Using deep learning to retrieve 3D geometrical characteristics of a particle field from 2D projected images: Application to multiphase flows - Mines Saint-Étienne Access content directly
Conference Papers Year : 2022

Using deep learning to retrieve 3D geometrical characteristics of a particle field from 2D projected images: Application to multiphase flows

Abstract

The main part of recycling processes are carried out in chemical engineering reactors that involve multiphase flows with dense dispersed phase. In a study and modeling approach of these processes, the description and characterization of hydrodynamic phenomena is crucial. A variety of techniques allows us to realize this type of measurement, but the most used one is the direct imaging associated with an efficient image processing. Recently, deep learning algorithms have proven to be very effective in solving image based problems, which led to the use of these algorithms to extract critical information in chemical engineering apprentices. The method employed in this paper relies on a deep learning based algorithm dedicated to the prediction of 3D features of multiphase flows using 2D projected images. The performance of the method has been evaluated both on synthetic images and on real images of beads in a dispersed phase.
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Dates and versions

emse-03879283 , version 1 (18-10-2023)

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Kassem Dia, Fabrice Lamadie, Johan Debayle. Using deep learning to retrieve 3D geometrical characteristics of a particle field from 2D projected images: Application to multiphase flows. ICPRS - 12th International Conference on Pattern Recognition Systems, Mines Saint-Étienne (France), Jun 2022, Saint-Étienne, France. pp.1 à 7, ⟨10.1109/ICPRS54038.2022.9854059⟩. ⟨emse-03879283⟩
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