Journal
JOURNAL OF COMPUTATIONAL PHYSICS
Volume 364, Issue -, Pages 111-136Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcp.2018.02.052
Keywords
Smoothed particle hydrodynamics; Finite volume method; Domain decomposition; Coupling algorithms
Funding
- Flagship Project RITMARE - The Italian Research for the Sea - Italian National Research Council
- Italian Ministry of Education, University and Research within the National Research Program
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Recently, an algorithm for coupling a Finite Volume (FV) method, that discretize the Navier-Stokes equations on block structured Eulerian grids, with the weakly-compressible Lagrangian Smoothed Particle Hydrodynamics (SPH) was presented in [16]. The algorithm takes advantage of the SPH method to discretize flow regions close to free-surfaces and of the FV method to resolve the bulk flow and the wall regions. The continuity between the two solutions is guaranteed by overlapping zones. Here we extend the algorithm by adding the possibility to have: 1) net mass transfer between the SPH and FV sub-domains; 2) free-surface across the overlapping region. In this context, particle generation at common boundaries is required to prevent depletion or clustering of particles. This operation is not trivial, because consistency between the Lagrangian and Eulerian description of the flow must be retained to ensure mass conservation. We propose here a new coupling paradigm that extends the algorithm developed in [16] and renders it suitable to test cases where vorticity and free surface significantly pass from one domain to the other. On the SPH side, a novel technique for the creation/deletion of particle was developed. On the FV side, the information recovered from the SPH solver are exploited to improve free surface prediction in a fashion that resemble the Particle Level-Set algorithms. The combination of the two new features was tested and validated in a number of test cases where both vorticity and front evolution are important. Convergence and robustness of the algorithm are shown. (C) 2018 Elsevier Inc. All rights reserved.
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