Retrieving mean volumetric properties of multiphase flows from 2D images: A new approach combining deep learning algorithms and 3D modelling - Mines Saint-Étienne Access content directly
Journal Articles Chemical Engineering Science Year : 2023

Retrieving mean volumetric properties of multiphase flows from 2D images: A new approach combining deep learning algorithms and 3D modelling

Abstract

Measuring the morphological properties of complex multiphase systems is a crucial problem in many areas of science and industry and is particularly difficult in dense environments with limited optical access. This paper presents a new approach capable of extracting three-dimensional (3D) information from spherical particle systems based solely on two-dimensional (2D) projections of the system. Synthetic images of the system are generated using a stochastic geometrical model from a simulated 3D particle system with the same geometrical features as the studied system, which is projected into 2D images labeled with the appropriate 3D information. These images are then fed to a convolutional neural network (CNN) for training before being tested on synthetic and experimental images. Validation results show that this technique successfully predicts the mean features of the studied systems, even for dense environments with overlapping particles, with high computational efficiency.
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Dates and versions

emse-04245777 , version 1 (18-10-2023)

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Kassem Dia, Fabrice Lamadie, Johan Debayle. Retrieving mean volumetric properties of multiphase flows from 2D images: A new approach combining deep learning algorithms and 3D modelling. Chemical Engineering Science, 2023, 279, pp.118933. ⟨10.1016/j.ces.2023.118933⟩. ⟨emse-04245777⟩
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