A stochastic model for the hydrodynamic force in Euler--Lagrange simulations of particle-laden flows

TL;DR

This paper introduces a stochastic force model for Euler--Lagrange simulations that captures neighbor-induced drag fluctuations, improving predictions of particle velocity variance in particle-laden flows.

Contribution

The paper develops a Langevin-based stochastic drag force model that accounts for higher-order force statistics in EL simulations, enhancing accuracy over traditional mean-based models.

Findings

The stochastic model accurately predicts particle velocity variance across various flow conditions.

Standard EL models under-predict velocity variance and are sensitive to projection bandwidth.

The new model's predictions are insensitive to projection bandwidth, unlike standard approaches.

Abstract

Standard Eulerian--Lagrangian (EL) methods generally employ drag force models that only represent the mean hydrodynamic force acting upon a particle-laden suspension. Consequently, higher-order drag force statistics, arising from neighbor-induced flow perturbations, are not accounted for; with implications on predictions for particle velocity variance and dispersion. We develop a force Langevin (FL) model that treats neighbor-induced drag fluctuations as a stochastic force within an EL framework. The stochastic drag force follows an Ornstein-Uhlenbeck process and requires closure of the integral time scale for the fluctuating hydrodynamic force and the standard deviation in drag. The former is closed using the mean-free time between successive collisions, derived from the kinetic theory of non-uniform gases. For the latter, particle-resolved direct numerical simulation (PR--DNS) of fixed particle assemblies is utilized to develop a correlation. The stochastic EL framework specifies unresolved drag force statistics, leading to the correct evolution and sustainment of particle velocity variance over a wide range of Reynolds numbers and solids volume fractions when compared to PR--DNS of freely-evolving homogeneous suspensions. By contrast, standard EL infers drag statistics from variations in the resolved flow and thus under-predicts the growth and steady particle velocity variance in homogeneous suspensions. Velocity statistics from standard EL approaches are found to depend on the bandwidth of the projection function used for two-way momentum coupling, while results obtained from the stochastic EL approach are insensitive to the projection bandwidth.