Inferring algebraic stress models from high-fidelity data and applying machine learning to enhance and generalize turbulence lag models

Adverse pressure gradients, separation and other forms of non-equilibrium flows are often encountered in flows of interest. In these type of flows, the Boussinesq hypothesis does not hold and often leads to erroneous predictions by eddy viscosity models. In an attempt to capture these non-equilibrium effects, lag parameter models introduce a lag...

A Machine Learning Approach to Unresolved-Scale Modeling for Burgers’ Equation

This thesis is part of a greater effort to use machine learning for the development of flexible and universal unresolved-scale models in large eddy simulation (LES). The novelty in the current work is that a neural network learns to predict the integral forms of the unresolved-scale terms directly without a priori assumptions on the underlying...

Field inversion and machine learning in turbulence modeling

Turbulence closure models will continue to be necessary in order to perform computationally affordable simulations in the foreseeable future. It is expected that Reynolds-averaged Navier-Stokes (RANS) turbulence models will still be useful with the further development of the more accurate, but computationally expensive large eddy simulation (LES...