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Parallel O(N) Stokes’ solver towards scalable Brownian dynamics of hydrodynamically interacting objects in general geometries [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy. Office of Basic Energy Sciences, 2017.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
Physical Description
Article numbers 244,114 : digital, PDF file
Additional Creators
Argonne National Laboratory, United States. Department of Energy. Office of Basic Energy Sciences, United States. Department of Energy. Office of Advanced Scientific Computing Research, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
An efficient parallel Stokes’s solver is developed towards the complete inclusion of hydrodynamic interactions of Brownian particles in any geometry. A Langevin description of the particle dynamics is adopted, where the long-range interactions are included using a Green’s function formalism. We present a scalable parallel computational approach, where the general geometry Stokeslet is calculated following a matrix-free algorithm using the General geometry Ewald-like method. Our approach employs a highly-efficient iterative finite element Stokes’ solver for the accurate treatment of long-range hydrodynamic interactions within arbitrary confined geometries. A combination of mid-point time integration of the Brownian stochastic differential equation, the parallel Stokes’ solver, and a Chebyshev polynomial approximation for the fluctuation-dissipation theorem result in an O(N) parallel algorithm. We also illustrate the new algorithm in the context of the dynamics of confined polymer solutions in equilibrium and non-equilibrium conditions. Our method is extended to treat suspended finite size particles of arbitrary shape in any geometry using an Immersed Boundary approach.
Report Numbers
E 1.99:1393928
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Note
Published through SciTech Connect.
06/29/2017.
"132924"
Journal of Chemical Physics 146 24 ISSN 0021-9606; JCPSA6 AM
Xujun Zhao; Jiyuan Li; Xikai Jiang; Dmitry Karpeev; Olle Heinonen; Barry Smith; Juan P. Hernandez-Ortiz; Juan J. de Pablo.
Funding Information
AC02-06CH11357
View MARC record | catkey: 24057429