Current automotive aerodynamic design is driven by a myriad of requirements including minimum drag, minimum noise, favourable handling, thermal management and vehicle soiling. Our group have developed new approaches coupling steady Reynolds-Averaged Navier-Stokes methods with unsteady turbulence resolving methods such as Large-Eddy-Simulation. These are implemented in our very-high order accurate compressible in-house solver, permitting computations at flow speeds of interest to industry. This PhD will focus on the further development and application of this hybrid method to understand the fundamentals of automotive aerodynamics (particularly base-flows) and aero-acoustic predictions.
The FLuD group has ongoing collaborations with industry and international Universities to push forward the state of the art in modelling of fluid flow around generic cars. Two recent problems have focussed on the flow physics of large scale separations at the rear of a car which substantially impact the overall drag. This involves the application of very high order accurate numerical methods and new unsteady turbulence models to resolve the time dependent evolution of the base flow. To date, these studies have been conducted in collaboration with Jaguar Land Rover, UK. This PhD will take the existing solver and further improve the hybrid RANS-LES approach targeting practical Reynolds numbers, with aeroacoustics as a particular focus. Initial computations will be undertaken with the existing solver in comparison to experimental results from Hyundai detailing noise measurements around a simplified automotive body. Your role would be to derive and implement novel hybrid RANS-LES blending functions which are flow adaptive, requiring minimal a priori knowledge in particular about the boundary layers around the vehicle. The PhD will combine large scale computations on HPC systems with advanced numerical methods, in a collaboration with industry, to provide a fundamental understanding of noise production. In the later years, noise mitigation will be explored.
The opportunity ID for this research opportunity is 2273