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.
Original language | English |
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Title of host publication | 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO |
Publisher | American Institute of Aeronautics and Astronautics Inc. (AIAA) |
ISBN (Print) | 9781563479472 |
DOIs | |
Publication status | Published - 2008 |
Externally published | Yes |