A new paradigm for computing hydrodynamic forces on particles in Euler-Lagrange point-particle simulations

TL;DR

This paper introduces a novel force correlation framework for Euler-Lagrange particle simulations that improves hydrodynamic force predictions by using volume-filtered quantities, reducing errors especially in dense suspensions.

Contribution

It proposes a new method leveraging volume-filtered data to accurately compute hydrodynamic forces without estimating undisturbed flow fields, validated through analytical and numerical benchmarks.

Findings

Accurately predicts drag forces in single particle and dense suspensions.

Shows improved agreement with high-fidelity simulations over existing correlations.

Enhances simulation fidelity without high computational costs.

Abstract

Accurate prediction of the hydrodynamic forces on particles is central to the fidelity of Euler-Lagrange (EL) simulations of particle-laden flows. Traditional EL methods typically rely on determining the hydrodynamic forces at the positions of the individual particles from the interpolated fluid velocity field, and feed these hydrodynamic forces back to the location of the particles. This approach can introduce significant errors in two-way coupled simulations, especially when the particle diameter is not much smaller than the computational grid spacing. In this study, we propose a novel force correlation framework that circumvents the need for undisturbed velocity estimation by leveraging volume-filtered quantities available directly from EL simulations. Through a rigorous analytical derivation in the Stokes regime and extensive particle-resolved direct numerical simulations (PR-DNS) at finite Reynolds numbers, we formulate force correlations that depend solely on the volume-filtered fluid velocity and local volume fraction, parametrized by the filter width. These correlations are shown to recover known drag laws in the appropriate asymptotic limits and exhibit a good agreement with analytical and high-fidelity numerical benchmarks for single particle cases, and, compared to existing correlations, an improved agreement for the drag force on particles in particle assemblies. The proposed framework significantly enhances the accuracy of hydrodynamic force predictions for both isolated particles and dense suspensions, without incurring the prohibitive computational costs associated with reconstructing undisturbed flow fields. This advancement lays the foundation for robust, scalable, and high-fidelity EL simulations of complex particulate flows across a wide range of industrial and environmental applications.