Computational methods dissipative particle dynamic

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... 

Simulation techniques suitable for the description of phenomena at each length-scale are now relatively well established Monte Carlo (MC) and Molecular Dynamics (MD) methods at the molecular length-scale, various mesoscopic simulation methods such as Dissipative Particle Dynamics (Groot and Warren, 1997), Brownian Dynamics, or Lattice Boltzmann in the colloidal domain, Computational Fluid Dynamics at the continuum length-scale, and sequential-modular or equation-based methods at the unit operation/process-systems level. 

Computer simulations in the mesoscopic regime are now possible using methods such as Lattice Bolztmann, Dissipative Particle Dynamics, MesoDyn and Cell Dynamics Simulations. MesoDyn is a commercial package (from... 

Transition state searching and kinetic Monte Carlo techniques MULTISCALE COMPUTATIONAL METHODS FOR FLUIDS Dissipative particle dynamics Agglomeration of particles... 

ScWijper, A., Hoogetbragge, R, Manke, C. Computer simulation of dilute polymer solutions with the dissipative particle dynamics method. J. Rheol. 39, 567 (1995). doi 10.1122/1. 550713... 

Dissipative particle dynamics refers to a particle-based numerical method in which the dynamics of particles that interact through conservative, dissipative, and random forces are computed. The method is coarse-grained molecular dynamics and has the ability to capture flow at mesoscopic scales, i.e., those scales that lie between microscopic and macroscopic ones. 

The computational problems involving multi-million particle ensembles, found in modeling mesoscopic phenomena, were considered only recently as the typical problems. Rapid increase of computational power of modem processors and growing popularity of coarse-grained discrete particle methods, such as dissipative particle dynamics, fluid particle model, smoothed particle hydrodynamics and LEG, allow for the modehng of complex problems by using smaller shared-memory systems [101]. 

A computationally more efficient approach to the modeling of polymer systems is the Dissipative Particle Dynamics (DPD) method, introduced by Hoogerbmgge and Koelman [84] to describe the dynamics and rheological properties of complex fluids,... 

Pivkin, I.V. and Kamiadakis, G.E. (2005) A new method to impose no-slip boundary conditions in dissipative particle dynamics. Journal of Computational Physics, 207,114-28. 

The CG method not only provides general models for studying a class of block copolymers but also conducts efficient algorithms for simulation. In this chapter, we overview the theoretical and computational approaches toward the simulations of dynamics of microphase separation of block copolymers with the focus on the recent contributions applying Monte Carlo (MC), dissipation particle dynamics... 

While the evaluation of the interactions in a dense system is computationally beneficial, the underlying lattice structure requires the usage of special simulation techniques to accurately calculate the contribution of the nonbonded interactions to the pressure. These difficulties can be mitigated by using a soft, coarse-grained, off-lattice model. Since forces are well defined in off-lattice models, one can use Brownian dynamics or dissipative particle dynamics methods [97-103]. Also, simulations under constant pressure or surface tension are feasible. 

In order to overcome these difficulties, considerable effort has been devoted to the development of mesoscale simulation methods such as Dissipative Particle Dynamics [1-3], Lattice-Boltzmann [4-6], and Direct Simulation Monte Carlo [7-9]. The common approach of all these methods is to average out irrelevant microscopic details in order to achieve high computational efficiency while keeping the essential features of the microscopic physics on the length scales of interest. Applying these ideas to suspensions leads to a simplified, coarse-grained description of the solvent degrees of freedom, in which embedded maaomolecules such as polymers are treated by conventional molecular dynamics simulations. 

Particle methods (Molecular Dynamics, Dissipative Particle Dynamics, Multi-Particle Collision Dynamics) simulate a system of interacting mass points, and therefore thermal fluctuations are always present. The particles may have size and structure or they may be just point particles. In the former case, the finite solvent size results in an additional potential of mean force between the beads. The solvent structure extends over unphysically large length scales, because the proper separation of scale between solute and solvent is not computationally realizable. In dynamic simulations of systems in thermal equilibrium [43], solvent structure requires that the system be equilibrated with the solvent in place, whereas for a structureless solvent the solute system can be equilibrated by itself, with substantial computational savings [43]. Finally, lattice models have a (rigorously) known solvent viscosity, whereas for particle methods the existing analytical expressions are only approximations (which however usually work quite well). 

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