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

• Published in: Mathematics (Basel) Vol. 12; no. 10; p. 1417
• Main Authors: Wang, Liyue, Zhang, Haochen, Wang, Cong, Tao, Jun, Lan, Xinyue, Sun, Gang, Feng, Jinzhang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2024
• Subjects: Accuracy; advanced model; aerodynamic optimization; Airfoils; Analysis; Artificial intelligence; data driven; Deformation; Design optimization; Design techniques; Efficiency; Energy consumption; Genetic algorithms; Geometry; Methods; Optimization; Optimization algorithms; Parameterization; Performance evaluation; Variables
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math12101417

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.