Comparing Uncertainty Quantification with Polynomial Chaos and Metamodel-Based Strategies for Computationally Expensive CAE Simulations and Optimization Applications

2015-01-0437

04/14/2015

Event
SAE 2015 World Congress & Exhibition
Authors Abstract
Content
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.
Meta TagsDetails
DOI
https://doi.org/10.4271/2015-01-0437
Pages
8
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.
Additional Details
Publisher
Published
Apr 14, 2015
Product Code
2015-01-0437
Content Type
Technical Paper
Language
English