Particulate Systems
Key features of the DEM model to analyse behaviour and interaction of particles in various small or large scale processes
Cohesive particles
Wet particles with liquid bridges
Liquid bridge force (capillary and viscous forces) model
Improved viscous force model for pendular liquid bridges
Newtonian and power-law fluids
[Video] Flow of dry (left) and wet (right) particles in a mixer
Improved liquid bridge viscous force model (Washino et al., 2017a and 2017b) for Newtonian fluid. Models are compared with Direct Numerical Simulation (DNS) results.
Improved liquid bridge viscous force model (Washino et al., 2018 and 2021) for power-law fluid. Models are compared with Direct Numerical Simulation (DNS) results.
Surface adhesion
Full (with negative overlap force) and simplified JKR models for adhesive contacts
Adhesive rolling friction model
[Video] Dry cohesive powder flow in a twin screw mixer
System simplification, computational time reduction
Scaled-up particle model
Particle size scaled-up to reduce number of particles for faster computation
All types of particle interaction forces (contact, cohesion etc.) are scaled accordingly to maintain system dynamics
[Video] Pile formation of dry cohesive particles with scaled-up particle model
Reduced particle stiffness (RPS) model for cohesive particles
Particle stiffness reduced (softer particle) to increase simulation time step while maintaining system dynamics
Wet particle velocities in mixer: solid line indicates the original particle stiffness while the red filled circles are results for reduced particle stiffness with RPS scaling
Dynamic load balancing
Dynamic adjustment of processor sub-domains sizes to balance number of particles and thus, computational cost evenly across processors
Particularly useful for systems with spatially-varying particle distribution
[Video] Particles falling on a chute simulation with dynamic load balancing
[Video] Particles falling on a chute simulation without dynamic load balancing