Verbesserung der Vorhersagbarkeit von Grenzflächenphänomenen - Beschreibung komplexer Tropfen-Wand-Interaktionen im Zusammenspiel von Experimenten, Direkten Numerischen Simulationen und innovativen Modellbildungsansätzen auf Basis künstlicher Intelligenz

  • contact:

    Maximilian Dreisbach, Alexander Stroh, Jochen Kriegseis

  • funding:

    Friedrich und Elisabeth Boysen-Stiftung (BOY160)

  • Partner:

    Dr.-Ing. Kathrin Schulte & Jonathan Wurst (Institut für Thermodynamik der Luft- und Raumfahrt (ITLR), Universität Stuttgart)

  • startdate:

    January 2021

The comparison of experimental data and results from numerical simulations - and thus the full description of a physical process - is often difficult, since usually only two-dimensional information can be obtained from the experiments.  The project therefore aims to develop innovative modeling approaches that provide a more comprehensive picture of the physical phenomena under investigation.  These approaches are based on the interaction of Direct Numerical Simulations (DNS), optical measurements, and the reconstruction of experimentally obtained data using artificial intelligence (AI).  This combined approach is applied in order to better understand and subsequently describe the physical processes associated with the entrapment of a bubble in a droplet after its impact with a smooth or structured wall.