Numerical Investigation of Particle Preferential Concentration in HomogeneousTurbulence Using DNS

This study establishes a quantitative relationship between particle physics, specifically particle inertia and density, and the topological features of the underlying turbulent flow that govern preferential concentration. Using high-fidelity Direct Numerical Simulations (DNS) with two-way coupling in an Eulerian-Lagrangian framework, we investigate how the Stokes number (St_k) and particle-to-fluid density ratio (ρp/ρf) dictate particle clustering within homogeneous, isotropic turbulence. We analyze particleladen flows in an Eulerian-Lagrangian framework with a uniform mesh resolution of 128^3 and a particle volume fraction of 10E-3.The simulations isolate the effects of density ratio at a fixed Particle Stokes number (St = 1) and explore the influence of varying St_k tounderstand the interplay between particle inertia and turbulent structures.Our results reveal that particle accumulation is directly controlled by the interplay between inertial parameters and flow topology. We observe a non-monotonic relationship with density, where maximum correlation occurs at ρp/ρf = 500, indicating an optimal inertia for interaction with specific flow structures. Furthermore, the centrifuge mechanism is quantitatively confirmed, with clustering intensity peaking at St_k=1 as particles are most effectively ejected from vortical regions (identified by positive Q-criterion) and accumulate in strain-dominated zones. This topological segregation is visually evident in instantaneous particle snapshots and quantified by Radial Distribution Function (RDF) analysis.While the inverse correlation between particle number density and vortical structures aligns with theoretical expectations, its weak magnitude suggests that conventional topology metrics like the Q-criterion alone are insufficient to fully quantify the phenomenon. This points to the significant influence of additional, complex factors such as turbulence intermittency and transient structural interactions. By directly linking particle inertial properties to their spatial organization within the flow’s topological skeleton, this study provides a foundational framework for developing more accurate, physics-based models of particle-laden turbulence.