基于注意力机制的翼型反设计方法  

Airfoil reverse design method based on self-attention mechanism

作　　者：王超杰 何磊[2] 李川 钱炜祺[2] 黄友翔 WANG Chaojie;HE Lei;LI Chuan;QIAN Weiqi;HUANG Youxiang(School of Computer(School of Software),Sichuan University,Chengdu 610065,China;Computational Aerodynamics Institute,China Aerodynamics Research and Development Center,Mianyang Sichuan 621000,China)

机构地区：[1]四川大学计算机学院(软件学院),成都610065 [2]中国空气动力研究与发展中心计算空气动力研究所,四川绵阳621000

出　　处：《航空动力学报》2025年第1期285-292,共8页Journal of Aerospace Power

摘　　要：为了简化翼型反设计的过程,基于注意力机制设计了一个端到端的,应用于翼型反设计的深度学习模型,该模型可以学习到翼型曲线和压力分布之间的联系,直接输入压力分布图像就可以得到与之对应的翼型图像。生成了6561组样本,其中6000组样本用于训练,561组样本用于验证。实验结果表明:该模型在验证集上的方均根误差为0.0023,平均相对偏差为2.53%,训练耗时743.4 s,验证耗时12.18 s,预测一个翼型曲线平均耗时0.0217 s,由此表明该模型具有较高的精度和效率。An end-to-end deep learning model for reverse airfoil design based on attention mechanism was proposed to simplify the reverse airfoil design process.The model could learn the relationship between the airfoil curve and the pressure distribution,and the matching airfoil picture could be generated by directly inputting the pressure distribution image.6561 samples were generated,with 6000 used for training and 561 for validation.The model's root mean square error on the verification set was 0.0023,its average relative deviation was 2.53%,its training and verification time was 743.4 s and 12.18 s,respectively,and its average prediction time for an airfoil curve was 0.0217 s.The results demonstrated the model's high accuracy and efficiency.

关 键 词：翼型反设计 翼型曲线 压力分布 深度学习 注意力机制 

分 类 号：V211.3[航空宇航科学与技术—航空宇航推进理论与工程]