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potential hazard scenarios and the planning of risk mitigation measures, as well as in
scientific research to improve our understanding of the physical processes governing the
dynamics of lava flow emplacement. Existing predictive models of lava flow behavior include
various methods and solvers, each with its advantages and disadvantages. Codes differ in
their physical implementations, numerical accuracy, and computational efficiency. In order to
validate their efficiency and accuracy, several benchmark test cases for computational lava
flow modeling have been established. Despite the popularity that the Smoothed Particle
Hydrodynamics (SPH) method has gained in Computational Fluid Dynamics (CFD), very few
validations against lava flows have been successfully conducted. At the Tecnolab of INGV-
Catania we designed GPUSPH, an implementation of the weakly-compressible SPH method
running fully on Graphics Processing Units (GPUs). GPUSPH is a particle engine capable of
modeling both Newtonian and non-Newtonian fluids, solving the three-dimensional Navier–
Stokes equations, using either a fully explicit integration scheme, or a semi-implicit scheme
in the case of highly viscous fluids. Thanks to the full coupling with the thermal equation, and
its support for radiation, convection and phase transition, GPUSPH can be used to faithfully
simulate lava flows. Here we present the preliminary results obtained with GPUSPH for
some of the benchmarks introduced by Cordonnier et al. , including analytical, semi-
analytical and experimental problems. The results are reported in terms of correctness and
performance, highlighting the benefits and the drawbacks deriving from the use of SPH to
simulate lava flows.
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