Hybrid stochastic-structural modelling of particle-laden turbulent flows based on wavelet reconstruction

Le résumé fourni par la source

Reduced-order modelling and simulation of turbulent particle-laden flows is required in numerous configurations, where the whole spectrum of turbulent scales through DNS is out of reach. Whereas structural or stochastic models have be derived in order to provide a synthetic turbulent model for the non-resolved scales of the gaseous flow field, reproducing preferential concentration is challenging because it requires capturing both spatial and temporal correlations. We present a novel reduced-order framework that overcomes this limitation by combining wavelet-based structural modelling with stochastic evolution. Using compactly supported divergence-free wavelets within a multiresolution analysis, the method provides direct control over spatial structures and correlations of synthetic multiscale velocity fields. In particular, a dedicated procedure enables to enforce a prescribed turbulent energy spectrum despite the nonlocal contribution in Fourier space of the wavelet basis functions. The stochastic evolution of wavelet coefficients further ensures consistent temporal correlations. The proposed framework is evaluated in homogeneous isotropic turbulence under a fully reduced setting, where all turbulent scales must be provided by the model. Results show that it accurately reproduces preferential concentration across a wide range of Stokes numbers, achieving closer agreement with DNS data than classical Fourierbased kinematic simulations. This establishes a versatile and physically consistent turbulence model that combines structural fidelity with stochastic dynamics, offering a new tool to investigate particle–turbulence interactions.

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