A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models

Liyue Wang, Haochen Zhang, Cong Wang, Jun Tao, Xinyue Lan, Gang Sun () and Jinzhang Feng
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Liyue Wang: Department of Aeronautics & Astronautics, Fudan University, Shanghai 200433, China
Haochen Zhang: Department of Aeronautics & Astronautics, Fudan University, Shanghai 200433, China
Cong Wang: Department of Aeronautics & Astronautics, Fudan University, Shanghai 200433, China
Jun Tao: Department of Aeronautics & Astronautics, Fudan University, Shanghai 200433, China
Xinyue Lan: Department of Aeronautics & Astronautics, Fudan University, Shanghai 200433, China
Gang Sun: Department of Aeronautics & Astronautics, Fudan University, Shanghai 200433, China
Jinzhang Feng: Department of Aeronautics & Astronautics, Fudan University, Shanghai 200433, China

Mathematics, 2024, vol. 12, issue 10, 1-21

Abstract: With the rapid development of artificial intelligence technology, data-driven advanced models have provided new ideas and means for airfoil aerodynamic optimization. As the advanced models update and iterate, many useful explorations and attempts have been made by researchers on the integrated application of artificial intelligence and airfoil aerodynamic optimization. In this paper, many critical aerodynamic optimization steps where data-driven advanced models are employed are reviewed. These steps include geometric parameterization, aerodynamic solving and performance evaluation, and model optimization. In this way, the improvements in the airfoil aerodynamic optimization area led by data-driven advanced models are introduced. These improvements involve more accurate global description of airfoil, faster prediction of aerodynamic performance, and more intelligent optimization modeling. Finally, the challenges and prospect of applying data-driven advanced models to aerodynamic optimization are discussed.

Keywords: aerodynamic optimization; advanced model; artificial intelligence; data driven (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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