A CNN-based shock detection method in flow visualization

Abstract

As one of the main technologies of flow visualization, shock detection plays a key role in feature identification and has been intensively studied. However, existing methods take too much execution time to meet the requirement of post-processing on large-scale Computational Fluid Dynamics (CFD) flow field data. To address this problem, in this paper, we propose a detection method for shock waves based on Convolutional Neural Networks (CNN) and design a novel loss function to optimize the detection results. In specific, the proposed method samples small patches from flow field data, and trains a detection network which includes multiple convolutional layers. This network is responsible for generating shock values and finding the location of shock waves. Compared with the existing shock detection methods which are not based on deep learning, our method has great advantages in detection time. Compared with the ones based on deep learning, our method gives a better detection result of shock waves. Extensive experimental results demonstrate the good generalization of the proposed method on many datasets.