Evolutionary generative design of supercritical airfoils: an automated approach driven by small data

Sun, Kebin1; Wang, Weituo1; Cheng, Ran1; Liang, Yu2; Xie, Hairun3; Wang, Jing3; Zhang, Miao3

2023-08-01

10.1007/s40747-023-01214-0

COMPLEX & INTELLIGENT SYSTEMS   影响因子和分区

2199-4536

2198-6053

Supercritical airfoils are critical components in the design of commercial wide-body aircraft wings due to their ability to enhance aerodynamic performance in transonic flow regimes. However, traditional design methods for supercritical airfoils can be time-consuming and require significant manual effort, not to mention the high cost associated with computational fluid dynamics analysis. To address these challenges, this paper introduces a highly automated approach for supercritical airfoil design, called Evolutionary Generative Design (EvoGD). The EvoGD approach is based on the framework of Evolutionary Computation and employs a series of sophisticated data-driven generative models incorporated with physical information to iteratively refine initial airfoil shapes, resulting in improved aerodynamic performances and reduced constraint violations. Moreover, to speed up the evaluation of the generated airfoils, a series of accurate and efficient data-driven predictors are utilized. The efficacy of the EvoGD approach was demonstrated through experiments on a dataset of 501 supercritical airfoils, including one baseline design and 500 randomly perturbed airfoils. On average, the generated airfoils showed improved performance in terms of buffet lift coefficient, cruise lift-to-drag ratio, and thickness by 5%, 4%, and 1%, respectively. The best generated airfoil outperformed the baseline design in terms of critical buffet lift coefficient and cruise lift-to-drag ratio by 7.1% and 6.4%, respectively. The entire design process was completed in less than an hour on a personal computer, highlighting the high efficiency and scalability of the EvoGD approach.

Evolutionary computation Artificial neural network Generative learning Supercritical airfoil design

SCI

英语

第一 ; 通讯

Computer Science

Computer Science, Artificial Intelligence

WOS:001052528300001

SPRINGER HEIDELBERG

2-s2.0-85168625857

Web of Science

1.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China
2.Beihang Univ, Inst Unmanned Syst, Beijing 100191, Peoples R China
3.Shanghai Aircraft Design & Res Inst, Shanghai 200436, Peoples R China

Sun, Kebin,Wang, Weituo,Cheng, Ran,et al. Evolutionary generative design of supercritical airfoils: an automated approach driven by small data[J]. COMPLEX & INTELLIGENT SYSTEMS,2023,10(1):1167-1183.

Sun, Kebin.,Wang, Weituo.,Cheng, Ran.,Liang, Yu.,Xie, Hairun.,...&Zhang, Miao.(2023).Evolutionary generative design of supercritical airfoils: an automated approach driven by small data.COMPLEX & INTELLIGENT SYSTEMS,10(1),1167-1183.

Sun, Kebin,et al."Evolutionary generative design of supercritical airfoils: an automated approach driven by small data".COMPLEX & INTELLIGENT SYSTEMS 10.1(2023):1167-1183.