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...

Equivalent Fluid Modelling of a Honeycomb Acoustic Liner using the Acoustic Porous Medium Formulation

Over the past decades, numerous methods have been used to study acoustic liners through their impedance, of which numerical methods are of particular interest. Despite their advantages, numerical methods are bounded by certain drawbacks. One such drawback stems from the need to accurately discretize the liner geometry, which can lead to large...