Application of Direct and Surrogate-Based Optimization to Two-Dimensional Benchmark Aerodynamic Problems: A Comparative Study
This paper presents the results of applying direct and surrogate-based optimization (SBO) algorithms to two-dimensional aerodynamic benchmark problems, both involving transonic flow, one invisvid and the other viscous. The direct optimization methods used in this study are the adjoint-based FUN3D and Stanford University Unstructured solvers. The SBO algorithms include the SurroOpt framework, which exploits approximation-based models, the multi-level optimization (MLO) algorithm, which relies on physics-based models, as well as the adjoint-enhanced MLO algorithm. The results demonstrate that direct optimization and the approximation-based methods are able to yield designs that are comparable to those obtained with high-dimensional shape parameterization methods. Physics-based SBO shows a rapid design improvement at a low computational cost compared to the direct and the approximation-based SBO techniques, which indicates that-for certain problems-derivative-free methods may be competitive to adjoint-based algorithms when embedded in surrogate-assisted frameworks. On the other hand, global search approaches, while more expensive, exhibit the potential to produce the best quality results.
Y. A. Tesfahunegn et al., "Application of Direct and Surrogate-Based Optimization to Two-Dimensional Benchmark Aerodynamic Problems: A Comparative Study," Proceedings of the 53rd AIAA Aerospace Sciences Meeting (2015, Kissimmee, FL), American Institute of Aeronautics and Astronautics (AIAA), Jan 2015.
The definitive version is available at https://doi.org/10.2514/6.2015-0265
53rd AIAA Aerospace Sciences Meeting (2015: Jan. 5-9, Kissimmee, FL)
Mechanical and Aerospace Engineering
Center for High Performance Computing Research
Keywords and Phrases
Aerodynamics; Aerospace Engineering; Algorithms; Approximation Algorithms; Benchmarking; Comparative Studies; Computational Costs; Derivative-Free Methods; Global Search Approach; Multilevel Optimization; Physics-Based Models; Shape Parameterization; Surrogate-Based Optimization; Optimization
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2015 American Institute of Aeronautics and Astronautics (AIAA), All rights reserved.