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.
How to join:
The talk is held online via Jitsi. You can join with the link https://jitsi.uni-kl.de/SciCompSeminar_02. Please follow the rules below:
- Use a chrome based browser (One member with a different browser can crash the whole meeting).
- Mute your microphone and disable your camera.
- If you have a question, raise your hand.
More information is available at https://www.rhrk.uni-kl.de/dienstleistungen/netz-telefonie/konferenzdienste/jitsi/.
Referent:G. Suárez, AG Scientific Computing, TU Kaiserslautern https://www.scicomp.uni-kl.de/
Zeit: 11:30 Uhr
Ort: online
