Simulation of shallow-water systems using graphics processing units

2009 ◽  
Vol 80 (3) ◽  
pp. 598-618 ◽  
Author(s):  
Miguel Lastra ◽  
José M. Mantas ◽  
Carlos Ureña ◽  
Manuel J. Castro ◽  
José A. García-Rodríguez
Keyword(s):  
Shallow Water ◽  
Graphics Processing Units ◽  
Water Systems ◽  
Graphics Processing

scholarly journals Discharge hydrograph estimation at upstream-ungauged sections by coupling a Bayesian methodology and a 2-D GPU shallow water model

2018 ◽  
Vol 22 (10) ◽  
pp. 5299-5316 ◽  
Author(s):  
Alessia Ferrari ◽  
Marco D'Oria ◽  
Renato Vacondio ◽  
Alessandro Dal Palù ◽  
Paolo Mignosa ◽  
...  
Keyword(s):  
Shallow Water ◽  
Graphics Processing Units ◽  
High Performance ◽  
Optimization Procedure ◽  
Water Model ◽  
Computational Time ◽  
Geostatistical Approach ◽  
River Reach ◽  
Graphics Processing ◽  
Flow Propagation

Abstract. This paper presents a novel methodology for estimating the unknown discharge hydrograph at the entrance of a river reach when no information is available. The methodology couples an optimization procedure based on the Bayesian geostatistical approach (BGA) with a forward self-developed 2-D hydraulic model. In order to accurately describe the flow propagation in real rivers characterized by large floodable areas, the forward model solves the 2-D shallow water equations (SWEs) by means of a finite volume explicit shock-capturing algorithm. The two-dimensional SWE code exploits the computational power of graphics processing units (GPUs), achieving a ratio of physical to computational time of up to 1000. With the aim of enhancing the computational efficiency of the inverse estimation, the Bayesian technique is parallelized, developing a procedure based on the Secure Shell (SSH) protocol that allows one to take advantage of remote high-performance computing clusters (including those available on the Cloud) equipped with GPUs. The capability of the methodology is assessed by estimating irregular and synthetic inflow hydrographs in real river reaches, also taking into account the presence of downstream corrupted observations. Finally, the procedure is applied to reconstruct a real flood wave in a river reach located in northern Italy.

A Fast Numerical Method for Internal Flood Water Dynamics to Simulate Water on Deck and Flooding Scenarios of Ships

2013 ◽  
Author(s):  
Christian F. Janßen ◽  
Sebastian Bengel ◽  
Thomas Rung ◽  
Hendrik Dankowski
Keyword(s):  
Shallow Water ◽  
Lattice Boltzmann ◽  
Graphics Processing Units ◽  
State Of The Art ◽  
Water Dynamics ◽  
Lattice Boltzmann Model ◽  
Flood Water ◽  
Graphics Processing ◽  
Tank Sloshing ◽  
Boltzmann Model

The paper reports the extension of a Lattice Boltzmann model for the nonlinear viscous shallow water equations (NSW) and its application to the simulation of internal flood water dynamics. The solver is accelerated with the help of NVIDIAs CUDA framework to access the computational power of graphics processing units (GPGPUs). The model is validated with typical tank sloshing and cross flooding scenarios and the results are compared to analytical solutions and the results of a state-of-the-art shallow water solver on the basis of Glimm’s method.

scholarly journals Discharge hydrograph estimation at upstream-ungauged sections by coupling a Bayesian methodology and a 2D GPU Shallow Water model

2018 ◽  
Author(s):  
Alessia Ferrari ◽  
Marco D'Oria ◽  
Renato Vacondio ◽  
Alessandro Dal Palù ◽  
Paolo Mignosa ◽  
...  
Keyword(s):  
Shallow Water ◽  
Graphics Processing Units ◽  
High Performance ◽  
Optimization Procedure ◽  
Water Model ◽  
Computational Time ◽  
Coupled Models ◽  
Geostatistical Approach ◽  
River Reach ◽  
Graphics Processing

