Multidimensional data analysis and visualization for time-dependent CFD problems

This paper is devoted to analysis and visualization of multidimensional data in problems of computational fluid dynamics (CFD). Multidimensional data are considered as the result of parametrical search and optimizing analysis. For such types of multidimensional data, analysis is aimed to find some hidden dependencies of specific parameters. This is the main difference of the proposed approach from the general approach of data analysis, which is usually applied to solve problems of classification and clusterization. A rough approximate approach is proposed for data processing. This approach includes data visualization in the space of principal components and data approximation by geometrical primitives (in particular, planes). An example of practical application of the proposed approach is given. The possibility of elastic maps application to multidimensional data in question is discussed.