Reasoning about Fluid Motion I: Finding Structures

Abstract

With the increasing role of high performance computing in attacking complex physical problems , there is an urgent need for the development of advanced computational technology to provide scientists with high-level assistance in the analysis, interpretation, and modeling of a massive amount of quantitative data . A critical area where this need is quite evident is the problem of turbulence. The overall research goal is to develop a computational environment to help scientists efficiently make observations and conceptual models of turbulence data sets. This paper presents the progress of this project . My approach is based on two key ideas: (1) Local interactions and evolution of coherent objects like vortices enable high-level qualitative interpretation of turbulence data, and (2) Abstracting from the particular features of fluid dynamical reasoning, I propose five core operations - aggregation, classification, re-description, spatial inference, and configuration change - as part of a general theory of imagistic reasoning. A new vortex-finding algorithm is also presented.