Volumetric reconstruction of drop impact dynamics by means of color-coded glare points and deep learning
Dreisbach, Maximilian
1; Blessing, Sebastian 1; Stroh, Alexander [Beteiligte*r] 1; Kriegseis, Jochen [Beteiligte*r]
1
1 Institut für Strömungsmechanik (ISTM), Karlsruher Institut für Technologie (KIT)
1; Blessing, Sebastian 1; Stroh, Alexander [Beteiligte*r] 1; Kriegseis, Jochen [Beteiligte*r]
11 Institut für Strömungsmechanik (ISTM), Karlsruher Institut für Technologie (KIT)
Abstract (englisch):
The present work introduces a method for the volumetric reconstruction of the gas-liquid interface of a droplet impinging on a solid surface from experimental image data by means of deep learning.
Differently colored glare points are used in order to encode three-dimensional information of the phase boundary on the image in addition to a standard shadowgraph.
It is demonstrated that this volumetric information can be successfully reconstructed into a three-dimensional representation of the droplet phase boundary by the proposed neural network.
Differently colored glare points are used in order to encode three-dimensional information of the phase boundary on the image in addition to a standard shadowgraph.
It is demonstrated that this volumetric information can be successfully reconstructed into a three-dimensional representation of the droplet phase boundary by the proposed neural network.
| Zugehörige Institution(en) am KIT | Institut für Strömungsmechanik (ISTM) |
| Publikationstyp | Vortrag |
| Publikationsdatum | 05.04.2023 |
| Sprache | Englisch |
| Identifikator | KITopen-ID: 1000164218 |
| Veranstaltung | 11th International Conference on Multiphase Flow (ICMF 2023), Kobe, Japan, 02.04.2023 – 07.04.2023 |
| Schlagwörter | shadowgraphy, glare points, drop impact, volumetric reconstruction, gas-liquid interface |