It is widely acknowledged that the prediction of turbulent flows in the presence of separation is one of the most significant challenges in fluid dynamics.
Low cost simulation methods (RANS or even wall-modelled LES) allow for an extensive exploration of the design space, but suffer from lower reliability especially for separated and secondary flows. Improving model reliability will therefore have a major impact on energy consumption, emission and noise of aircraft, cars, and ships due to significant improvements in design.The financial consequences would likely reach billions of euros in savings of time-to-market and cost of the whole aircraft-design chain.
The objective of this proposal is to generate a high-fidelity DNS database on a representative and challenging configuration featuring flow separation and exploit the resulting data for improving LES wall-modelling through artificial intelligence and big data methodologies. This configuration is the HiFi-TURB DLR rounded step defined in a new section of the ERCOFTAC KB Wiki which is relevant for many industrial flows. It features a separation bubble of which the start and extent is highly dependent on the correct capture of turbulent momentum transfer upstream.
Université de Liège, Belgium;
Cenaearo, Belgium.