工程设计学报2024，Vol.31Issue(3)：292-300,9.DOI:10.3785/j.issn.1006-754X.2024.03.143
基于深度学习的翼型参数化建模方法
Deep learning-based method for parametrized modeling of airfoil
摘要
Abstract
In order to solve the problems of low efficiency and heavy computational workload during the optimization design process in the existing airfoil geometric parametrized modeling methods,a deep learning-based airfoil parametrized modeling method was put forward.In this method,the two-dimensional airfoil images converted from coordinate points of airfoil upper and lower surfaces in the airfoil database of the University of Illinois at Urbana-Champaign(UIUC)were taken as the input.Firstly,the convolution operations were used to extract geometric features of a large amount of airfoil images.Then,the extracted geometric features were classified and compressed by multi-layer perceptron,and the airfoil shape was compressed into several simplified fitting parameters.Finally,the airfoil image was restored and the coordinates of points on the upper and lower surfaces of airfoil were output by a decoder.On this basis,the influence of the number of fitting parameters on the geometric accuracy of airfoil was discussed,and a convolutional neural network(CNN)structure with six fitting parameters was determined.At the same time,the fitting accuracy of the proposed method was verified by the computational fluid dynamics numerical simulation.Finally,the visual airfoil geometry design software was developed to adjust and modify the fitting parameters,and the influence law of each fitting parameter on the airfoil shape was summarized.The results indicated that all the six fitting parameters had a global impact on the airfoil shape,and the new airfoil design space could be obtained by adjusting the six fitting parameters individually or jointly.This research results can provide technical support and theoretical guidance for airfoil optimization design.关键词
翼型参数化/几何特征/深度学习/卷积神经网络Key words
airfoil parameterization/geometric feature/deep learning/convolutional neural network分类
机械制造引用本文复制引用
沈剑雄,刘迎圆,王乐勤..基于深度学习的翼型参数化建模方法[J].工程设计学报,2024,31(3):292-300,9.基金项目
国家自然科学基金青年基金资助项目(51806145) （51806145）
