GPU-based implementation of a diagnostic wind field model used in real-time prediction of atmospheric dispersion of radionuclides

In order to improve the prediction of atmospheric dispersion of radionuclides (ADR) in vicinity of Central Nuclear Almirante Alvaro Alberto (CNAAA) Brazilian Nuclear Power Plants (NPP), a more refined computational system is under development. To achieve desired refinement, the required computationa...

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Bibliographic Details
Published in:Progress in nuclear energy (New series) 2017-09, Vol.100, p.146-163
Main Authors: Pinheiro, Andre, Desterro, Filipe, Santos, Marcelo C., Pereira, Claudio M.N.A., Schirru, Roberto
Format: Article
Language:English
Subjects:
Algorithms
Atmospheric diffusion
Atmospheric dispersion of radionuclides
C (programming language)
Computation
Computer simulation
CUDA
Decomposition
Diagnostic systems
Dispersion
Electric power plants
Forecasting
GPU
Mathematical models
Modules
Nuclear electric power generation
Nuclear power plants
Parallel computing
Programming languages
Real time
Red-Black partitioning
Studies
WEST model
Wind field
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Summary:In order to improve the prediction of atmospheric dispersion of radionuclides (ADR) in vicinity of Central Nuclear Almirante Alvaro Alberto (CNAAA) Brazilian Nuclear Power Plants (NPP), a more refined computational system is under development. To achieve desired refinement, the required computational effort increases in such a way that system's execution by current computers leads to prohibitive processing time. Aiming to accelerate execution of such refined system, allowing its effective use in real-time prediction of ADR, a GPU-based parallel approach has been proposed. Basically, the ADR system used in CNAAA is comprised by 4 main modules (programs): Source Term, Wind Field, Plume Dispersion and Plume Projection modules. This work is focused on the Wind Field module, which uses a mass-consistent approach, based on Winds Extrapolated from Stability and Terrain (WEST) model. Due to the strong sequential nature of the algorithm, domain decomposition by a 3D-Red-Black partitioning was proposed and a new parallel GPU-based algorithm was implemented using the Compute Unified Device Architecture (CUDA) and C programming language. As a result, the execution time of a fine-grained simulation decreased from about 450 s (running on an Intel-I7) to 18 s (running on a GTX-680 GPU). Here, the most important issues of the parallel implementation, as well as comparative results, are presented and discussed.
ISSN:0149-1970
1878-4224
DOI:10.1016/j.pnucene.2017.05.027