Dissipation particle dynamics

A fiirther theme is the development of teclmiques to bridge the length and time scales between truly molecular-scale simulations and more coarse-grained descriptions. Typical examples are dissipative particle dynamics [226] and the lattice-Boltzmaim method [227]. Part of the motivation for this is the recognition that... 

Greet R D and Warren P B 1997 Dissipative particle dynamics bridging the gap between atomistic and mesoscopic simulation J. Chem. Phys. 107 4423-35... 

Espanol P and Warren P 1995 Statistical mechanics of dissipative particles dynamics Euro. Phys. Lett. 30 191... 

Espanol P 1996 Dissipative particle dynamics for a harmonic chain a first-principles derivation Phys. Rev. B 53 1572... 

Espanol P and P B Warren 1995 Statistical Mechanics of Dissipative Particle Dynamics. Europhysl Letters 30 191-196. 

Groot R D and P B Warren 1997. Dissipative Particle Dynamics Bridging the Gap Between Atomist and Mesoscopic Simulation. Journal of Chemical Physics 107 4423-4435. 

Dissipative particle dynamics (DPD) is a technique for simulating the motion of mesoscale beads. The technique is superficially similar to a Brownian dynamics simulation in that it incorporates equations of motion, a dissipative (random) force, and a viscous drag between moving beads. However, the simulation uses a modified velocity Verlet algorithm to ensure that total momentum and force symmetries are conserved. This results in a simulation that obeys the Navier-Stokes equations and can thus predict flow. In order to set up these equations, there must be parameters to describe the interaction between beads, dissipative force, and drag. 

DPD (dissipative particle dynamics) a mesoscale algorithm DREIDING a molecular mechanics force field... 

Short-time Brownian motion was simulated and compared with experiments [108]. The structural evolution and dynamics [109] and the translational and bond-orientational order [110] were simulated with Brownian dynamics (BD) for dense binary colloidal mixtures. The short-time dynamics was investigated through the velocity autocorrelation function [111] and an algebraic decay of velocity fluctuation in a confined liquid was found [112]. Dissipative particle dynamics [113] is an attempt to bridge the gap between atomistic and mesoscopic simulation. Colloidal adsorption was simulated with BD [114]. The hydrodynamic forces, usually friction forces, are found to be able to enhance the self-diffusion of colloidal particles [115]. A novel MC approach to the dynamics of fluids was proposed in Ref. 116. Spinodal decomposition [117] in binary fluids was simulated. BD simulations for hard spherocylinders in the isotropic [118] and in the nematic phase [119] were done. A two-site Yukawa system [120] was studied with... 

P. J. Hoogerbrugge and J. M. V. A. Koelman, Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics, Europhys. Lett. 19, 155 (1992). 

A disadvantage of Langevin thermostats is that they require a (local) reference system. Dissipative particle dynamics (DPD) overcomes this problem by assuming that damping and random forces act on the center-of-mass system of a pair of atoms. The DPD equations of motion read as... 

Prinsen et al. [23] and Warren et al. [31] used dissipative particle dynamics to simulate dissolution of a pure surfactant in a solvent. Tuning surfactant-surfactant, surfactant-solvent, and solvent-solvent interactions to yield an equilibrium phase diagram similar to Fig. 1 at low temperatures except for the absence of the V i phase, they found that the kinetics of formation of the liquid crystalline phases at the interfaces was rapid and that the rate of dissolution was controlled by diffusion, in agreement with the above experimental results. 

Finally, it should be mentioned that a combination of COSMO-RS with tools such as MESODYN [127] or DPD [128] (dissipative particle dynamics) may lead to further progress in the area of the mesoscale modeling of inhomogeneous systems. Such tools are used in academia and industry in order to explore the complexity of the phase behavior of surfactant systems and amphiphilic block-co-polymers. In their coarse-grained 3D description of the long-chain molecules the tools require a thermodynamic kernel... 

During the past few decades, various theoretical models have been developed to explain the physical properties and to find key parameters for the prediction of the system behaviors. Recent technological trends focus toward integration of subsystem models in various scales, which entails examining the nanophysical properties, subsystem size, and scale-specified numerical analysis methods on system level performance. Multi-scale modeling components including quantum mechanical (i.e., density functional theory (DFT) and ab initio simulation), atom-istic/molecular (i.e., Monte Carlo (MC) and molecular dynamics (MD)), mesoscopic (i.e., dissipative particle dynamics (DPD) and lattice Boltzmann method (LBM)), and macroscopic (i.e., LBM, computational... 

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