The objective of the research is to decompose two-dimensional and three-dimensional steady state and two-dimensional time dependent fluid flow data sets into elementary structures for purposes of description, analysis and comparison. Results obtained from numerical calculations and modern (optical) diagnostics are often too complicated for manual inspection, manipulation and display. A simpler but still accurate description of these results is needed to facilitate data understanding. New methods will be developed for automatic extraction of features from vector data. Vector fields such as velocity, vorticity or pressure gradient are to be decomposed into elementary structures such as critical points and dividing streamlines. Topological features are recognized and the connections between them determined. The set of features and connections are represented in the form of a graph, from which displays of topologically significant features can be selected and displayed. This simplified feature description also aids data interpretation and allows comparison between data bases using syntactic pattern recognition. Such data can be both due to large- scale computations or multi-channel experimental measurements. The need for this research is becoming increasingly urgent with the development of new optical three-dimensional flow diagnostic techniques and improved speed and storage capability of supercomputers. Currently developed digital image processing and pattern recognition algorithms are mainly aimed at two-dimensional imagery. Extension of these techniques to higher dimensional data bases needs to be carefully considered and is not a simple task. Research that appears to hold promise for developing automatic methods for the manipulation, understanding and display of multi-dimensional vector fields will be carried out.
