Simulating Complex Fluids with Smoothed Particle Hydrodynamics

Zago, Vito; Bilotta, Giuseppe; Dalrymple, Robert A.; Fortuna, Luigi; Ganci, Gaetana; Hérault, Alexis; Negro, Ciro Del

doi:10.4401/ag-7362

Cited by 7 publications

(10 citation statements)

References 20 publications

(20 reference statements)

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“…The inlet will also give advantage in terms of performance and robustness. In fact, the high viscosity values that will be involved in BM4 require the adoption of the semi implicit integration scheme, recently introduced in GPUSPH [Zago et al, 2018]; in that case, we shown that the performances of the semi implicit scheme have a strict dependence on the number of particles involved in the simulation, then simpler geometries obtained substituting the piston structure with an open boundary will be more suited for the adoption of the semi implicit scheme.…”

Section: Discussionmentioning

confidence: 93%

“…The dynamical equations seen above are discretized according to the SPH method as described in , Hérault et al [2011] and Zago et al [2018] obtaining: 8for the continuity Equation 1, 9for the momentum Equation 2, and 10for the thermal Equation 3.…”

Section: Sph Discretization Of the Equationsmentioning

confidence: 99%

“…As stated by Cordonnier et al [2016], this choice affects the quality of the simulation, requiring an adjustment of parameters in order to improve the results. Moreover, the adoption of a weakly-compressible model affects the simulation performance, since the time stepping is driven by the speed of sound [Zago et al, 2018]. On the other hand, a weakly-compressible model allows a complete parallelization of the computations that can be run on massively parallel hardware like Graphics Processing Units (GPU), giving an advantage in terms of performance [Hérault et al, 2010].…”

Section: Introductionmentioning

confidence: 99%

“…The TecnoLab at the Istituto Nazionale di Geofisica e Vulcanologia (INGV) in Catania has created the GPUSPH particle engine [Hérault et al, 2011;Bilotta, 2014;Zago et al, 2017], the first implementation of the Weakly-Compressible Smoothed Particle Hydrodynamics (WCSPH) method to run entirely on Graphic Processing Units (GPUs). To better handle the computational needs of lava flow simulations, GPUSPH has been extended to distribute computations across multiple GPUs [Rustico et al, 2012], even across separate nodes in a cluster [Rustico et al, 2014], and it includes a semi-implicit integration scheme for highly viscous flows [Zago et al, 2018].…”

Section: Introductionmentioning

confidence: 99%

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Preliminary validation of lava benchmark tests on the GPUSPH particle engine

Zago

Bilotta

Dalrymple

et al. 2018

Annals of Geophysics

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Lava flow modeling is important in many practical applications, such as the simulation of 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 gained by the Smoothed Particle Hydrodynamics (SPH) method 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 weaklycompressible 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 a benchmark series for computational lava-flow modeling, 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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Section: Discussionmentioning

confidence: 93%

Section: Sph Discretization Of the Equationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Preliminary validation of lava benchmark tests on the GPUSPH particle engine

Zago

Bilotta

Dalrymple

et al. 2018

Annals of Geophysics

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“…We will make extensive reference to our experience from the implementation of GPUSPH [13][14][15][16][17], the first implementation of SPH to run completely on GPU using CUDA and currently supporting multi-GPU and multi-node distribution of the computation. However, all the themes that we discuss and solutions we present are of interest to all particle systems and related methods, regardless of the specific theoretical background underlying them.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Particle Systems for Meshfree Methods with High Performance

Bilotta¹,

Zago²,

Hérault³

2019

High Performance Parallel Computing

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Particle systems, commonly associated with computer graphics, animation, and video games, are an essential component in the implementation of numerical methods ranging from the meshfree methods for computational fluid dynamics and related applications (e.g., smoothed particle hydrodynamics, SPH) to minimization methods for arbitrary problems (e.g., particle swarm optimization, PSO). These methods are frequently embarrassingly parallel in nature, making them a natural fit for implementation on massively parallel computational hardware such as modern graphics processing units (GPUs). However, naive implementations fail to fully exploit the capabilities of this hardware. We present practical solutions to the challenges faced in the efficient parallel implementation of these particle systems, with a focus on performance, robustness, and flexibility. The techniques are illustrated through GPUSPH, the first implementation of SPH to run completely on GPU, and currently supporting multi-GPU clusters, uniform precision independent of domain size, and multiple SPH formulations.

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