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