[en] During the operation, maintenance, decommissioning of nuclear facilities, workers often need to work directly in the radiation environment. Considering the personal dose limit principle in the radiation protection, specific planning is usually carried out prior to the implementation of the work, which requires the radiation protection personnel to obtain the radiation field data of the entire work area. However, due to the number of detectors is limited, the radiation data can only be obtained at a few locations in the radiation area. Hence, it is necessary to select appropriate algorithm to reconstruct the complete radiation field based on these limited radiation data. After studying the accuracy and principle of commonly used reconstruction algorithms, the combination of Empirical Bayesian Kriging algorithm and Least-squares Fitting algorithm is introduced to reconstruct nuclear radiation field based on the sparse measurement data. And after many attempts, the semivariogram type of the Empirical Bayesian Kriging algorithm adopts the whittle detrended, while the model of the Least-squares Fitting algorithm is exponential function. In this method, the two algorithms are responsible for calculating different regions, the radiation field of the region surrounded by the measuring points is interpolated using Empirical Bayesian Kriging algorithm. Then the extrapolation is performed to obtain the radiation field of the outside region with Least-squares Fitting algorithm based on the results obtained in the previous step. To demonstrate the feasibility of this method, the simulation experiment with scattered and sparse measuring data is performed based on a large gradient virtual radiation field, and the average relative error of the reconstructed results is 20.7%. Considering more realistic application, another experiment with sparse data along a certain path is simulated. It shows that the average relative error is 25.1%. The results in this study indicate that the combined method is effective for the reconstruction of large gradient radiation field with sparse measurement data, which is helpful for radiation protection in practical engineering. (author)

[en] The CPU-GPU heterogeneous system provides method and idea for accelerating the whole-core MOC (method of characteristics) neutron transport calculation. A performance analysis model was proposed to identify the factors which significantly impact the parallel efficiency of the 2D MOC heterogeneous parallel algorithm based on the CPU-GPU heterogeneous system. Then the overall parallel efficiency was improved by the transport sweep and the data movement overlapping after the performance analysis. The numerical results demonstrate that the parallel algorithm maintains the desired accuracy. The data movement which includes the MPI communication and the data copy between CPU and GPU is the main factor affecting the parallel efficiency of heterogeneous parallel algorithm. The overall performance and the strong scaling efficiency are improved with the transport sweep and the data movement overlapping. About 8% improvement is observed in the overall performance and the strong scaling efficiency reaches 95% from 87% when 5 heterogeneous nodes (including 20 GPUs) are utilized to perform the simulation. Compared against the CPU-based parallelization, the overall performance of 4 CPU-GPU heterogeneous nodes outperforms the performance of 20 CPU nodes. (authors)

[en] The radiological protection of the workers in the nuclear industry is an important part of the safety culture. Optimizing the radiation exposure through the “As Low As Reasonably Achievable (ALARA)” principle is a very important procedure for assessing workers’ safety and health. A 3D ALARA planning tool is developed by China Institute for Radiation Protection (CIRP) to provide advanced, radiation exposure analysis technology to different users, who can benefit from an interactive 3D visual representation of radiation risks to support ALARA optimization. By integrating radiological information with 3D models of radiological environments, this tool provides the possibility to plan the work in a 3D environment by taking into account the geometric shielding, material, and radioactive source specifications. This tool is supposed to facilitate risk- informed planning and work execution and to support the optimization of radiological protection for activities in nuclear environments and enhance safety in the nuclear industry.

[en] The Method of Characteristics (MOC) is capable to accurately solve the neutron transport equation with arbitrary geometry. However, the MOC suffers from some drawbacks: slow convergence and time consuming. Based on the spatial domain decomposition and the ray parallelization, the parallel 2D MOC algorithm was implemented with MPI+OepnMP/CUDA programming model to leverage the computing power of Central Processing Unit-Graphics Processing Unit (CPU-GPU) heterogeneous high-performance computing systems. In addition, a dynamic workload partitioning scheme was proposed to efficiently take advantage of all the CPU and GPU resources. The workload is appropriately assigned to the CPU and GPU according to their computational capabilities, and all CPUs and GPUs perform the calculation concurrently. The numerical results demonstrate that the parallel algorithm maintains the desired accuracy. Meanwhile, the dynamic workload portioning scheme can provide the optimal workload partition based on the runtime performance. As a result, about 14% improvement is observed in the overall performance compared with the MPI+CUDA parallelization when the CPU-GPU heterogeneous computation is performed on 5 heterogeneous nodes (including 20 GPUs). (authors)

[en] The method of characteristics (MOC) consumes more computing time when solving the neutron transport equation with the configuration of practical reactor cores. As a result, researches are focused on the acceleration techniques and the parallel algorithms. Based on the parallelism of characteristic rays and energy groups, the GPU-accelerated parallel 2D MOC algorithm was implemented with the compute unified device architecture (CUDA). The code accuracy and efficiency were tested in the diamond difference scheme and the step characteristics scheme with single-precision, mixed-precision and double-precision floating-point operation. Meanwhile, the performance bottleneck of GPU application was analyzed by utilizing the NVIDIA profiling tool. The numerical results demonstrate that the parallel algorithm maintains the desired accuracy for the diamond difference scheme and the step characteristics scheme in all selected floating-point precision conditions. In addition, the GPU-based code is 35 times and 100 times faster than the CPU-based code in double-precision and single-precision, respectively. (authors)

