期刊
ENVIRONMENTAL MODELLING & SOFTWARE
卷 75, 期 -, 页码 28-43出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2015.10.002
关键词
SPH; GPU; Shallow water equations; Quad-tree neighbour searching; Dam break
类别
资金
- National Natural Science Foundation of China (NSFC) [51379074]
- Chinese Government 'Recruitment Program of Global Experts'
- Royal Society [1E131297]
- NSFC cost share International Exchanges award [51411130125]
Smoothed particle hydrodynamics (SPH) is a fully Lagrangian meshless computational method for solving the fluid dynamics equations. In recent years, it has also been employed to solve the shallow water equations (SWEs) and promising results have been obtained. However, SPH models are computationally very demanding and the SPH-SWE models considered in this work have no exception. In this paper, the Graphic Processing Units (GPUs) are explored to accelerate an SPH-SWE model for wider applications. Unlike Central Processing Units (CPUs), GPUs are highly parallelized, which makes it suitable for accelerating scientific computing algorithms like SPH. The aim is to design a GPU-based SPH model for solving the two-dimensional SWEs with variable smoothing lengths. Furthermore, a quad-tree neighbour searching method is implemented to further optimize the model performance. An idealized benchmark test and two real-world dam-break cases have been simulated to demonstrate the superior performance of the current GPU-accelerated high-performance SPH-SWE model. (C) 2015 Elsevier Ltd. All rights reserved.
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