基于深度学习的 PIV 流场图像修复技术

Particle Image Velocimetry（PIV）is widely used to measure the flow fields in aerospace researches. However，it can hardly tackle complex flow fields in combustors，and defects could usually be found in the flow field after the post-processing，where traditional cross-correlation method is applied. The deep learning method is applied in the PIV post-processing to achieve detection and inpainting of abnormal flow field data. On the counterflow premixed methane flame dataset，the abnormal data is classified into two categories，which is fixed with a U-Net-based convolutional neural network model. After training and optimizing，the model can detect anomalies at a high confidence level and repair them with different strategies. The noise is filtered while the original normal data retains. Meanwhile，the model has good transferability，which might help other flow data inpainting researches. Owing to the strong nonlinearity of the model，the proposed method behaves with high accuracy and robustness，which shows huge advantages over traditional inpainting models such as POD iteration method and median filtering.