Flow Field Feature Extraction and Tracking Based on Spatial Similarity Metrics

The analysis of temporal evolution patterns and interactions in complex, time-varying flow field numerical simulations is crucial to understanding the underlying mechanisms. In order to effectively describe the evolution of the flow field, feature extraction can effectively identify high-level feature regions in the flow field, and feature tracking can match relationships in the time dimension of the flow field, thus recognizing the generation of feature events. In this paper, a feature point classifier based on local extraction methods was designed to binary-judge flow field data points, and a feature region diffusion algorithm was used to extract feature regions. Based on the feature extraction results, this paper proposes a new feature similarity measurement method based on spatial distribution optimization, and implements a globally optimized feature tracking method based on Directed Acyclic Graph (DAG) graphs based on this measurement method. This method effectively solves the problem of tracking interruptions and tracking errors caused by using volume overlap as a measurement standard in traditional tracking methods, and eliminates the need for setting thresholds in traditional methods. Finally, the effectiveness of the feature extraction and feature tracking methods is proven through several experimental data.