南京航空航天大学学报2024，Vol.56Issue(2)：253-263,11.DOI:10.16356/j.1005-2615.2024.02.007
基于UNet的翼型可压缩流场机器学习推理方法
Compressible Flowfields Machine Learning Inference Method for Airfoil Based on UNet
摘要
Abstract
In order to further improve the accuracy and efficiency of predicted compressible flowfields arounds airfoils at high Reynolds number,large angle of attack(AoA),and high Mach numbers,a machine learning inference method based on coordinate transformation method and UNet neural network is propesed.Firstly,a novel coordinate transformation method for data pre-processing is developed.This method transforms physical quantities and grid information in computational fluid dynamics into spatial information of neural network,making the distribution of flowfield information more in line with the input requirements of the neural network.Secondly,an improved deep UNet neural network is established,allowing the model to learn the fine and complex localized flow characteristics of the airfoil flowfield.The two innovative methods are combined to establish a machine learning inference method for the compressible flowfield of airfoils,and a fast and high-precision inference model is obtained.Finally,the flowfields and aerodynamic forces of different types of airfoils are predicted and analyzed,and results are compared with those from traditional machine learning method.The results show that the machine learning inference method proposed in this paper can better predict the compressible flowfield of airfoils,and can better capture the complex flow behavior at high Reynolds numbers,and predict the flow separation and shock waves phenomena under high Mach numbers with large AoA.关键词
UNet/机器学习/坐标转换/翼型/流动分离/激波Key words
UNet/machine learning/coordinate transformation/airfoil/flow separation/shock wave分类
航空航天引用本文复制引用
朱智杰,赵国庆,高远,招启军..基于UNet的翼型可压缩流场机器学习推理方法[J].南京航空航天大学学报,2024,56(2):253-263,11.基金项目
国家自然科学基金(12072156) （12072156）
直升机动力学全国重点实验室基金(61422202103) （61422202103）
江苏高校优势学科建设工程项目. （）
