Finding regions of interest on toroidal meshes

Fusion promises to provide clean and safe energy, and a considerable amount of research effort is under way to turn this aspiration into a reality. This work focuses on a building block for analyzing data produced from the simulation of microturbulence in magnetic confinement fusion devices: the task of efficiently extracting regions of interest. Like many other simulations where a large number of data are produced, the careful study of 'interesting' parts of the data is critical to gain understanding. In this paper, we present an efficient approach for finding these regions of interest. Our approach takes full advantage of the underlying mesh structure in magnetic coordinates to produce a compact representation of the mesh points inside the regions and an efficient connected component labeling algorithm for constructing regions from points. This approach scales linearly with the surface area of the regions of interest instead of the volume as shown with both computational complexity analysis and experimental measurements. Furthermore, this new approach is hundreds of times faster than a recently published method based on Cartesian coordinates.