4.7 Article

Particle regeneration technique for Smoothed Particle Hydrodynamics in simulation of compressible multiphase flows

出版社

ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2020.113653

关键词

Smoothed Particle Hydrodynamics; Particle regeneration technique (PRT); Multiphase flows; Compressible flows; Riemann solver

资金

  1. National Key R&D Program of China [2018YFC0308900]
  2. National Science Foundation for Distinguished Young Scholars of China [51925904]
  3. National Natural Science Foundation of China [51909042, 52088102]

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In this work, a particle regeneration technique is developed for Smoothed Particle Hydrodynamics (SPH) to address particle disorder phenomenon in strongly compressible flow. The multiphase interface is calculated using an interface reproducing algorithm, with modifications made to the momentum equation for Riemann SPH to eliminate instability. Several numerical examples were studied to validate the algorithm.
In this work, a particle regeneration technique is developed for Smoothed Particle Hydrodynamics (SPH). In traditional SPH, the particle disorder phenomenon will occur when dealing with the strongly compressible flow problem. To solve this, in the present work, uniformly distributed background particles filled in the computational domain are adopted. The particle regeneration technique is that the fluid particles replaced by the background particles when the fluid density changes to a specific limitation. The fluid variables of the background particles are approximated by the fluid variables of the initial particles in their support domain. For the multiphase flow, the multiphase interface is calculated by an interface reproducing algorithm, in which, we defined an indicator variable, and set the indicator value discontinuity between different materials. By setting the threshold value, the multiphase interface is reconstructed. Meanwhile, the momentum equation for the Riemann SPH is modified to eliminate the instability in the light phase. Several numerical examples are studied to verify the present algorithm. (C) 2020 Elsevier B.V. All rights reserved.

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