Project Title: Data-Driven RANS Modelling and Prediction of Turbulence Flow
Project description:
In simulations of turbulent fluid flows (a flow regime featured by fluctuations and chaotic-looking motion), a direct numerical solution of governing equations is highly computational-expensive and hardly feasible in most industrial projects. As a remedy, one can only solve the equations for the mean values of quantities and try to model the effect of fluctuations on the mean (so-called RANS method). Despite a large deal of effort devoted to physics-based models in the past, these models still face several limitations and shortcomings. With recent advances in the data-driven and, in particular, machine learning (ML) techniques and the broader availability of generated data in simulations of turbulent flows, there is a high potential that a well-trained ML algorithm can lead to superior modeling capabilities. In this project, the aforementioned potentials will be investigated to find out how much an ML model trained by high-fidelity data can improve the accuracy of low-fidelity approaches in turbulent flow simulations.
