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Accelerating Fluids01:17

Accelerating Fluids

When a fluid is in constant acceleration, the pressure and buoyant force equations are modified. Suppose a beaker is placed in an elevator accelerating upward with a constant acceleration, a. In the beaker, assume there is a thin cylinder of height h with an infinitesimal cross-sectional area, ΔS.
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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
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Published on: September 17, 2021

Spatial updating grand canonical Monte Carlo algorithms for fluid simulation: generalization to continuous potentials

C J O'Keeffe1, Ruichao Ren, G Orkoulas

  • 1Department of Chemical and Biomolecular Engineering, University of California-Los Angeles, CA 90095, USA.

The Journal of Chemical Physics
|November 27, 2007
PubMed
Summary

New spatial updating grand canonical Monte Carlo algorithms handle soft-core potentials, improving simulation speed for fluid models. Sequential updating offers the fastest convergence and is ideal for parallel computing, significantly reducing simulation times.

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Area of Science:

  • Computational physics
  • Statistical mechanics
  • Fluid dynamics

Background:

  • Grand canonical Monte Carlo (GCMC) algorithms are essential for simulating fluid systems.
  • Existing spatial updating GCMC methods were limited to impenetrable spheres, excluding overlapping configurations.
  • Soft-core potentials are crucial for accurately modeling many real-world fluid interactions.

Purpose of the Study:

  • To generalize spatial updating GCMC algorithms for soft-core potentials, enabling simulations of overlapping configurations.
  • To compare the efficiency of random and sequential spatial updating GCMC algorithms against standard methods.
  • To investigate the suitability of sequential spatial updating for parallel implementation.

Main Methods:

  • Developed generalized spatial updating GCMC algorithms applicable to continuous, soft-core potentials.
  • Applied algorithms to two- and three-dimensional Lennard-Jones fluids.
  • Implemented parallel processing using domain decomposition for large-scale simulations.

Main Results:

  • Spatial updating GCMC algorithms (both random and sequential) demonstrated faster convergence than standard GCMC algorithms.
  • Sequential spatial updating algorithms exhibited the fastest convergence rates.
  • Parallel implementation of sequential spatial updating significantly reduced simulation time for 3D Lennard-Jones fluids, especially for larger systems.

Conclusions:

  • Generalized spatial updating GCMC algorithms effectively handle soft-core potentials and overlapping configurations.
  • Sequential updating spatial GCMC algorithms offer superior performance and are well-suited for parallel computing.
  • These advancements provide a more efficient and versatile approach for simulating complex fluid systems.