The computational modeling of high-speed flows is characterized by a plethora of complex physical phenomena that do not appear in lower Mach regimes. The analysis of those flows requires accurate, robust and advanced numerical techniques to capture all flow features properly. The numerical investigation of such flow problems may require extremely fine meshes over narrow regions of the physical domain to resolve the large solution variations. The high-gradient regions are not known to the analyst a-priori. Thus, a-posteriori adaptive techniques are desirable to better capture the relevant flow features. Adaptive mesh algorithms represent a robust procedure improving the quality of the physical results, due to a local increase of the grid resolution at the price of an increased algorithmic complexity. The physics-based r-refinement, implemented within the COOLFluiD platform, consists in repositioning the mesh points according to a flow field variable, while keeping their number and connectivity frozen. The developed mesh refinement algorithms are based on spring networks mainly derivatives of linear spring analogy, the semi torsional spring analogy and the ortho-semi torsional spring analogy based on local physical and geometrical properties depending on a monitored variable. In the present paper, a concise overview of the mesh fitting techniques will be given, followed by some promising results of the physics-based r-refinement.
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