G. Suárez Martinez(Chair for Scientific Computing, University of Kaiserslautern)
hosted by Seminar Series on Scientific Computing
"Turbulence Modeling: An inverse problem"
The numerical simulation of turbulence using the Reynolds-Averaged Navier-Stokes (RANS) equations by considering Boussinesq' eddy viscosity assumption is a well established mathematical model in both, industrial applications and research. However, this assumption, derived at the end of 19th century, presents severe limitations even for simple shear flows, causing major errors on the results. To compensate for these deficiencies, new turbulence models have been proposed.
Traditional development of turbulence models relied on mathematical fundamentals as well as on empirical results. Nowadays, thanks to the outstanding progress in machine learning methods and the easy availability to computers, an alternative approach for turbulence modeling is to use high fidelity data.
In this presentation we will discuss a new methodology based on data-driven methods to improve the capabilities of RANS equations, more in precisely, to overcome for the constrains induced by the Boussinesq' approximation.
|Time:||Thursday, 12.11.2020, 11:30|