Parallel computational model based on GPU for real time calculation of atmospheric dispersion of radionuclides in the neighborhood of a nuclear power plant

A fast and accurate estimate of the atmospheric dispersion of radionuclides (ADR) is of fundamental importance for support the decisions in cases of accidents involving the release of radioactive materials at a nuclear power station. Aiming to improve the atmospheric dispersion of radionuclides system (ADRS) of the Almirante Álvaro Alberto Nuclear Power Plant (CNAAA), a refinement was proposed in the calculations of the physical models involved. However, the desired refinement imposes a large increase in computational cost, making current computers need a prohibitive time to process the calculations, making it impossible to run the system in real time. Therefore, in order to accelerate the execution of this system and to allow its effective use in predicting real-time ADS, an approach using parallel computation based on GPUs is proposed. Essentially, the ADRS used in the CNAAA consists of four main calculation modules (programs): Source Term, Wind Field, Plume Dispersion and Dose, and Projection. This work focuses on the development of a parallel version based on the GPU of the Plume Dispersion and Dose module, with focus on the dispersion calculation. The Plume Dispersion and Dose module uses a three-dimensional model of Lagrangian trajectory and Gaussian diffusion to perform calculations of the transport and diffusion of radionuclides into the atmosphere. Due to the constraints of the original program, an updated sequential version was developed and used as the basis for the implementation of a new GPU-based parallel algorithm. The parallel program was designed using the C programming language and the Compute Unified Device Architecture (CUDA), in conjunction with parallel programming techniques. As a result, the runtime of a refined dispersion model simulation decreased from 2498.59 s (running on an Intel-Core I5 7500 CPU) to 67.91 s (running on a GTX-1070 GPU). Here, the most important issues of parallel implementation as well as comparative results are presented and discussed. (author)