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Volumetric reconstruction of drop impact dynamics by means of color-coded glare points and deep learning

Dreisbach, Maximilian ORCID iD icon 1; Blessing, Sebastian 1; Stroh, Alexander [Beteiligte*r] 1; Kriegseis, Jochen [Beteiligte*r] ORCID iD icon 1
1 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.


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
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