Streamline Seeding Strategy Based on Quantitative Evaluation
Liming Shen, Wenke Wang · 2022
Seeding strategy can provide deep insight into complex flow data and plays a crucial role in generating comprehensible streamlines. For lack of quantitative evaluation in streamline distribution methods, we propose an evaluation method based on information theory, which quantifies the information difference between the original and current vector field with distribution of streamlines. Furtherly, we utilize the evaluation method to guide the seeding strategy, displaying the critical information with a specified number of streamlines. And we consider both direction and magnitude of vector to effectively exhibit the detailed content in the flow field. Quantitative and qualitative comparisons are also carried out with an existing classical approach, indicating that our seeding strategy can succinctly express more effective flow field information.