ER 3

Multiscale analysis of DEM data to enhance the prediction at system scale


The focus of this research project is to perform multiscale analysis of Discrete Element Method (DEM) data to enhance the prediction at system scale, especially focus on particulate processes involved with particle sized change such as milling and granulation.

Population balance model (PBM) has been used extensively to track size distribution of particles related to the aggregation and breakage phenomenon, but its accuracy strongly relies on the description of aggregation and breakage rate kernels which are usually empirical at present.  On the other hand, DEM is now a popular simulation tool to help understand the fundamental of granular material. DEM can provide particle-scale information such as collision rates and velocity distribution considering different material properties and equipment geometries. The aim of this project is to perform multiscale analysis of microscale DEM simulation results to inform PBM prediction of large scale systems.

In this project, DEM simulations at micro to meso scale will be conducted firstly to obtain the key parameters needed by PBM. Finally, a two-way PBM-DEM coupling framework will be further explored. The simulation results based on the multiscale model will also be calibrated and validated for industrial scale experiments

Xizhong Chen


Xizhong Chen received his Bachelor degree in chemical engineering from Xiamen University in 2010. Later, he joined the EMMS group to start his postgraduate research in Institute of Process Engineering, Chinese Academy of Sciences (IPE, CAS). He completed his Ph.D in chemical engineering in June 2016 and now works at University of Edinburgh. His past research focus on developing a spatio-temporally coupled hybrid multi-scale model in an attempt to bridge the application gap between the Eulerian based continuum model and the Lagrangian based discrete model for granular flows and gas-solid flows.

Contact details

Alexander Graham Bell Building

The King's Buildings,

The University of Edinburgh


Xizhong Chen