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    李睿涵, 侯叶凡, 李玉辉, 刘召远, 张海红, 梁金刚. 基于颗粒燃料的先进核反应堆大规模并行模拟与优化[J]. 计算机研究与发展. DOI: 10.7544/issn1000-1239.202221032
    引用本文: 李睿涵, 侯叶凡, 李玉辉, 刘召远, 张海红, 梁金刚. 基于颗粒燃料的先进核反应堆大规模并行模拟与优化[J]. 计算机研究与发展. 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. DOI: 10.7544/issn1000-1239.202221032
    Citation: 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. DOI: 10.7544/issn1000-1239.202221032

    基于颗粒燃料的先进核反应堆大规模并行模拟与优化

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

    • 摘要: 颗粒燃料是将核燃料制成颗粒并弥散在基体中的一种新型燃料构型,广泛应用于高温气冷堆、空间堆、氟盐冷却高温堆等先进堆型中. 以高温气冷堆和空间堆为例,基于开源蒙特卡罗程序OpenMC研究了适用于颗粒燃料临界计算的虚拟网格模拟加速方法,并在山河超算平台开展了超10万核心的大规模并行测试. 结果表明,高温气冷堆模型的有效增殖因数计算结果与石岛湾核电站实验数据符合较好,验证了程序及模型的准确性. 在性能方面,虚拟网格方法与OpenMC此前的真实网格方法相比,在存储空间和计算速度上均有明显提升,高温气冷堆虚拟网格模型的内存和耗时分别为真实网格模型的0.2%和82%;此外,由于虚拟网格方法简化了模型几何,其间接实现了更好的负载均衡,使得程序拥有了更高的并行效率. 对于强可扩展性,在10752核规模的测试中,虚拟网格的并行效率为83.4%,而真实网格为63.6%;对于弱可扩展性,虚拟网格模型在131600核并行效率为83.1%,而真实网格为66.1%.

       

      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, developed 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 was carried out on the Shanhe supercomputing platform. The keff result of the HTR model agrees well with the 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- and time-consumption of the HTR virtual lattice model are 0.2% and 82% 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%.

       

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