Vortex-U-Net: An efficient and effective vortex detection approach based on U-Net structure

Abstract

Vortex detection methods help researchers to better understand the potential flow mechanism, and can be divided into three groups. Global methods have higher accuracy at the expense of time performance, while local methods provide results rapidly with poor accuracy. Machine learning-based methods consider both computational speed and accuracy, but their generalization and scalability are poor, which prevents them from being applied to real scenes. To address the above issues, we propose a novel vortex detection method, termed Vortex-U-Net. Our method has three characteristics. Firstly, our approach combines the characteristics of both global and local vortex detection methods. Secondly, it adopts the vorticity field, which integrates the velocity field and coordinates of grid points as the input. In this manner, it can keep more physical grid information of flow fields, which further improves the accuracy and generalization. Thirdly, our method fusions the properties of flow fields into the design of the loss function of the network. The proposed Vortex-U-Net model is subsequently evaluated against several widely used vortex detection methods on both numerically-simulated and analytical flows. Results reveal that our approach can achieve both high accuracy and performance.