Streamline Distribution Method based on Vector-magnitude-aware Entropy

Abstract

Streamline is widely used in flow field visualization. Information-theoretic streamline distribution method performs well on demonstrating feature regions but only considers the direction component of vector. Our algorithm places streamlines based on information entropy calculated by both vector direction and vector magnitude, to cover more information of the flow field. By considering vector magnitude, the initial streamlines derived from entropy and supplementary streamlines derived from conditional entropy can convey the steepness of speed variation. An advanced streamline pruning method is also applied to improve the efficiency of our algorithm. Results prove that the streamlines produced by our algorithm can reflect the vector magnitude variation of a flow field without losing sight of the salient features.