The fundamental assumption of the method is that the material consists of separate, discrete particles. These particles may have different shapes and properties that influence inter-particle contact.<ref name="Wilke2022">{{Cite journal | last1 = Wilke | first1 = Daniel N. | doi = 10.1016/j.powtec.2022.117362 | title = Traction chain networks: Insights beyond force chain networks for non-spherical particle systems | journal = Powder Technology | volume = 402 | pages = 117362 | year = 2022 | arxiv = 2106.03771 | s2cid = 235359147 }}</ref> Some examples are:
* liquids and solutions, for instance of sugar or proteins;
* bulk materials in storage silos, like cereal;
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Disadvantages
* The maximum number of particles, and duration of a virtual simulation is limited by computational power. Typical flows contain billions of particles, but contemporary DEM simulations on large cluster computing resources have only recently been able to approach this scale for sufficiently long time (simulated time, not actual program execution time).
* DEM is computationally demanding, which is the reason why it has not been so readily and widely adopted as continuum approaches in computational engineering sciences and industry. However, the actual program execution times can be reduced significantly when graphical processing units (GPUs) are utilized to conduct DEM simulations,<ref>{{Cite journal |last1=Xu |first1=J. |last2=Qi |first2=H. |last3=Fang |first3=X.|last4=Lu |first4=L.|last5=Ge |first5=W. |last6=Wang |first6=X.|last7=Xu |first7=M. |last8=Chen |first8=F. |last9=He |first9=X. |last10=Li |first10=J.|doi=10.1016/j.partic.2011.01.003 |title=Quasi-real-time simulation of rotating drum using discrete element method with parallel GPU computing |journal=Particuology |volume=9 |issue=4 |pages=446–450 |year=2011 |s2cid=93467044 }}</ref><ref>{{Cite journal |last1=Govender |first1=N. |last2=Wilke |first2=D. N. |last3=Kok |first3=S.|doi=10.1016/j.softx.2016.04.004 |title=Blaze-DEMGPU: Modular high performance DEM framework for the GPU architecture |journal=SoftwareX |volume=5 |pages=62–66 |year=2016 |bibcode=2016SoftX...5...62G |doi-access=free }}</ref> due to the large number of computing cores on typical GPUs. In addition GPUs tend to be significantly more energy efficient than conventional computing clusters when conducting DEM simulations i.e. a DEM simulation solved on GPUs requires less energy than when it is solved on a conventional computing cluster.<ref>{{Cite journal|last1=He|first1=Yi|last2=Bayly|first2=Andrew E.|last3=Hassanpour|first3=Ali|last4=Muller|first4=Frans|last5=Wu|first5=Ke|last6=Yang|first6=Dongmin|date=2018-10-01|title=A GPU-based coupled SPH-DEM method for particle-fluid flow with free surfaces|journal=Powder Technology|volume=338|pages=548–562|doi=10.1016/j.powtec.2018.07.043|issn=0032-5910|doi-access=free}}</ref>
