Transport modeling of sedimenting particles in a turbulent pipe flow using Euler-Lagrange large eddy simulation

TL;DR

This paper presents a comprehensive Euler-Lagrange large eddy simulation approach to model sedimenting particles in turbulent pipe flow, capturing complex particle-fluid interactions and predicting bed formation and critical deposition velocities.

Contribution

It introduces a novel simulation methodology combining dynamic Smagorinsky and immersed boundary methods for accurate turbulent slurry flow modeling.

Findings

Particle bed formation varies with flow rate.

Simulation accurately predicts critical deposition velocity.

Flow patterns and interface fluctuations characterized.

Abstract

A volume-filtered Euler-Lagrange large eddy simulation methodology is used to predict the physics of turbulent liquid-solid slurry flow through a horizontal pipe. A dynamic Smagorinsky model based on Lagrangian averaging is employed to account for the sub-filter scale effects in the liquid phase. A fully conservative immersed boundary method is used to account for the pipe geometry on a uniform cartesian grid. The liquid and solid phases are coupled through volume fraction and momentum exchange terms. Particle-particle and particle-wall collisions are modeled using a soft-sphere approach. A series of simulations have been performed by varying the superficial liquid velocity to be consistent with the experimental data by Dahl et al. (2003). Depending on the liquid flow rate, a particle bed can form and develop different patterns, which are discussed in the light of regime diagrams proposed in the literature. The fluctuation in the height of the liquid-bed interface is characterized to understand the space and time evolution of these patterns. Statistics of engineering interest such as mean velocity, mean concentration, and mean streamwise pressure gradient driving the flow are extracted from the numerical simulations and presented. Sand hold-up calculated from the simulation results suggest that this computational strategy is capable of accurately predicting critical deposition velocity.