Fast transonic flow prediction enables efficient aerodynamic design

Abstract

A deep learning framework is proposed for real-time transonic flow prediction. To capture the complex shock discontinuity of transonic flow, we introduce the residual network ResNet and deconvolutional neural networks to learn the nonlinear discontinuity phenomenon in transonic flow, which is affected by the Mach number, angle of attack, Reynolds number, and aerodynamic shape. In our framework, flow field variables on actual grid points are utilized in the neural network training to avoid the interpolation operation and the input of spatial position with a point cloud that is required with traditional convolutional neural networks. To investigate and validate the proposed framework, transonic flows around two-dimensional airfoils and three-dimensional wings are utilized to verify its effectiveness and prediction accuracy. The results prove that the model is able to efficiently learn the transonic flow field under the influence of the Mach number, angle of attack, Reynolds number, and aerodynamic shape. Significantly, some essential physical features, such as shock strength and location, flow separation, and the boundary layer, are accurately captured by this model. Furthermore, it is shown that our framework is able to make accurate predictions of the pressure distribution and aerodynamic coefficients. Thus, the present work provides an efficient and robust surrogate model for computational fluid dynamics simulation that enhances the efficiency of complex aerodynamic shape design optimization tasks and represents a step toward the realization of the digital twin concept.