Dr Neil Ashton chairs and presents at Computational Fluid Dynamics seminar

Dr Neil Ashton chairs and presents at Computational Fluid Dynamics seminar

The Centre's Computational Fluid Dynamics expert Dr Neil Ashton recently chaired and presented at a seminar organised jointly with NAFEMS, the International Association for the Engineering Modelling, Analysis and Simulation Community.

The event, which was aimed at Computational Fluid Dynamics (CFD) academics and practitioners and those interested in applying CFD to complex real-life problems, focused on high-fidelity methods such as pure LES and hybrid RANS-LES methods. It offered delegates a better understanding of the theory behind high-fidelity methods as well as industrial examples of their real-life use.

As well as chairing the seminar in Stratford-upon-Avon on 16 November, Dr Ashton, who is a member of the NAFEMS CFD working group, presented on Hybrid RANS-LES – Automatic Mesh Refinement (AMR) and HPC considerations. He was joined by speakers including Dr Tom Grimble from Dyson, Dr Juan Uribe of EDF Energy and Dr Nicolas Oettle of Jaguar Land Rover.

For many companies there is a growing desire to improve the accuracy of their CFD simulations, in order to improve correlation to experiments and to allow more of their design to be undertaken in CFD. This desire for greater accuracy has led many to look beyond traditional Reynolds Averaged Navier-Stokes (RANS) approaches for turbulence to Large Eddy Simulation (LES), which can provide a much better alternative for unsteady flows. However they do so at a much higher cost - so much higher that, for high-Reynolds numbers flow, these costs are often too great for general purpose calculations.

This seminar focused on these high-fidelity methods. With expert speakers representing some of the key engineering sectors (automotive, aerospace, oil & gas, nuclear), practical examples of the methods were presented, offering attendees a chance to understand and question if these methods could be applied to their own simulations.