Robust multi-fidelity aerodynamic design optimization using surrogate models

Conference paper (2008)

Authors

Daniel M. Jaeggi University of Cambridge
Geoffrey T. Parks University of Cambridge
William N. Dawes Computational Fluid Dynamics Laboratory, University of Cambridge
P.J. Clarkson University of Cambridge
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Published Date

2008

Language

English

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Abstract

We present a novel framework for robust aerodynamic design optimization with respect to CFD modeling errors using multi-fidelity simulations, a multi-objective optimization algorithm, and a surrogate model employed to map the error landscape across the design space. We use the low- and high-fidelity CFD model divergence as a proxy for simulation risk, which is simultaneously optimized along with a measure of performance. Instead of generating high-fidelity simulations directly, we employ a Sparse Pseudo-input Gaussian Process surrogate modeling algorithm to predict the divergence. We apply this approach to a simple diffuser design problem, coupled with a multi-objective Tabu Search optimization algorithm, which shows encouraging results. We are able to generate a range of Pareto optimal design, which display a trade-off between aerodynamic performance and simulation risk. This approach is applicable to more general problems and would be of interest in an industrial design setting.