A splitting random-choice dynamic relaxation method for smoothed particle hydrodynamics

TL;DR

This paper introduces a novel dynamic relaxation method for smoothed particle hydrodynamics that accelerates convergence to equilibrium by combining operator splitting, random-choice viscous damping, and parallelization techniques.

Contribution

It proposes a new efficient relaxation approach with operator splitting and random viscous damping for faster static solutions in SPH simulations.

Findings

Accelerates convergence to equilibrium in SPH.

Reduces computational time compared to traditional methods.

Effective parallelization with splitting cell-linked list scheme.

Abstract

For conventional smoothed particle hydrodynamics (SPH), obtaining the static solution of a problem is time-consuming. To address this drawback, we propose an efficient dynamic relaxation method by adding large artificial-viscosity-based damping into the momentum conservation equation. Then, operator splitting methods are introduced to discretize the added viscous term for relaxing the time-step limit. To further improve the convergence rate, a random-choice strategy is adopted, in which the viscous term is imposed randomly rather than at every time step. In addition, to avoid the thread-conflict induced by applying shared-memory parallelization to accelerate implicit method, a splitting cell-linked list scheme is devised. A number of benchmark tests suggest that the present method helps systems achieve equilibrium state efficiently.