A CNN-based vortex identification method

Vortex identification and visualization are important for understanding the underlying physical mechanism of the flow field and have been intensively studied recently. Local vortex identification methods could provide results in a rapid way, but they require the choice of a suitable criterion and threshold, which leads to poor robustness. Global vortex identification methods could obtain reliable results, while they require considerable user input and are computationally intractable for large-scale data sets. To address the problems described above, we present a novel vortex identification method based on the convolutional neural network (CNN). The proposed method integrates the advantages of both the local and global vortex identification methods to achieve higher precision and recall efficiently. In specific, the proposed method firstly obtains the labels of all grid points using a global and objective vortex identification method and then samples local patches around each point in the velocity field as the inputs of CNN. After that it trains the CNN to decide whether the central points of these patches belong to vortices. By this way, our method converts the vortex identification task to a binary classification problem, which could detect vortices quickly from the flow field in an objective and robust way. Extensive experimental results demonstrate the efficacy of our proposed method, and we expect this method can replace or supplement existing traditional methods.