Preliminary validation of lava benchmark tests on the GPUSPH particle engine

Lava flow modeling is important in many practical applications, such as the simulation ofpotential hazard scenarios and the planning of risk mitigation measures, as well as inscientific research to improve our understanding of the physical processes governing thedynamics of lava flow emplacement. Existing predictive models of lava flow behavior includevarious methods and solvers, each with its advantages and disadvantages. Codes differ intheir physical implementations, numerical accuracy, and computational efficiency. In order tovalidate their efficiency and accuracy, several benchmark test cases for computational lavaflow modeling have been established. Despite the popularity that the Smoothed ParticleHydrodynamics (SPH) method has gained in Computational Fluid Dynamics (CFD), very fewvalidations against lava flows have been successfully conducted. At the Tecnolab of INGV-Catania we designed GPUSPH, an implementation of the weakly-compressible SPH methodrunning fully on Graphics Processing Units (GPUs). GPUSPH is a particle engine capable ofmodeling both Newtonian and non-Newtonian fluids, solving the three-dimensional Navier–Stokes equations, using either a fully explicit integration scheme, or a semi-implicit schemein the case of highly viscous fluids. Thanks to the full coupling with the thermal equation, andits support for radiation, convection and phase transition, GPUSPH can be used to faithfullysimulate lava flows. Here we present the preliminary results obtained with GPUSPH forsome of the benchmarks introduced by Cordonnier et al. [2016], including analytical, semi-analytical and experimental problems. The results are reported in terms of correctness andperformance, highlighting the benefits and the drawbacks deriving from the use of SPH tosimulate lava flows