Computer Aided Image Segmentation and Classification of the Reynolds Stress Anisotropy Tensor

A combinatorial technique merging image segmentation via K-means clustering and colormap of the barycentric triangle to investigate the Reynolds stress anisotropy tensor is posed. The clustering aids in extracting the identical features from the spatial distribution of the anisotropy colormap images by minimizing the sum of squared error between cluster center and all data points, simultaneously minimizing the squared error over all clusters. Three data sets are used to investigate the applicability of the clustering technique including a converging-diverging channel flow, a supersonic jet flow, and the flow in a large wind farm array under three different thermal stratification cases (unstable, neutral and stable). The clustering technique improves pattern visualization and allows identifying different complex region of the turbulent flow. Helping to better understand the internal structure of the turbulent flow for different cases. The clustering images of the anisotropy colormap allow extracting the characteristic turbulence states in large three dimensional domains with clarity, revealing the natural grouping of the anisotropy stress tensor and illustrating the form and behavior of the turbulence in a particular region.