Drag correlation for dilute and moderately dense fluid-particle systems using the lattice Boltzmann method

Abstract

This paper presents a numerical study of flow through static random assemblies of monodisperse, spherical particles. A lattice Boltzmann approach based on a two relaxation time collision operator is used to obtain reliable predictions of the particle drag by direct numerical simulation. From these predictions a closure law $F(Re,\phi )$ of the drag force relationship to the bed density $\phi$ and the particle Reynolds number $Re$ is derived. The present study includes densities $\phi$ ranging from $0.01$ to $0.35$ with Re ranging up to $300$, that is compiled into a single drag correlation valid for the whole range. The corelation has a more compact expression compared to others previously reported in literature. At low particle densities, the new correlation is close to the widely used Wen & Yu - correlation. Recently, there has been reported a discrepancy between results obtained using different numerical methods, namely the comprehensive lattice Boltzmann study of Beetstra et al. (2007) and the predictions based on an immersed boundary - pseudo-spectral Navier-Stokes approach (Tenneti et al., 2011). The present study excludes significant finite resolution effects, which have been suspected to cause the reported deviations, but does not coincide exactly with either of the previous studies. This indicates the need for yet more accurate simulation methods in the future.

Preprint submitted to Elsevier. Comments welcome!

Cited by in corpus (11)

• Massively Parallel Algorithms for the Lattice Boltzmann Method on Non-uniform Grids
• A comparative study of fluid-particle coupling methods for fully resolved lattice Boltzmann simulations
• Towards Particle-Resolved Accuracy in Euler-Lagrange Simulations of Multiphase Flow Using Machine Learning and Pairwise Interaction Extended Point-particle (PIEP) Approximation
• Physics-inspired architecture for neural network modeling of forces and torques in particle-laden flows
• Machine Learning for Physics-Informed Generation of Dispersed Multiphase Flow Using Generative Adversarial Networks
• Point-particle drag, lift, and torque closure models using machine learning: hierarchical approach and interpretability
• Particle-resolved simulation of freely evolving particle suspensions: Flow physics and modeling
• Direct simulation of liquid-gas-solid flow with a free surface lattice Boltzmann method
• Fully resolved simulation of dense suspensions of freely evolving buoyant particles using an improved immersed boundary method
• A Scalable Multiphysics Algorithm for Massively Parallel Direct Numerical Simulations of Electrophoresis
• A Data-Driven Approach for Predicting Hydrodynamic Forces on Spherical Particles Using Volume Fraction Representations