Massively Parallel Simulation and Optimization of Advanced Nuclear Reactors with Dispersed Particle Fuel

Li Ruihan; Hou Yefan; Li Yuhui; Liu Zhaoyuan; Zhang Haihong; Liang Jingang

doi:10.7544/issn1000-1239.202221032

Journal of Computer Research and Development > 2024 > 61(4): 973-982. > DOI: 10.7544/issn1000-1239.202221032

Li Ruihan, Hou Yefan, Li Yuhui, Liu Zhaoyuan, Zhang Haihong, Liang Jingang. Massively Parallel Simulation and Optimization of Advanced Nuclear Reactors with Dispersed Particle Fuel[J]. Journal of Computer Research and Development, 2024, 61(4): 973-982. DOI: 10.7544/issn1000-1239.202221032

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Massively Parallel Simulation and Optimization of Advanced Nuclear Reactors with Dispersed Particle Fuel

1.
Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084
2.
Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353

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Author Bio:
Li Ruihan: born in 1998. PhD candidate. His main research interest includes multi-physics coupling simulation of pebble-bed high-temperature gas-cooled reactors

Hou Yefan: born in 2000. PhD candidate. His main research interest includes resonance self-shielding treatment in nuclear reactor

Li Yuhui: born in 1998. Master candidate. His main research interest includes high performance computing

Liu Zhaoyuan: born in 1989. PhD, associate professor. His main research interests include nuclear reactor physics, Monte Carlo method, and high performance computing

Zhang Haihong: born in 1993. Master. Her main research interest includes high performance computing

Liang Jingang: born in 1989. PhD, associate professor, PhD supervisor. His main research interest includes computational modeling and safety analysis of nuclear reactors
Received Date: December 19, 2022
Revised Date: May 24, 2023
Available Online: February 01, 2024

Graphical Abstract

Abstract

Abstract

Dispersed particle fuel is a new type of nuclear fuel that is in shape of small spheres and dispersed in a matrix. It has been widely used in advanced reactors such as high-temperature gas-cooled reactors (HTRs), space reactors and fluoride-salt-cooled high-temperature reactors. This study, taking an HTR and a space reactor as examples, develops a virtual lattice method based on the open-source Monte Carlo code OpenMC to speed up dispersed particle fuel criticality simulation. Parallel tests on the scale of 100000 cores are carried out on Shanhe supercomputing platform. The k_eff result of the HTR model agrees well with Shidao-Bay nuclear power plant experiment, indicating that the code is of high accuracy. As for the performance of the code, results show that the virtual lattice model is of less memory footprint and higher computational efficiency than the original physical lattice model. The memory-consumption and time-consumption of the HTR virtual lattice model are 0.2% and 82% that of the physical lattice model respectively. And thanks to the simplification of geometry, the virtual lattice model is of higher parallel efficiency. For strong scalability, the parallel efficiency of the virtual lattice model with 10752 cores is 83.4% while that of the physical lattice model is 63.6%. And for weak scalability, the parallel efficiency of the virtual lattice model with 131600 cores is 83.1% while that of the physical lattice model is 66.1%.
- nuclear reactor,
- dispersed particle fuel,
- Monte Carlo,
- massively parallel,
- lattice speed up

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References (22)

References

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