Finding regions of interest on toroidal meshes

Abstract

Fusion promises to provide clean and safe energy, and a considerable amount of research effort is underway to turn this aspiration into 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 amount 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 100s of times faster than a recently published method based on Cartesian coordinates.

Detecting atmospheric rivers in large climate datasets

“…It is possible to represent the binary image differently to achieve better performance for the connected component labeling step [22]. The key idea is to make the representation of the foreground pixels more compact.…”

Introduction

“…In this context, a researcher might be interested in finding ignition kernels, or regions of extinction. On the other hand, when studying the stability of magnetic confinement for fusion, a researcher might be interested in regions with high electric potential because of their association with zonal flows, critical to the stability of the magnetic confinement-as shown in Figure 4a [42].…”

Towards Real-Time Detection and Tracking of Spatio-Temporal Features: Blob-Filaments in Fusion Plasma

“…By varying the first step, this procedure could be applying to different applications. Earlier, this approach was applied to data from regular meshes [2], [10]. In this work, we will demonstrated that it can also be effectively applied to irregular mesh data.…”

“…However, due to noise and lack of a ground truth image, this approach can be sensitive to the setting of parameters, and it is hard to use generic method for all images. In addition, the output from visualization is not convenient to feed into other analysis [2]. The regions of interest computed from this work can be more conveniently fed into other analyses.…”

“…A Wide variety of "big data" such as simulations of diesel combustion and images of tissue from biopsy, are spatiotemporal in nature [1], [2]. When analyzing these data sets, a common task is to find coherent structure in space and time, for example, ignition kernels in combustion, and cancerous cells in medical images.…”