[en] There is an increasing international focus on the need to optimize decommissioning strategies of nuclear facilities, especially considering the radiological impacts on workers, the general public, and the environment. Assessing and optimizing occupational radiation exposure during the decommissioning of nuclear facilities is important to ensure the safety and health of workers. A major effort has been spent on the development of Virtual Reality (VR) tools for radiological characterization, dose estimation, and work management. With the dismantling processes of source terms, new challenges emerged since the radiation field was dynamically changed. Combing the Computer-Aided Design (CAD) technique and the Point- Kernel method, this study aimed at dynamically assessing the radiological doses of workers during the dismantling of radiological components of nuclear facilities. The CAD-based cutting technology was introduced to meet the geometrical splitting of source terms. To accurately simulate the movement of cutting pieces, adaptive grid mapping technology was adopted to track the source terms. The results compared with Monte Carlo calculations in this study indicated that the dynamic changing of the radiation field can be accurately simulated in the phase of nuclear decommissioning. This study will help carry out the occupational safety and health management of decommissioning workers. (author)

[en] In recent years, graphics processing units (GPUs) have been adopted in many High-Performance Computing (HPC) systems due to their massive computational power and superior energy efficiency. This paper focuses on the development of a heterogeneous parallel algorithm for solving the 2D method of characteristics (MOC) equation. This algorithm is designed by introducing spatial domain decomposition (SDD) method and the ray parallelization. The SDD applies a distributed memory model which is supported by Message Passing Interface (MPI) and the size of each domain is manageable for a single process. Then the most computationally intensive and parallelizable parts of the MOC calculation, transport sweep along the characteristic rays, is identified and assigned onto the GPU threads dynamically by employing the Compute Unified Device Architecture (CUDA) environment. Numerical results demonstrate that the CPUs/GPUs heterogeneous parallel algorithm maintains the desired accuracy. And over 20x speedup are observed compared to the corresponding CPU-based parallelization. In addition, performance scales well with the number of SDD and the overall parallel efficiency is over 55% at 20 SDD. (author)

[en] Highlights: • A GPU-based parallel MOC algorithm is implemented, which includes several solving kernels. • A performance analysis model is applied to analyze the performance of the code and identify the limitation. • The corresponding optimizations according to the analysis are applied and the significant speedup ratio is obtained. - Abstract: The method of characteristics (MOC) is one of the most common methods for solving the neutron transport equation in practical application. Researches have been focused on the acceleration techniques and the parallel algorithm for improving the efficiency of MOC. The Graphics Processing Unit (GPU) provides an alternative method of parallelizing the MOC neutron transport sweep. In this work, a GPU-paralleled 2D MOC code is implemented, which employs the diamond difference (DD) scheme and the step characteristics (SC) scheme. Different parallel schemes which are ray-level, energy-group-level, and polar-angle-level, are analyzed to choose the proper parallel scheme. The C5G7 2D benchmark is calculated to verify the accuracy and efficiency of the code in different schemes with single precision and double precision. The bottlenecks of the GPU code are identified and the code is classified into three categories, which are compute-bound, memory-bound, and latency-bound, according to the performance analysis model introduced in this paper. In addition, corresponding optimization strategies are applied to improve the performance according to the analysis. Moreover, the speed, power efficiency, and hardware cost are compared for CPU and GPU based on a fictitious quarter core PWR problem. Numerical results demonstrate that the energy group-level parallelization can obtain the optimal performance on GPU. Optimization strategies are effective to improve the efficiency of the calculation on GPU, which indicates that the performance analysis model is useful and effective to locate the limitation of the code. Moreover, the GPU-version code is about 30 times faster than the CPU-version code with double precision and about 100 times faster with single precision, while the desired accuracy is kept. And the GPU delivers superior performance in both speed, energy efficiency, and hardware cost.

[en] Recently, method of characteristics (MOC) has been widely developed as the most promise method to process the whole-core transport calculation. Meanwhile, GPU/CPU heterogeneous parallel calculation has been widely used and great performance has been achieved. In this research, GPU/CPU concurrent heterogeneous parallel MOC calculation is implemented to exploit all the computational resource in the heterogeneous high performance computer (HPC) while the asynchronous communication scheme is introduced in to improve the parallel efficiency. In order to accomplish this scheme, both MPI, OpenMP, CUDA protocols are introduced. The spatial domain decomposition (SDD) technique provides the coarse-grained parallelism with the MPI protocol while the fine-grained parallelism is exploited through OpenMP (in CPU calculated domain) and CUDA (in GPU calculated domain) based on the ray parallelization. Numerical results indicate that both the concurrent heterogeneous parallel calculation and the asynchronous communication scheme are effective to improve the performance of the parallel MOC calculation. Moreover, the CPUs/GPUs heterogeneous clusters significantly outperform the CPUs clusters, which makes the large-scale whole-core transport calculation is more practicable. (author)

[en] With the development of lattice physics calculation, the requirement for efficiency and geometry adaptability of resonance self-shielding treatment has become increasingly exigent. To handle this problem, the subgroup method is applied in this work. Combined with method of characteristics, subgroup fixed source problems are solved to obtain subgroup fluxes and an equivalence cross sections is added to the background cross sections to deduce the final effective cross sections. For resonance interference effect, the Bondarenko iteration process is introduced and a set of resonance category is adopted to reduce the load of computations. In addition, a parallel scheme based on region decomposition is carried out to improve efficiency. A series of benchmark problems are solved for verification and the numerical results show that this work has a good performance to provide resonance cross sections for multiple resonant region and complicated materials accurately and effectively. (author)