4.7 Article

A dynamic relaxation method with operator splitting and random-choice strategy for SPH

期刊

JOURNAL OF COMPUTATIONAL PHYSICS
卷 458, 期 -, 页码 -

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcp.2022.111105

关键词

Smoothed particle hydrodynamics; Viscous damping; Dynamic relaxation; Operator splitting

资金

  1. Xidian University (China)
  2. National Natural Science Foundation of China (NSFC) [91952110]
  3. Deutsche Forschungsgemeinschaft [DFG HU1572/10-1, DFG HU1527/12-1]

向作者/读者索取更多资源

This paper proposes an efficient dynamic relaxation method for smoothed particle hydrodynamics (SPH) to address the time-consuming issue when achieving the equilibrium of a dynamic system. The method includes an artificial-viscosity-based damping term, two operator splitting methods, a random-choice strategy, and a splitting cell-linked list scheme to improve computational efficiency and avoid thread conflict.
In this paper, we propose an efficient dynamic relaxation method for smoothed particle hydrodynamics (SPH) to address the time-consuming issue when dealing with achieving the equilibrium of a dynamic system. First, an artificial-viscosity-based damping term is added into the momentum equation to dissipate the velocity gradient without losing the momentum conservation of the system. Two operator splitting methods, which update velocities implicitly, are then developed to discretize the added damping term for relaxing the time-step limit. To further improve the computational efficiency, a random-choice strategy is also introduced into the dynamic relaxation process, by which the damping is imposed randomly rather than each time step. Furthermore, a splitting cell-linked list scheme is proposed by dividing the conventional cell-linked list into several blocks to avoid the thread conflict when shared-memory parallelization is applied to accelerate the computation. A number of benchmark tests show the fast and efficient feature of the present method for dynamic systems achieving their equilibrium.(C) 2022 Elsevier Inc. All rights reserved.

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