Extension of evaluation methods and closure models for bubbly flows with the aid of machine learning methods

• Applicant: Dr.-Ing. Hendrik Hessenkemper
• Subject Area: Fluid Mechanics
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 516836175

The simulation of liquid flows with dispersed gas bubbles requires an exact modelling of relevant bubble forces in order to be able to reliably reproduce the characteristics of the flow and all associated variables, such as heat and mass transport. For ellipsoidal gas bubbles, which occur in a large number of technical applications, the various bubble forces have only been investigated for certain flow conditions and a generally valid description is not yet possible. So-called swarm effects, which occur in the case of a dense gas bubble loading and possibly modify the bubble forces, have been investigated even less. This project serves the experimental investigation of bubble forces of ellipsoidal bubbles. In addition to an improved understanding of the physical processes and interactions in single bubble experiments, swarm effects will be characterised in additional experiments with increased gas fraction. In order to be able to evaluate bubbly flows with increased gas fraction, current evaluation methods will be extended using image sequences and/or multiple views. To uses these approaches for a better bubble identification, in particular novel methods from the field of machine learning will be used. The results of the project should contribute to improve modelling of bubbly flows, which will extend the accuracy and reliability of corresponding CFD simulations.

• DFG Programme: Research Grants