Calibration of DEM simulations for dynamic particulate systems

Abstract

Calibration and validation represent crucial but often-overlooked ingredients in the successful application of discrete element method (DEM) simulations. Without rigorous calibration/validation protocols, the results of DEM simulations can be imprecise or even unphysical, yet all too often the methods used by practitioners are at best cursory, and at worst entirely absent. As the particle-handling industries show an increasing interest in DEM, it is vital that this issue be resolved lest a potentially powerful tool be written off by industry as unreliable. In this work, we provide a concise overview of contemporary methods used in the calibration and validation of DEM simulations of powder flows, providing practical insights into their strengths and weaknesses, and ideas for manners in which they may be improved and/or rendered more easily adoptable in the future.

Numerical Modelling and Imaging of Industrial-Scale Particulate Systems: A Review of Contemporary Challenges and Solutions

“…By repeating the above process again and again-effectively stepping through time in pseudo-infinitesimal increments-one may thus simulate the motion of a system of particles. By tuning the relevant parameters (friction coefficients, restitution coefficients, cohesive properties...) used to represent the interactions between particles, this simulated motion can be brought into quantitative alignment with the dynamics of the real system being modelled (Windows-Yule et al, 2016;Windows-Yule and Neveu, 2022) 2 . A significant shortcoming of the discrete element method, however, is its computational expense.…”

Fluidized Bed Scale-Up for Sustainability Challenges. 1. Tomorrow’s Tools

“…For instance, fluidization and defluidization tests have been used to calibrate drag models . Rheological and flow testing can be used to validate the collisional stress model. , …”

“…160 Rheological and flow testing can be used to validate the collisional stress model. 93,161 Jiang et al 136 gave an example using a neural network ML tool for optimizing a filter drag constitutive model for fTFM (filtered two-fluid model), which for gas-particle flows require closures for the subfilter scale corrections to interphase drag force and stresses, the former being more significant. It allowed them to verify that an algebraic drift flux model can be used to capture the micromixing observed in the highly resolved, fine- grid simulations.…”

Reviewing the Impact of Powder Cohesion on Continuous Direct Compression (CDC) Performance

“…When there is resistance to this gradient of increasing powder-to-wall interactions, it suggests there is a lack of flowability, which commonly manifests in the form of cohesivity. Though cohesion is ultimately a microscopic process (i.e., it occurs at the scale of particle-particle contacts), at present its effects are predominantly characterised via macroscopic (bulk) measurements [ 48 ].…”