Automotive

One of the main applications for my research is the automotive sector. I have always been interested in cars, particularly after working in Formula 1 for some time. The past 10 years has seen a gradual increase in the use of CFD in the design process. This is simply because it allows the designers and aerodynamicists the chance to evaluate new designs without building an actual model. This is particularly useful for UK car companies that typically travel to continental Europe to use wind-tunnels, because of the lack of a UK tunnel full-scale tunnel with rolling roads.

The drawback of CFD is that it is still not able to predict the aerodynamics forces (and particularly the aeroacoustics) to the accuracy that would be needed to completely remove wind tunnels. My research as shown in the paper below is about developing both new turbulence models as well as the software and numerics to allow car companies to move to an entirely digital process.

The main challenges are not only related to the physics e.g how to model the turbulence correctly but also how well the software can unitise high-performance computing so that high-fidelity simulations can run in days rather than weeks.

I work with one of the major automotive companies on developing new methods that can this dream a reality.

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This paper presents a comprehensive investigation of RANS and DES models for the Ahmed car body and a realistic automotive vehicle; the DrivAer model. A variety of RANS models, from the 1-equation Spalart Allmaras model to a low-Reynolds number Reynolds Stress model have shown an inability to consistently correctly capture the flow field for both the Ahmed car body and DrivAer model, with the under-prediction of the turbulence in the initial separated shear layer found as a key deficiency. It has been shown that the use of a hybrid RANS-LES model (in this case, Detached Eddy Simulation) offers an advantage over RANS models in terms of the force coefficients, and general flow field for both the Ahmed car body and the DrivAer model. However, for both cases even at the finest mesh level hybrid RANS-LES methods still exhibited inaccuracies. Suggestions are made on possible improvements, in particular on the use of embedded LES with synthetic turbulence generation. Finally the computational cost of each approach is compared, which shows that whilst hybrid RANS-LES offer a clear benefit over RANS models for automotive relevant flows they do so at a much increased cost.

Ashton et al. - 2016 - Assessment of RANS and DES methods for realistic automotive models