Clifford convolution and pattern matching on irregular grids

Abstract

Flow features are the essence of fluid flow data and their extraction and analysis is a major goal of most flow visualizations. Unfortunately, most techniques are sensitive to noise and limited to a certain class of features like vortices. Excellent general feature detection methods for scalar fields can be found in image processing. Many of these methods use convolution filters. In an earlier paper, we showed that the convolution operator can be extended to vector fields using Clifford algebra, but the approach is limited to uniform grids. In this article, we extend this approach to irregular grids by examining three different methods. Results on several CFD data sets clearly favor a local resampling of the flow field.