Dept. Física Fundamental, Universidad Nacional de Educación a Distancia, Aptdo. 60141, E-28080 Madrid, Spain.
Unilever R&D Port Sunlight, Quarry Road East, Bebington, Wirral CH63 3JW, United Kingdom.
J Chem Phys. 2017 Apr 21;146(15):150901. doi: 10.1063/1.4979514.
Dissipative particle dynamics (DPD) belongs to a class of models and computational algorithms developed to address mesoscale problems in complex fluids and soft matter in general. It is based on the notion of particles that represent coarse-grained portions of the system under study and allow, therefore, reaching time and length scales that would be otherwise unreachable from microscopic simulations. The method has been conceptually refined since its introduction almost twenty five years ago. This perspective surveys the major conceptual improvements in the original DPD model, along with its microscopic foundation, and discusses outstanding challenges in the field. We summarize some recent advances and suggest avenues for future developments.
耗散粒子动力学(DPD)属于一类模型和计算算法,旨在解决复杂流体和软物质的介观问题。它基于粒子的概念,这些粒子代表研究系统的粗粒部分,因此允许达到微观模拟无法达到的时间和长度尺度。自近二十五年前推出以来,该方法在概念上得到了进一步的完善。本综述调查了原始 DPD 模型及其微观基础的主要概念改进,并讨论了该领域的突出挑战。我们总结了一些最新进展,并提出了未来发展的途径。