation
Xue, Z., Marchi, M., Parashar, S., and Li, G., "Comparing Uncertainty Quantification with Polynomial Chaos and Metamodel-Based Strategies for Computationally Expensive CAE Simulations and Optimization Applications," SAE Technical Paper 2015-01-0437, 2015, https://doi.org/10.4271/2015-01-0437.Also In
References
- Clarich , A. , Pediroda , V. , and Poloni , C. A competitive Game Approach for Multi-objective Robust Design Optimization Proceedings of the AIAA 1 st Intelligent Systems Technical Conference Chicago, IL 2004 10.2514/6.2004-6511
- Pediroda , V. , Parussini , L. , Poloni , C. , Parashar , S. et al. Efficient Stochastic Optimization using Chaos Collocation Method with modeFRONTIER SAE Int. J. Mater. Manf. 1 1 747 753 2009 10.4271/2008-01-1429
- Parashar , S. , Clarich , A. , Geremia , P. , and Otani , A. Reverse Multi-Objective Robust Design Optimization (R-MORDO) Using Chaos Collocation Based Robustness Quantification for Engine Calibration 13 th AIAA/ISSMO Conference Fort Worth, TX 2010 10.2514/6.2010-9038
- Wiener , N. The homogeneous chaos Amer. J. Math. 60 4 897 936 1938 10.2307/2371268
- Xiu , D. and Karniadakis , G. E. The Wiener-Askey polynomial chaos for stochastic differential equations SIAM J. Sci. Comput. 24 2 619 644 2002 10.1137/S1064827501387826
- Poles , S. and Lovison , A. A polynomial chaos approach to multiobjective optimization Dagstuhl Seminar Proceedings 09041, Hybrid and Robust Approaches to Multiobjective Optimization 2009 http://drops.dagstuhl.de/opus/volltexte/2009/2000
- Rasmussen , C. E. and Williams , C. K. I. Gaussian Processes for Machine Learning MIT Press 2006 026218253X
- Rökkönen , J. Continuous Multimodal Global Optimization with Differential Evolution-Based Methods PhD Thesis Lappeenranta University of Technology 2009
- modeFRONTIER® is a product of ESTECO srl www.esteco.com
- Turco , A. MetaHybrid: Combining Metamodels and Gradient-Based Techniques in a Hybrid Multi-Objective Genetic Algorithm Learning and Intelligent Optimization, 5 th International Conference, LION 5 Rome, Italy 2011 10.1007/978-3-642-25566-3_22
- http://en.wikipedia.org/wiki/Truncated_normal_distribution
- GT-Power® is a product of Gamma Technologies www.gtisoft.com
- Rigoni , E. and Turco , A. Metamodels for Fast Multi-objective Optimization: Trading Off Global Exploration and Local Exploitation 8 th International Conference, Simulated Evolution and Learning 2010, Lecture Notes in Computer Science 6457 523 532 2010
- Xue , Z. , Parashar , S. , Li , G. , and Fu , Y. Optimization Strategies to Explore Multiple Optimal Solutions and Its Application to Restraint System Design SAE Int. J. Passeng. Cars - Mech. Syst. 5 1 540 551 2012 10.4271/2012-01-0578
- Deb , K. , Pratap , A. , Agarwal , S. , and Meyarivan , T. A fast and elitist multiobjective genetic algorithm: NSGA-II Evolutionary Computation, IEEE Transactions on 6 2 182 197 2002 10.1109/4235.996017