Flow Field Super-Resolution Method Based on Graph Neural Networks for Wing Profiles

The primary objective of flow field super-resolution technology is to enhance the resolution of flow field data while simultaneously reducing computational resource requirements through computational simulations and data processing methods. Building upon prior research, this study introduces a novel flow field super-resolution method composed of three essential components: an interpolation module, graph data preprocessing, and a graph neural network, referred to as RGHF. Within the interpolation module, the radial basis function interpolation method is employed. Furthermore, a pioneering graph convolution operation named GConv is introduced in the graph neural network and prediction modules. This approach effectively leverages low-resolution data to predict high-resolution data, resulting in reduced computational time while maintaining an acceptable level of precision. Experimental results on the RAE2822 airfoil dataset demonstrate an 81% reduction in computational time, along with robustness across varying flight conditions.