Visualizing turbulence anisotropy in the spatial domain with componentality contours

Visual representations of quantitative information have proven very useful in the context of fluids engineering. In experiments Schlieren imagery for compressible flows and dye/smoke streaks for low-speed flows (Van Dyke 1982) are vivid examples of how physical behaviors and important system dynamics can be observed, giving the analyst a better understanding of the flow structures. Computational fluid dynamics (CFD) relies just as heavily on visual representations to extract understanding from and explore the large amounts of data generated by numerical simulations. Whether using the Q-criterion (Dubief & Delcayre 2000) to investigate vortices or plotting of velocity profiles, the ability to visually represent quantitative data is a critical aspect of numerical simulation analysis. One such frequently visualized quantity is the anisotropic behavior of turbulence, a common feature of complex fluid flows. In many engineering applications, properly predicting the amount and type of anisotropy is critical for accurate numerical simulation of these flows. In practice, the turbulence modeling community uses various properties of the Reynolds stress anisotropy tensor