Machine learning-augmented turbulence models for the simulation of two-phase shear flows - Bertolotti, Luc (2022)
This dissertation focuses on the investigation of co-current two-phase stratified gas-liquid shearing flows with a sharp interface for industrial applications and more specifically for the study of flows present in aero-engine bearing chambers. Predicting the behaviour of shear flows in the different parts of an aero-engine, such as bearing chambers, is crucial. As a matter of fact, in the context of the thermal management of the bearing chamber, methodologies to predict the oil film thickness distribution must be investigated in order to optimise the system lubrication and prevent from oil coking or degradation. The thickness distribution of wavy films is largely analysed in the industry, but turbulent two-phase flows remain very challenging to predict depending on the case. Carrying out experiments is often too expensive and computational fluid dynamics (CFD) still struggles with two-phase flow prediction especially when it involves the modelling of a sharp interface. Many CFD methods are employed to predict the oil film thickness distribution and interface velocity. However, the vastly used standard Reynolds-averaged Navier-Stokes equations (RANS) turbulence models are derived from semi-empirical methods of turbulence damping, which are inaccurate for wavy films with high gradients of velocity across the interface, thus impacting flow modelling in bearing chambers.