A data-driven machine learning framework to model turbulence modulation and preferential concentration by particles

Abstract

Particle-laden turbulent flows appear across a wide range of engineering and environmental systems, from spray combustion and aerosol dispersion to sediment transport and industrial multiphase reactors. Yet accurately predicting particle behaviour within these flows remains a persistent challenge, particularly in resolving how particle inertia couples to turbulent flow structures and how that coupling can be retained at computational scales where full resolution is prohibitive. This dissertation addresses both challenges through two connected studies. The first study establishes a quantitative framework linking particle inertial properties to the topological features of homogeneous isotropic turbulence that govern preferential concentration. Using high-fidelity DNS with two-way Eulerian–Lagrangian coupling on a 1283 grid at Reλ = 120, we systematically investigate the influence of the Stokes number St and the particle-to-fluid density ratio ρp/ρf on particle clustering. The centrifuge mechanism is quantitatively confirmed: clustering intensity peaks at St = 1, where particles are most effectively expelled from vortical regions and accumulate in straindominated zones identified by the Q-criterion. A non-monotonic dependence on density ratio is observed, with the maximum correlation between Q and particle number density occurring at ρp/ρf = 500, reflecting an optimal balance between inertial decoupling and fluid responsiveness. A split-correlation analysis further reveals that vortex exclusion is a robust and nearly universal mechanism, whereas strain-field accumulation exhibits stronger sensitivity to particle inertia and density. The relatively weak magnitude of the overall linear correlations demonstrates that the Q-criterion alone is insufficient to fully characterize preferential concentration, indicating that turbulence intermittency, multiscale particle–structure interactions, and trajectory memory effects also play important roles. The second study builds on these physical insights by developing a machine-learningenhanced framework for particle-laden turbulence simulations. The objective is to recover the small-scale velocity structures that are removed by filtering operations in coarseresolution simulations but are essential for accurately predicting inertial particle dynamics. To achieve this, a multilayer perceptron (MLP) model is trained using DNS data from the first study to reconstruct subgrid-scale velocity corrections from filtered flow fields. The reconstructed velocity field restores a substantial portion of the gradient intensity and high-wavenumber energy content that is absent in the filtered representation. The reconstructed field is then used to drive an Eulerian–Lagrangian particle-tracking solver with full two-way coupling, allowing particle drag forces to be consistently returned to the carrier phase as momentum source terms. Because particles evolve within a velocity field that contains reconstructed small-scale structures, the simulation recovers key features of particle-turbulence interaction that are typically suppressed in filtered simulations. In particular, the ML-enhanced framework restores stronger preferential concentration, more realistic particle slip velocities, and spatially intermittent particle source terms associated with strain-dominated regions of the flow. Together, the two studies form a coherent progression. The first establishes the physical mechanisms governing particle clustering and identifies the flow-topology conditions under which preferential concentration occurs. The second demonstrates that these critical small-scale flow structures can be reconstructed using a data-driven model, enabling improved particle tracking and two-way coupling predictions in simulations where full DNS resolution is not computationally feasible. The combined framework therefore provides a computationally efficient pathway toward higher-fidelity prediction of particle-laden turbulent flows, with direct relevance to industrial and environmental multiphase systems.