Scientific Computing Seminar

Date and Place: Thursdays and hybrid (live in 32-349/online via Zoom). For detailed dates see below!

Content

In the Scientific Computing Seminar we host talks of guests and members of the SciComp team as well as students of mathematics, computer science and engineering. Everybody interested in the topics is welcome.

List of Talks

Event Information:

  • Thu
    05
    Nov
    2020

    SC Seminar: Rohit Pochampalli

    11:30Online

    Rohit Pochampalli, Chair for Scientific Computing (SciComp), TU Kaiserslautern

    Title: New Approaches in Data Driven Turbulence Modeling.

    Abstract:

    RANS equations combined with turbulence models perform adequately at lower computational cost than high fidelity methods. One equation turbulence models such as Spalart-Allmaras(SA), produce good results for specific applications, such as flows past wings and airfoils. However, the latter suffers from inaccuracies on quantities such as lift and drag in certain regimes, such as separated flows. A direct means to improve existing models is by introducing correction functionals onto terms of the model. For a specific flow configuration, these correction functionals can be estimated by inverse problems based on experimental measurements. Data driven techniques generalize this process by learning the correction functional in terms of flow variables, using data obtained from inverse problems.
    We present two new approaches that primarily address statistical aspects of turbulence modeling with machine learning. In particular, the correction functional varies depending on several factors including the flow conditions and topology. The modeling approaches taken up here reflect the circumstance that the data at hand does not conform to the classical assumptions of statistical learning theory. Consequently, the focus is on developing frameworks that can treat non-identically distributed data. The first approach introduces a means to reduce the dependency of data on flow domains. The second approach transforms the data into a high dimensional representation that captures the topology of the flow domain. Finally, the performance of the machine learning enhanced turbulence model is compared to the SA model.

    How to join:

    The talk is held online via Jitsi. You can join with the link https://jitsi.rptu.de//SciCompSeminar_01. 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://rz.rptu.de/dienstleistungen/netz-telefonie/konferenzdienste/jitsi/.