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Data-Driven Adaptive Reynolds-Averaged Navier-Stokes K - Ω Models For Turbulent Flow-Field Simulations, Zhiyong Li
Data-Driven Adaptive Reynolds-Averaged Navier-Stokes K - Ω Models For Turbulent Flow-Field Simulations, Zhiyong Li
Theses and Dissertations--Mechanical Engineering
The data-driven adaptive algorithms are explored as a means of increasing the accuracy of Reynolds-averaged turbulence models. This dissertation presents two new data-driven adaptive computational models for simulating turbulent flow, where partial-but-incomplete measurement data is available. These models automatically adjust (i.e., adapts) the closure coefficients of the Reynolds-averaged Navier-Stokes (RANS) k-ω turbulence equations to improve agreement between the simulated flow and a set of prescribed measurement data.
The first approach is the data-driven adaptive RANS k-ω (D-DARK) model. It is validated with three canonical flow geometries: pipe flow, the backward-facing step, and flow around an airfoil. For all 3 test …