Visualization Techniques for Studying Large-Scale Flow Fields from Fusion Simulations

摘要

This article presents a joint study between computer scientists and fusion scientists in developing visual tools for studying patterns in flow fields from large-scale magnetic confinement fusion simulations. The authors visualize time-varying flow data by generating trajectory curves via massless particle advection and design a set of color functions, pathline filters, and projection methods specific to achieve fusion research objectives. These tools aid scientists in managing the visual complexity of large trajectory datasets and are crucial in locating and understanding subtle features of interest. The authors demonstrate the effectiveness of their techniques by using real fusion simulation data and provide insight by domain scientists. They also discuss how their methods address common scalability concerns.