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The algorithm for the DPD fluid, the dynamics of which is conceptually a combination of molecular dynamics, Brownian dynamics and lattice gas automata, is designed for simulating rheological properties of complex fluids on hydrodynamic time…

Statistical Mechanics · Physics 2009-10-30 C. Marsh , G. Backx , M. H. Ernst

An extension of the H-theorem for dissipative particle dynamics (DPD) to the case of a multi-component fluid is made. Detailed balance and an additional H-theorem are proved for an energy-conserving version of the DPD algorithm. The…

Statistical Mechanics · Physics 2009-10-31 C. A. Marsh , P. V. Coveney

Electrohydrodynamics is crucial in many nanofluidic and biotechnological applications. In such small scales, the complexity due to the coupling of fluid dynamics with the dynamics of ions is increased by the relevance of thermal…

Fluid Dynamics · Physics 2022-01-19 Alberto Gubbiotti , Mauro Chinappi , Carlo Massimo Casciola

The stochastic differential equations for a model of dissipative particle dynamics with both total energy and total momentum conservation in the particle-particle interactions are presented. The corresponding Fokker-Planck equation for the…

Statistical Mechanics · Physics 2009-10-30 J. Bonet Avalos , A. D. Mackie

Dissipative particle dynamics (DPD) is a relatively new technique which has proved successful in the simulation of complex fluids. We caution that for the equilibrium achieved by the DPD simulation of a simple fluid the temperature depends…

Statistical Mechanics · Physics 2009-10-30 C. A. Marsh , J. M. Yeomans

We present a systematic, top-down, thermodynamic parametrization scheme for dissipative particle dynamics (DPD) using water-octanol partition coefficients, supplemented by water-octanol phase equilibria and pure liquid phase density data.…

Dissipative particle dynamics (DPD) does not conserve energy and this precludes its use in the study of thermal processes in complex fluids. We present here a generalization of DPD that incorporates an internal energy and a temperature…

Statistical Mechanics · Physics 2016-08-15 Pep Español

The stochastic differential equations for a model of dissipative particle dynamics, with both total energy and total momentum conservation at every time-step, are presented. The algorithm satisfies detailed balance as well as the…

Statistical Mechanics · Physics 2007-05-23 Allan D. Mackie , Josep Bonet Avalos

Dissipative particle dynamics (DPD) is a novel particle method for mesoscale modeling of complex fluids. DPD particles are often thought to represent packets of real atoms, and the physical scale probed in DPD models are determined by the…

Chemical Physics · Physics 2016-10-18 R. Qiao , P. He

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…

Statistical Mechanics · Physics 2017-05-24 Pep Español , Patrick B Warren

A model system for classical fluids out of equilibrium, referred to as DPD solid (Dissipative Particles Dynamics), is studied by analytical and simulation methods. The time evolution of a DPD particle is described by a fluctuating heat…

Statistical Mechanics · Physics 2009-11-10 Marisol Ripoll , Matthieu H. Ernst

Even though the Dissipative Particle Dynamics (DPD) has shown its worth in a variety of research areas, it has been rarely used for polymer dynamics, particularly in dilute and semi-dilute conditions and under imposed flow fields. For such…

Soft Condensed Matter · Physics 2024-12-23 Sanjay Jana , Venkata Siva Krishna , Praphul Kumar , Indranil Saha Dalal

We study numerical methods for dissipative particle dynamics (DPD), which is a system of stochastic differential equations and a popular stochastic momentum-conserving thermostat for simulating complex hydrodynamic behavior at mesoscales.…

Numerical Analysis · Mathematics 2021-06-08 Xiaocheng Shang

The viscosity and self-diffusion constant of a mesoscale hydrodynamic method, dissipative particle dynamics (DPD), are investigated. The viscosity of DPD with finite time step, including the Lowe-Anderson thermostat, is derived analytically…

Soft Condensed Matter · Physics 2009-11-13 Hiroshi Noguchi , Gerhard Gompper

Dissipative Particle Dynamics (DPD) is a popular simulation model for investigating hydrodynamic behavior of systems with non-negligible equilibrium thermal fluctuations. DPD employs soft core repulsive interactions between the system…

Statistical Mechanics · Physics 2016-03-23 Oded Farago , Niels Grønbech-Jensen

We propose an efficient simulation algorithm based on the dissipative particle dynamics (DPD) method for studying electrohydrodynamic phenomena in electrolyte fluids. The fluid flow is mimicked with DPD particles while the evolution of the…

Computational Physics · Physics 2015-06-23 Stefan Medina , Jiajia Zhou , Zhen-Gang Wang , Friederike Schmid

We present a novel approach of mapping dissipative particle dynamics (DPD) into classical molecular dynamics. By introducing the invariant volume element representing the swarm of atoms we show that the interactions between the emerging…

Soft Condensed Matter · Physics 2019-02-19 Vlad P Sokhan , Ilian T Todorov

We develop a computational method based on Dissipative Particle Dynamics (DPD) that introduces solvent hydrodynamic interactions to coarse-grained models of solutes, such as ions, molecules, or polymers. DPD-solvent (DPDS) is a fully…

Soft Condensed Matter · Physics 2024-05-24 Tine Curk

Smoothed Dissipative Particle Dynamics (SDPD) is a mesoscopic particle method which allows to select the level of resolution at which a fluid is simulated. The numerical integration of its equations of motion still suffers from the lack of…

Statistical Mechanics · Physics 2017-10-25 Gérôme Faure , Gabriel Stoltz

Dissipative particle dynamics (DPD) is an effective mesoscopic particle model with a lower computational cost than molecular dynamics because of the soft potentials that it employs. However, the soft potential is not strong enough to…

Computational Physics · Physics 2018-07-27 Zhijie Xu , Paul Meakin
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