The comparison of experimental data and results from numerical simulations - and thus the complete description of a physical process - is often difficult, since usually only two-dimensional information can be obtained from the experiments. Therefore, the proposed project aims to develop innovative modelling approaches that will provide a comprehensive picture of the physical phenomena under investigation. These are based on the interplay of direct numerical simulations, imaging experimental investigations and the the three-dimensional reconstruction of images based on artificial intelligence. A drop impacting on a smooth or structured surface with a possible disintegration can be understood as an exemplary case, where this new approach is applied. For this case, physical phenomena will be investigated and modelled. DNS not only provides a deep insight into the underlying physics and is the basis for the modelling of the process, it also allows the creation of huge data sets to train AI.
|Project Number||PN 6A-2|
|Project Name||Improving the predictability of interfacial phenomena - Description of complex drop-wall interactions in the interaction of experiments, direct numerical simulations and innovative modelling approaches based on artificial intelligence|
|Project Duration||January 2021 - December 2023|
|Project Leader||Kathrin Schulte
Jochen Kriegseis (KIT)
Alexander Stroh (KIT)
|Project Members||Jonathan Wurst, PhD Researcher|