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Particle agglomeration in flows: fast data-driven spatial decomposition algorithm for CFD simulations

Kerlyns Martínez Rodríguez 1, 2, * Mireille Bossy 1 Christophe Henry 1 
* Corresponding author
1 CALISTO - Stochastic Approaches for Complex Flows and Environment
CEMEF - Centre de Mise en Forme des Matériaux, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Computational fluid dynamics simulations in practical industrial/environmental cases often involve non-homogeneous concentrations of particles. In Euler-Lagrange simulations, this can induce the propagation of numerical error when the number of collision/agglomeration events is computed using mean-field approaches. In fact, mean-field statistical collision models allow to sample the number of collision events using a priori information on the frequency of collisions (the collision kernel). Yet, since such methods often rely on the mesh used for the Eulerian simulation of the fluid phase, the particle number concentration within a given cell might not be homogeneous, leading to numerical errors. In this article, we apply the data-driven spatial decomposition (D2SD) algorithm to control such error in simulations of particle agglomeration. Significant improvements are made to design a fast D2SD version, minimizing the additional computational cost by developing re-meshing criteria. Through the application to some practical simulation cases, we show the importance of splitting the domain when computing agglomeration events in Euler/Lagrange simulations, so that within each elementary cell there is a spatially uniform distribution of particles.
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Submitted on : Tuesday, January 11, 2022 - 7:04:32 PM
Last modification on : Tuesday, May 31, 2022 - 11:38:02 AM


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Kerlyns Martínez Rodríguez, Mireille Bossy, Christophe Henry. Particle agglomeration in flows: fast data-driven spatial decomposition algorithm for CFD simulations. International Journal of Multiphase Flow, Elsevier, 2022, 149, pp.103962. ⟨10.1016/j.ijmultiphaseflow.2021.103962⟩. ⟨hal-03180740v2⟩



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