The proper orthogonal decomposition (POD) based reduced-order model (ROM) has been an effective tool for flow field prediction in the engineering industry. The sample selection in the design space for POD basis construction affects the ROM performance sensitively. Adaptive sampling can significantly reduce the number of samples to achieve the required model accuracy. In this work, we propose a novel adaptive sampling algorithm, called conjunction sampling strategy, which is based on proven strategies. The conjunction sampling strategy is demonstrated on airfoil flow field prediction within the transonic regime. We demonstrate the performance of the proposed strategy by running 10 trials for each strategy for the robustness tests. Results show that the conjunction sampling strategy consistently achieves higher predictive accuracy compared with Latin hypercube sampling (LHS) and existing strategies. Specifically, under the same computational budget (40 training samples in total), the conjunction strategy reduced the L2 error by 56.7% compared with LHS. In addition, the conjunction strategy reduced the standard deviation of L2 errors by 62.1% with a 2.6% increase on the mean error compared with the best existing strategy.
J. Wang et al., "Novel Adaptive Sampling Algorithm for POD-Based Non-Intrusive Reduced Order Model," AIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021, article no. AIAA 2021-3051, American Institute of Aeronautics and Astronautics, Inc., AIAA, Jan 2021.
The definitive version is available at https://doi.org/10.2514/6.2021-3051
Mechanical and Aerospace Engineering
International Standard Book Number (ISBN)
Article - Conference proceedings
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01 Jan 2021