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Robust design using Bayesian Monte Carlo
Author(s) -
Kumar Apurva,
Nair Prasanth B.,
Keane Andy J.,
Shahpar Shahrokh
Publication year - 2007
Publication title -
international journal for numerical methods in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 168
eISSN - 1097-0207
pISSN - 0029-5981
DOI - 10.1002/nme.2126
Subject(s) - monte carlo method , aerodynamics , parametric statistics , robustness (evolution) , gas compressor , optimal design , uncertainty quantification , computer science , blade (archaeology) , computational fluid dynamics , bayesian probability , mathematical optimization , engineering , mathematics , machine learning , artificial intelligence , mechanical engineering , biochemistry , statistics , chemistry , gene , aerospace engineering
In this paper, we propose an efficient strategy for robust design based on Bayesian Monte Carlo simulation. Robust design is formulated as a multiobjective problem to allow explicit trade‐off between the mean performance and variability. The proposed method is applied to a compressor blade design in the presence of manufacturing uncertainty. Process capability data are utilized in conjunction with a parametric geometry model for manufacturing uncertainty quantification. High‐fidelity computational fluid dynamics simulations are used to evaluate the aerodynamic performance of the compressor blade. A probabilistic analysis for estimating the effect of manufacturing variations on the aerodynamic performance of theblade is performed and a case for the application of robust design is established. The proposed approach is applied to robust design of compressor blades and a selected design from the final Pareto set is compared with an optimal design obtained by minimizing the nominal performance. The selected robust blade has substantial improvement in robustness against manufacturing variations in comparison with the deterministic optimal blade. Significant savings in computational effort using the proposed method are also illustrated. Copyright © 2007 John Wiley & Sons, Ltd.