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{{distinguish|finite element method}}
{{More citations needed|date=November 2019}}
A '''discrete element method''' ('''DEM'''), also called a '''distinct element method''', is any of a family of [[numerical analysis|numerical]] methods for computing the motion and effect of a large number of small particles. Though DEM is very closely related to [[molecular dynamics]], the method is generally distinguished by its inclusion of rotational [[Degrees of freedom (statistics)|degrees-of-freedom]] as well as stateful contact and often complicated geometries (including polyhedra). With advances in computing power and numerical algorithms for nearest neighbor sorting, it has become possible to numerically simulate millions of particles on a single processor. Today DEM is becoming widely accepted as an effective method of addressing engineering problems in granular and discontinuous materials, especially in granular flows, powder mechanics, and rock mechanics. DEM has been extended into the [[Extended Discrete Element Method]] taking [[heat transfer]],<ref name="Peng">{{cite journal |last1=Peng |first1=Z. |last2=Doroodchi |first2=E. |last3=Moghtaderi |first3=B. |date=2020 |title=Heat transfer modelling in Discrete Element Method (DEM)-based simulations of thermal processes: Theory and model development |journal=Progress in Energy and Combustion Science |volume=79,100847 |page=100847 |doi=10.1016/j.pecs.2020.100847}}</ref> [[chemical reaction]]<ref name="Papadikis">{{cite journal |last1=Papadikis |first1=K. |last2=Gu |first2=S. |last3=Bridgwater |first3=A.V. |date=2009 |title=CFD modelling of the fast pyrolysis of biomass in fluidised bed reactors: Modelling the impact of biomass shrinkage |journal=Chemical Engineering Journal |volume=149 |issue=1–3 |pages=417–427|doi=10.1016/j.cej.2009.01.036 |url=https://eprints.soton.ac.uk/149223/1/Paper.pdf }}</ref> and coupling to [[Computational fluid dynamics|CFD]]<ref name="Kafui">{{cite journal |last1=Kafui |first1=K.D. |last2=Thornton |first2=C. |last3=Adams |first3=M.J. |date=2002 |title=Discrete particle-continuum fluid modelling of gas–solid fuidised beds |journal=Chemical Engineering Science |volume=57 |issue=13 |pages=2395–2410|doi=10.1016/S0009-2509(02)00140-9 }}</ref> and [[Finite element method|FEM]]<ref name="Trivino">{{cite journal |last1=Trivino |first1=L.F. |last2=Mohanty |first2=B. |date=2015 |title=Assessment of crack initiation and propagation in rock from explosion-induced stress waves and gas expansion by cross-hole seismometry and FEM–DEM method |journal=International Journal of Rock Mechanics & Mining Sciences |volume=77 |pages=287–299|doi=10.1016/j.ijrmms.2015.03.036 }}</ref> into account.
Discrete element methods are relatively computationally intensive, which limits either the length of a simulation or the number of particles. Several DEM codes, as do molecular dynamics codes, take advantage of parallel processing capabilities (shared or distributed systems) to scale up the number of particles or length of the simulation. An alternative to treating all particles separately is to average the physics across many particles and thereby treat the material as a [[Continuum mechanics|continuum]]. In the case of [[solid]]-like granular behavior as in [[soil mechanics]], the continuum approach usually treats the material as [[Elasticity (physics)|elastic]] or [[Plasticity (physics)|elasto-plastic]] and models it with the [[finite element method]] or a [[Meshfree methods|mesh free method]]. In the case of liquid-like or gas-like granular flow, the continuum approach may treat the material as a [[fluid]] and use [[computational fluid dynamics]]. Drawbacks to [[Homogenization (chemistry)|homogenization]] of the granular scale physics, however, are well-documented and should be considered carefully before attempting to use a continuum approach.
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==Long-range forces==
When long-range forces (typically gravity or the Coulomb force) are taken into account, then the interaction between each pair of particles needs to be computed. Both the number of interactions and cost of computation [[quadratic growth|increase quadratically]] with the number of particles. This is not acceptable for simulations with large number of particles. A possible way to avoid this problem is to combine some particles, which are far away from the particle under consideration, into one pseudoparticle. Consider as an example the interaction between a star and a distant [[galaxy]]: The error arising from combining all the stars in the distant galaxy into one point mass is negligible. So-called tree algorithms are used to decide which particles can be combined into one [[pseudoparticle]]. These algorithms arrange all particles in a tree, a [[quadtree]] in the two-dimensional case and an [[octree]] in the [[Three-dimensional space|three-dimensional]] case.
However, simulations in molecular dynamics divide the space in which the simulation take place into cells. Particles leaving through one side of a cell are simply inserted at the other side (periodic [[boundary condition]]s); the same goes for the forces. The force is no longer taken into account after the so-called cut-off distance (usually half the length of a cell), so that a particle is not influenced by the mirror image of the same particle in the other side of the cell. One can now increase the number of particles by simply copying the cells.
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