A CNN-based shock detection method in flow visualization. (30th April 2019)

• Record Type: Journal Article
• Authors: Liu, Yang; Lu, Yutong; Wang, Yueqing; Sun, Dong; Deng, Liang; Wang, Fang; Lei, Yan
• Is Part Of: Computers & fluids. Volume 184(2019)
• Page Start: 1
• Page End: 9
• Publication Date: 2019-04-30
• Subjects: Flow visualization -- Shock detection -- CFD Flow field -- CNN; Fluid dynamics -- Data processing -- Periodicals; 532.050285
• DOI: 10.1016/j.compfluid.2019.03.022
• Languages: English
• ISSNs: 0045-7930

Abstract

Highlights: A shock detection method based on Convolutional Neural Networks is proposed. A novel loss function is designed for shock waves. Compared with existing methods, ours is more efficient or yields better results. The good generalization of our method is demonstrated by extensive experiments. 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.