翼型
计算流体力学
Lift(数据挖掘)
升阻比
遗传算法
空气动力学
解算器
阻力系数
人工神经网络
物理
阻力
形状优化
升力系数
算法
计算机科学
机械
数学优化
数学
有限元法
人工智能
机器学习
雷诺数
热力学
湍流
作者
Mingyu Wu,Xin-Yi Yuan,Zhihua Chen,Wei‐Tao Wu,Yue Hua,Nadine Aubry
出处
期刊:Physics of Fluids
[American Institute of Physics]
日期:2023-08-01
卷期号:35 (8)
被引量:9
摘要
To alleviate the computational burden associated with the computational fluid dynamics (CFD) simulation stage and improve aerodynamic optimization efficiency, this work develops an innovative procedure for airfoil shape optimization, which is implemented through coupling the genetic algorithm (GA) optimizer with the aerodynamic coefficients prediction network (ACPN) model. The ACPN is established using a fully connected neural network with the airfoil geometry as the input and aerodynamic coefficients as the output. The results show that the ACPN's mean prediction accuracy for the lift and drag coefficient is high up to about 99.02%. Moreover, the prediction time of each aerodynamic coefficient is within 5 ms, four orders of magnitude faster compared to the CFD solver (3 min). Taking advantage of the fast and accurate prediction, the proposed ACPN model replaces the expensive CFD simulations and couples with GA to force the airfoil shape change to maximize the lift–drag ratio under multiple constraints. In terms of time efficiency, optimized airfoils can be fast obtained within 25 s. Even considering an extra 50 h spent on data preparing and 20 s for model training, the overall calculation cost is reduced by a remarkable 62.1% compared to the GA-CFD optimization method (5.5 days). Furthermore, the GA-ACPN model improves the lift–drag ratio with and without constraint by 51.4% and 55.4% for NACA0012 airfoil, respectively, while 50.3% and 60.0% improvement achieved by the GA-CFD optimization method. These results indicate that the GA-ACPN optimization approach significantly enhances the optimization efficiency and has great potential to address varying constraint optimization problems.
科研通智能强力驱动
Strongly Powered by AbleSci AI