Flow feature detection for grid adaptation and flow visualization

Abstract Adaptive grid refinement/coarsening is an important method for achieving increased accuracy of flow simulations with reduced computing resources. Further, flow visualization of complex 3-D fields is a major task of both computational fluid dynamics (CFD), as well as experimental data analysis. A primary issue of adaptive simulations and flow visualization is the reliable detection of the local regions containing features of interest. A relatively wide spectrum of detection functions (sensors) is employed for representative flow cases which include boundary layers, vortices, jets, wakes, shock waves, contact discontinuities, and expansions. The focus is on relatively simple sensors based on local flow field variation using 3-D general hybrid grids consisting of multiple types of elements. A quantitative approach for sensors evaluation and comparison is proposed and applied. It is accomplished via the employment of analytic flow fields. Automation and effectiveness of an adaptive grid or flow visualization process requires the reliable determination of an appropriate threshold for the sensor. Statistical evaluation of the distributions of the sensors results in a proposed empirical formula for the threshold. The qualified sensors along with the automatic threshold determination are tested with more complex flow cases exhibiting multiple flow features.

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