Graduate Thesis Or Dissertation


Anisotropic Mesh Adaptivity for Turbulent Flows with Boundary Layers Public Deposited

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  • Turbulent flows are found everywhere in nature and are studied, analyzed and simulated using various experimental and numerical tools. For computational analysis, a variety of turbulence models are available and the accuracy of these models in capturing the phenomenon depends largely on the mesh spacings, especially near the walls, in the boundary layer region. Special semi-structured meshes called "mesh boundary layers" are widely used in the CFD community in simulations of turbulent flows, because of their graded and orthogonal layered structure. They provide an efficient way to achieve very fine and highly anisotropic mesh spacings without introducing poorly shaped elements. Since usually the required mesh spacings to accurately resolve the flow are not known a priori to the simulations, an adaptive approach based on a posteriori error indicators is used to achieve an appropriate mesh. In this study, we apply the adaptive meshing techniques to turbulent flows with a focus on boundary layers. We construct a framework to calculate the critical wall normal mesh spacings inside the boundary layers based on the flow physics and the knowledge of the turbulence model. This approach is combined with numerical error indicators to adapt the entire flow region. We illustrate the effectiveness of this hybrid approach by applying it to three aerodynamic flows and studying their superior performance in capturing the flow structures in detail. We also demonstrate the capabilities of the current developments in parallel boundary layer mesh adaptation by applying them to two internal flow problems. We also study the application of adaptive boundary layer meshes to complex geometries like multi element wings. We highlight the advantage of using such techniques for superior wake and tip region resolution by showcasing flow results. We also outline the future direction for the adaptive meshing techniques to be useful to the large scale flow computations.
Date Issued
  • 2013
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Last Modified
  • 2019-11-14
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