Automated Optimal Experimental Design Strategy for Reduced Order Modeling of Aerodynamic Flow Fields
Abstract
Aerodynamic flow fields reveal essential physical insights (such as shocks) that substantially affect aerodynamic performance. However, conventional flow field computations require time-consuming simulations. Alternatively, reduced-order models (ROMs) allow fast flow field predictions, enabling real-time decision-making. Efficient sampling is required to train ROMs without incuring prohibitive costs. In this paper, we propose a fully automated optimal experimental design (Auto-OED) strategy on proper orthogonal decomposition (POD) for ROM-based rapid flow field predictions. Auto-OED uses two individual optimal experimental design (OED) strategies, automatically selects the number of POD bases for the first sampling strategy, and intelligently switches to the second strategy on the fly. We showcased the Auto-OED strategy on airfoil flow field predictions in the transonic regime. OED-based ROMs were constructed over 20 trials for robustness tests with a total of 40 training samples in each trial, including 16 random initial samples by Latin hypercube sampling (LHS) and 24 OED samples. The results demonstrated that the ROM predictions completely based on LHS had an error of 1.6x10−3 while the existing OED strategies-based ROMs over 20 trials achieved a mean error (μerr) of 7.5x10−4 with a standard deviation (σerr) of 9.0x10−5. In contrast, the best Auto-OED ROM achieved the lowest μerr of 7.5x10−4 and lowest σerr of 6.2x10−5. These error reductions confirm the viability of the proposed Auto-OED-based ROM in fluid field predictions and potentially other engineering applications of a similar type.
Recommended Citation
J. Wang et al., "Automated Optimal Experimental Design Strategy for Reduced Order Modeling of Aerodynamic Flow Fields," Aerospace Science and Technology, vol. 150, article no. 109214, Elsevier; Elsevier Masson, Jul 2024.
The definitive version is available at https://doi.org/10.1016/j.ast.2024.109214
Department(s)
Mechanical and Aerospace Engineering
International Standard Serial Number (ISSN)
1270-9638
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier; Elsevier Masson, All rights reserved.
Publication Date
01 Jul 2024
Comments
University of Texas at Austin, Grant None