Compared to traditional computational fluid dynamics methods, which involve high computational costs to solve the governing flow equations, deep learning has emerged as a promising alternative for rapid flow field prediction. This paper introduces a novel deep learning method for quickly reconstructing three-dimensional (3D) wing inviscid flow fields under sparse data conditions. Initially, an autoencoder model is utilized to extract the geometric features of the 3D wing. This model efficiently compresses the point cloud data of the wing into a six-dimensional latent vector, facilitating a compact representation of complex geometric information. Next, a residual-attention multilayer perceptron (RAMLP) model, which incorporates attention mechanisms and residual connections, is introduced to predict the flow field variables, including velocity components in the x, y, and z directions, as well as the pressure coefficient. The model takes geometric feature parameters, Mach number, angle of attack, and flow field coordinates as inputs, allowing it to generalize effectively across various incoming flow conditions. Finally, a comparative analysis is conducted to evaluate the performance of RAMLP against two commonly used aerodynamic surrogate models: multilayer perceptron (MLP) and multi-head perceptron (MHP), in the context of 3D wing inviscid flow field prediction. Although RAMLP has a more complex computational structure, it maintains efficient prediction times, processing a single condition in under one second. Furthermore, RAMLP exhibits superior prediction accuracy under sparse data conditions compared to both MLP and MHP models.
Li et al. (Fri,) studied this question.