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A 3D stochastic model for geometrical characterization of particles in two-phase flow applications

Abstract : In this paper a new approach to geometrically model and characterize 2D silhouette images of two-phase flows is proposed. The method consists of a 3D modeling of the particles population based on some morphological and interaction assumptions. It includes the following steps. First, the main analytical properties of the proposed model – which is an adaptation of the Matérn type II model – are assessed, namely the effect of the thinning procedures on the population’s fundamental properties. Then, orthogonal projections of the model realizations are made to obtain 2D modeled images. The inference technique we propose and implement to determine the model parameters is a two-step numerical procedure: after a first guess of the parameters is defined, an optimization procedure is achieved to find the local minimum closest to the constructed initial solution. The method was validated on synthetic images, which has highlighted the efficiency of the proposed calibration procedure. Finally, the model was used to analyze real, i.e., experimentally acquired, silhouette images of calibrated polymethyl methacrylate (PMMA) particles. The population properties are correctly evaluated, even when suspensions of concentrated monodispersed and bidispersed particles are considered, hence highlighting the method’s relevance to describe the typical configurations encountered in bubbly flows and emulsions.
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Mathieu de Langlard, Fabrice Lamadie, Sophie Charton, Johan Debayle. A 3D stochastic model for geometrical characterization of particles in two-phase flow applications. Image Analysis and Stereology, International Society for Stereology, 2018, 37 (3), pp.233 à 247. ⟨10.5566/ias.1942⟩. ⟨hal-01949689⟩



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