Simulation of Turbulent Flows by Deterministic and Statistical Model Coupling in a Dual-Grid System

Abstract

Despite the increasing computational power available to conduct turbulence-resolving Large Eddy Simulation (LES), it is still very expensive to simulate high Reynolds number flows as they appear in industrial problems. This thesis addresses the search for economic means to carry out LES by proposing a novel hybrid RANS/LES model, based on parallel LES and RANS simulations on two separate computational grids and a blended subgrid-scale model. The two-simulation framework enables the LES to be run on isotropic grids with low wall-parallel and wall-normal resolution, with the latter still being rare among present hybrid RANS/LES models. In contrast, the auxiliary RANS simulation is run on wall-refined grids with high-aspect ratio cells, where a full grid overlapping avoids complex boundary conditions. The seamless blending on the subgrid-scale level permits an unhindered movement of turbulent structures into and out of the RANS/LES subdomains. The Dual-Grid model is applied to a variety of wall-bounded flows with increasing flow complexity. Extensive testing on a heated plane channel flow for a range of Reynolds numbers overall shows a good prediction quality for velocity and turbulent stresses, although the grids are too coarse for conventional LES. Then, a periodic hill flow is simulated at different Reynolds numbers. Although the boundary layer separation is sensitive to the near-wall grid resolution and turbulence modelling, the model performance is competitive with other hybrid models on similar grid topologies. The Dual-Grid model is also demonstrated on a rib-roughened duct flow with a squared channel cross-section and high blockage ratio; a geometry usually found in turbine blade cooling applications. Again, the results are consistent with experimental data. Naturally, the computational cost is found to be higher than of the underlying LES on identical grids, yet, the prediction quality is increased. With possible improvements to the transfer of information across the simulations to reduce computational cost and to the heat transfer modelling, it is concluded that the proposed model has potential to become relevant to the industry.

Date of Award	1 Aug 2020
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Juan Uribe Torres (Supervisor), Dominique Laurence (Supervisor) & Imran Afgan (Supervisor)