The particles' internal degrees of freedom are integrated out and replaced by simplified pairwise dissipative and random forces, so as to conserve momentum locally and ensure correct hydrodynamic behaviour.
[5] The algorithms presented in this article choose randomly a pair particle for applying DPD thermostating thus reducing the computational complexity.
A key property of all of the non-bonded forces is that they conserve momentum locally, so that hydrodynamic modes of the fluid emerge even for small particle numbers.
In principle, simulations of very large systems, approaching a cubic micron for milliseconds, are possible using a parallel implementation of DPD running on multiple processors in a Beowulf-style cluster.
Such DPD applications range from modeling the rheological properties of concrete[6] to simulating liposome formation in biophysics[7] to other recent three-phase phenomena such as dynamic wetting.
[8] The DPD method has also found popularity in modeling heterogeneous multi-phase flows containing deformable objects such as blood cells[9] and polymer micelles.
[13] [14] Swope et al, have provided a detailed analysis of literature data and an experimental dataset based on Critical micelle concentration (CMC) and micellar mean aggregation number (Nagg).