DEM and CFD–DEM meso-particle modelling of mixing processes

Balázs Füvesi

Host Institutions

University of Twente [ 12 months ]
DCS Computing [ 24 months ]


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I am Balázs Füvesi from Hungary. I graduated as a chemical and process engineer from University of Pannonia. I have been involved in DEM simulations since the middle of my bachelor studies when I joined a research program for students and simulated silo discharge with a rotary valve. Later I continued in this field and during my master thesis I developed a GPU accelerated DEM simulation software.

My project title within the Tusail consortium is DEM and CFD-DEM meso-particle modelling of mixing processes. Mixing and segregation processes of granular materials are important and common steps in different industrial technologies such as mining, agriculture, chemical and pharmaceutical industry. The discrete element method (DEM) is a widely used modelling technique to simulate granular materials and particulate flows. DEM treats the granular material as discrete particles and calculates their motion individually. Due to the large number of particles in industrial scale, the discrete approach has a high computational demand, making its usage infeasible for industrial scale applications. One of the possible solutions is meso-particles or coarse graining applied to DEM, which is a discrete to discrete scale-up method. By using this scaling method, the number of simulated particles can be reduced and thus the computational load decreased. Coarse graining is a widely researched area of DEM however, it is still not well understood how the upscaling of particle sizes with coarse graining affects the mixing and segregation processes.

Project Description

Develop a meso-particle model to predict segregation and mixing behaviour of large industrial mixing systems.

Specific objectives are:
  1. Develop spatial and temporal filters to derive sub-parcel models and/or correlations to correct for the impact of the non-resolved part of the physics in meso-particle simulations; address the fundamental challenge of modelling the micromechanics to predict mixing and segregation;
  2. Develop DEM and CFD–DEM based simulation methodology using meso-particle scale-up and compare to fully resolved DEM models;
  3. Apply to selected large industrial mixers at BASF/Procter & Gamble and validate predictive capabilities.
Expected Results:
  1. Literature study and existing model evaluation; choice of methods;
  2. Report simulation data and proposed correlations laws for use in sub-parcel models for meso-particle modelling;
  3. Sub-parcel models for meso-particle modelling of segregation and mixing established, calibrated and validated;
  4. Industrial application (mixing system) established and validated.

Research Output