Application and extension of a data-driven turbulence modeling method using Machine Learning

In recent years, many data-driven approaches which leverage high-fidelity reference data have been developed to augment the performance of Reynolds Averaged Navier–Stokes (RANS) turbulence models by providing an improved closure to the governing fluid flow equations. The goal of this M.Sc. thesis is to apply and extend one such data-driven...

Enhancement of data-driven turbulence models for wind turbine wake applications

Wind turbine wakes cause significant reductions in power production and increased fatigue damage for downwind turbines. Thus, they affect the wind levelized cost of energy. Computational Fluid Dynamics (CFD) can be used to quantify the wake characteristics, whereby Reynolds-averaged Navier-Stokes (RANS) has the most potential for industrial...

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...

Machine Learning for Data-Driven RANS Turbulence Modelling

The application of machine learning algorithms as data-driven turbulence modelling tools for Reynolds Averaged Navier-Stokes (RANS) simulations is presented. A novel machine learning algorithm, called the Tensor Basis Random Forest (TBRF) is introduced, which is able to predict the Reynolds stress anisotropy tensor. The algorithm is trained on...