A general framework for airfoil flow field reconstruction based on transformer-guided diffusion models

High-Resolution (HR) data on flow fields are critical for accurately evaluating the aerodynamic performance of aircraft. However, acquiring such data through large-scale numerical simulations or wind tunnel experiments is highly resource intensive. This paper proposes a FlowViT-Diff framework that integrates a Vision Transformer (ViT) with an enhanced denoising diffusion probabilistic model for the Super-Resolution (SR) reconstruction of HR flow fields based on low-resolution inputs. It provides a quick initial prediction of the HR flow field by optimizing the ViT architecture, and incorporates this preliminary output as guidance within an enhanced diffusion model. The latter captures the Gaussian noise distribution during forward diffusion and progressively removes it during backward diffusion to generate the flow field. Experiments on various supercritical airfoils under different flow conditions show that FlowViT-Diff can robustly reconstruct the flow field across multiple levels of downsampling. It obtains more consistent global and local features than traditional SR methods, and yields a 3.6-fold increase in its training speed via transfer learning. Its accuracy of reconstruction of the flow field is 99.7% under ultra-low downsampling. The results demonstrate that FlowViT-Diff not only exhibits effective flow field reconstruction capabilities, but also provides two reconstruction strategies, both of which show effective transferability.