Multicomponent Energy Conserving Dissipative Particle Dynamics: A General Framework for Mesoscopic Heat Transfer Applications

[+] Author and Article Information
Anuj Chaudhri

Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA 19104chaudhri@seas.upenn.edu

Jennifer R. Lukes1

Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA 19104jrlukes@seas.upenn.edu


Corresponding author.

J. Heat Transfer 131(3), 033108 (Jan 23, 2009) (9 pages) doi:10.1115/1.3056602 History: Received March 04, 2008; Revised October 01, 2008; Published January 23, 2009

A multicomponent framework for energy conserving dissipative particle dynamics (DPD) is presented for the first time in both dimensional and dimensionless forms. Explicit definitions for unknown scaling factors that are consistent with DPD convention are found by comparing the present, general dimensionless governing equations to the standard DPD expressions in the literature. When the scaling factors are chosen based on the solvent in a multicomponent system, the system of equations reduces to a set that is easy to handle computationally. A computer code based on this multicomponent framework was validated, under the special case of identical components, for one-dimensional transient and one- and two-dimensional steady-state heat conduction in a random DPD solid. The results, which compare well with existing DPD works and with analytical solutions in one and two dimensions, show the promise of energy conserving DPD for modeling heat transfer at mesoscopic length scales.

Copyright © 2009 by American Society of Mechanical Engineers
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Figure 1

Initial setup for one-dimensional heat conduction with N=3000 and Nwall=1764; figure rendered using VMD (39)

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Figure 2

Time averaged dimensionless steady-state temperature for a system with N=3000, ρ¯(1)=3.0,15.0,31.62

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Figure 3

Instantaneous dimensionless temperature profiles with increasing time for a system with N=5184, ρ¯(1)=3.0. The solid lines indicate the analytical temperature profiles and the points indicate the DPD simulation results.

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Figure 4

Dimensionless time averaged heat flux as a function of imposed temperature gradient for a system with N=3000, ρ¯(1)=31.62; the solid line is a linear fit to the simulation results.

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Figure 5

Dimensionless thermal conductivity as a function of dimensionless density

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Figure 6

Steady state isotherms for the two-dimensional heat conduction model; analytical solution (37) is shaded showing the variation in Θ¯ over the yz domain



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