Abstract. In this paper a novel methodology to estimate the unknown discharge hydrograph at the entrance of a river reach, where no information is available, is presented. The methodology is obtained by coupling an optimization procedure, based on the Bayesian Geostatistical Approach (BGA), with a forward self-developed 2D hydraulic model of the stream. In order to accurately describe the flow propagation in real rivers characterized by large floodable areas, the forward model solves the 2D Shallow Water Equations by means of a Finite Volume explicit shock-capturing algorithm. The forward code exploits the computational power of Graphics Processing Units (GPUs) achieving ratio of physical to computational time up to 1000. With the aim of enhancing the computational efficiency of the inverse estimation, the Bayesian technique is parallelized developing a procedure based on the Secure Shell (SSH) protocol which allows to take advantage of remote High Performance Computing clusters (including those available on the Cloud) equipped with GPUs. The capability of the coupled models is assessed estimating irregular and synthetic inflow hydrographs in real river reaches, taking into account also the presence of downstream corrupted observations. Finally, the capability to adopt this methodology for real cases is demonstrated by reconstructing a real flood wave in a river reach located in Northern Italy.

scholarly journals Review of smoothed particle hydrodynamics: towards converged Lagrangian flow modelling

2020 ◽  
Vol 476 (2241) ◽  
pp. 20190801
Author(s):  
Steven J. Lind ◽  
Benedict D. Rogers ◽  
Peter K. Stansby
Keyword(s):  
Smoothed Particle Hydrodynamics ◽  
Graphics Processing Units ◽  
Wave Structure ◽  
Free Form ◽  
Mesh Free ◽  
Weakly Compressible ◽  
Particle Hydrodynamics ◽  
Massively Parallel Computing ◽  
Smoothed Particle ◽  
Graphics Processing

This paper presents a review of the progress of smoothed particle hydrodynamics (SPH) towards high-order converged simulations. As a mesh-free Lagrangian method suitable for complex flows with interfaces and multiple phases, SPH has developed considerably in the past decade. While original applications were in astrophysics, early engineering applications showed the versatility and robustness of the method without emphasis on accuracy and convergence. The early method was of weakly compressible form resulting in noisy pressures due to spurious pressure waves. This was effectively removed in the incompressible (divergence-free) form which followed; since then the weakly compressible form has been advanced, reducing pressure noise. Now numerical convergence studies are standard. While the method is computationally demanding on conventional processors, it is well suited to parallel processing on massively parallel computing and graphics processing units. Applications are diverse and encompass wave–structure interaction, geophysical flows due to landslides, nuclear sludge flows, welding, gearbox flows and many others. In the state of the art, convergence is typically between the first- and second-order theoretical limits. Recent advances are improving convergence to fourth order (and higher) and these will also be outlined. This can be necessary to resolve multi-scale aspects of turbulent flow.

Adaptive Precision Block-Jacobi for High Performance Preconditioning in the Ginkgo Linear Algebra Software

2021 ◽  
Vol 47 (2) ◽  
pp. 1-28
Author(s):  
Goran Flegar ◽  
Hartwig Anzt ◽  
Terry Cojean ◽  
Enrique S. Quintana-Ortí
Keyword(s):  
Linear Algebra ◽  
Graphics Processing Units ◽  
High Performance ◽  
Numerical Algorithms ◽  
Mixed Precision ◽  
Before And After ◽  
Memory Accesses ◽  
Specialized Hardware ◽  
The Individual ◽  
Graphics Processing

The use of mixed precision in numerical algorithms is a promising strategy for accelerating scientific applications. In particular, the adoption of specialized hardware and data formats for low-precision arithmetic in high-end GPUs (graphics processing units) has motivated numerous efforts aiming at carefully reducing the working precision in order to speed up the computations. For algorithms whose performance is bound by the memory bandwidth, the idea of compressing its data before (and after) memory accesses has received considerable attention. One idea is to store an approximate operator–like a preconditioner–in lower than working precision hopefully without impacting the algorithm output. We realize the first high-performance implementation of an adaptive precision block-Jacobi preconditioner which selects the precision format used to store the preconditioner data on-the-fly, taking into account the numerical properties of the individual preconditioner blocks. We implement the adaptive block-Jacobi preconditioner as production-ready functionality in the Ginkgo linear algebra library, considering not only the precision formats that are part of the IEEE standard, but also customized formats which optimize the length of the exponent and significand to the characteristics of the preconditioner blocks. Experiments run on a state-of-the-art GPU accelerator show that our implementation offers attractive runtime savings.

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