Fast and accurate Voronoi density gridding from Lagrangian hydrodynamics data
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Voronoi grids have been successfully used to represent density structures of gas in astronomical hydrodynamics simulations. While some codes are explicitly built around using a Voronoi grid, others, such as Smoothed Particle Hydrodynamics (SPH), use particle-based representations and can benefit from constructing a Voronoi grid for post-processing their output. So far, calculating the density of each Voronoi cell from SPH data has been done numerically, which is both slow and potentially inaccurate. This paper proposes an alternative analytic method, which is fast and accurate. We derive an expression for the integral of a cubic spline kernel over the volume of a Voronoi cell and link it to the density of the cell. Mass conservation is ensured rigorously by the procedure. The method can be applied more broadly to integrate a spherically symmetric polynomial function over the volume of a random polyhedron.
Petkova , M A , Laibe , G & Bonnell , I A 2018 , ' Fast and accurate Voronoi density gridding from Lagrangian hydrodynamics data ' , Journal of Computational Physics , vol. 353 , pp. 300-315 . https://doi.org/10.1016/j.jcp.2017.10.024
Journal of Computational Physics
© 2017 Elsevier Ltd. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1016/j.jcp.2017.10.024
DescriptionMAP and IAB acknowledge funding from the European Research Council for the FP7 ERC advanced grant project ECOGAL. This work used the DiRAC Complexity system, operated by the University of Leicester IT Services, which forms part of the STFC DiRAC HPC Facility (www.dirac.ac.uk). This equipment is funded by BIS National E-Infrastructure capital grant ST/K000373/1 and STFC DiRAC Operations grant ST/K0003259/1. DiRAC is part of the National E-Infrastructure. GL acknowledges financial support from PNP, PNPS, PCMI of CNRS/INSU, CEA and CNES, France. An implementation of the code can be downloaded from https://github.com/mapetkova/kernel-integration.
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