High-Fidelity Simulations of Adverse Pressure Gradient Flow over a Rounded Step for Turbulence Model Improvement via Database Generation

It is widely acknowledged (Slotnick et al., 2014) 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, such as Reynolds-Averaged Navier-Stokes (RANS) or even Wall-Modelled Large Eddy Simulations (WMLES) 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, mission and noise of aircraft, cars, and ships due to significant improvements in design. The objective of this research is to generate a high-fidelity database on a representative and challenging configuration featuring flow separation. The resulting data can then be exploited to improve RANS and WMLES through machine learning and data-driven methodologies. The considered configuration is the HiFi-TURB DLR rounded step defined in Alaya, Grabe, and Eisfeld (2022) and Alaya and Grabe (2023). It has been designed to investigate the effect of an adverse pressure gradient on a turbulent boundary layer, which is relevant for many industrial flows. It features a separation bubble of which the start and extent are highly dependent on the correct capture of turbulent momentum transfer upstream.