Sayed, M. A.Dehbi, A.Hadžiabić, M.Ničeno, B.Mikityuk, K.2022-08-242022-08-242022-08-242022-08-1610.1007/s10494-022-00356-4https://infoscience.epfl.ch/handle/20.500.14299/190228Particulate flow in closed space is involved in many engineering applications. In this paper, the prediction of particle removal is investigated in a thermally driven 3D cavity at turbulent Rayleigh number Ra = 1E9 using Coarse Large Eddy Simulation (CLES). The depletion dynamics of SiO2 aerosol with aerodynamic diameters between 1.4 and 14 µm is reported in an Euler/Lagrange framework. The main focus of this work is therefore to assess the effect of the subgrid-scale motions on the prediction of the particulate flow in a buoyancy driven 3D cavity flow when the mesh resolution is coarse and below optimal LES standards. The research is motivated by the feasibility of modeling more complex particulate flows with reduced CPU cost. The cubical cavity of 0.7 m side-length is set to have a temperature difference of 39 K between the two facing cold and hot vertical walls. As a first step, the carrier fluid flow was validated by comparing the first and second-moment statistics against both previous well-resolved LES and experimental databases [Kalilainen (J. Aero Sci. 100:73–87, 2016); Dehbi (J. Aero. Sci. 103:67–82, 2017)]. First moment Eulerian statistics show a very good match with the reference data both qualitatively and quantitatively, whereas higher moments show underprediction due to the lesser spatial resolution. In a second step, six particle swarms spanning a wide range of particle Stokes numbers were computed to predict particle depletion. In particular, predictions of 1.4 and 3.5 µm particles were compared to LES and available experimental data. Particles of low inertia i.e. dp < 3.5 µm are more affected by the SGS effects, while bigger ones i.e. dp = 3.5–14 µm exhibit much less grid-dependency. Lagrangian statistics reported in both qualitative and quantitative fashions show globally a very good agreement with reference LES and experimental databases at a fraction of the CPU power needed for optimal LES.CFDTurbulence modelingHeat transferBuoyancy-driven fowsNatural convectionCoarse LES (CLES)Diferentially heated cavity (DHC)Particle removal efficiencyBoussinesq approximationLagrangian particle trackingtext::journal::journal article::research article