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

为进一步提升高雷诺数、大迎角(Angle of attack,AoA)和高马赫数下的翼型可压缩流场预测精度和效率,本文提出了一种基于坐标转换方法和UNet神经网络的机器学习推理方法。首先,提出了用于数据前处理的坐标转换方法,将计算流体力学中的物理量和网格信息转换成神经网络空间信息,使流场信息的分布更符合神经网络的输入要求。其次,建立了新型深度UNet神经网络,使模型学习到翼型流场精细复杂的局部流动特征。本文将两种方法结合,建立了翼型可压缩流场机器学习推理方法,得到快速高精度的推理模型。最后,对不同种类翼型的流场与气动力进行预测分析,并与传统机器学习方法预测的结果进行比较。结果表明,本文提出的机器学习推理方法能够较好地预测翼型的可压缩流场,并且能够更好地捕捉高雷诺数下的复杂流动行为以及预测大迎角、高马赫数条件下的流动分离和激波现象。

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.