基于随机特征线法的三维C5G7问题直接输运计算

Direct Transport Calculation of Three-dimensional C5G7 Benchmark Based on the Random Ray Method

  • 摘要: 相比传统的确定论特征线法,随机特征线法通过实时生成和随机抽样特征线的方式,可以有效降低计算所需的内存和特征线密度,在三维堆芯问题的直接输运计算中有很大潜力。本文基于随机特征线法初步研发了三维中子输运计算程序,并对三维C5G7基准题进行了直接输运计算。其中,通过对随机特征线参数的敏感性分析确定最优参数组合;通过引入低差异序列抽样特征线提高计算的收敛速率。计算结果表明,随机特征线法和多群蒙特卡罗符合较好,其中有效增殖因数计算偏差不超过20 pcm,功率相对偏差不超过1%;在随机特征线法中使用低差异序列抽样可以显著提升有效增殖因数计算的收敛速率,但对棒功率计算无明显效果。本文研究验证了随机特征线法在三维输运计算中的准确性,针对特征线参数的敏感性分析和伪随机数的应用为进一步优化随机特征线法计算提供了有效途径。

     

    Abstract: The method of characteristics (MOC) has gained significant attention in high-fidelity neutron transport calculations for reactor cores due to its geometric flexibility, high accuracy, and inherent parallelizability. However, its direct application to three-dimensional (3D) core transport calculations faces challenges such as excessive memory requirements and computational costs. While the widely adopted 2D/1D coupling method resolves these issues by decomposing the problem into radial 2D MOC and axial 1D calculations, it suffers from reduced geometric flexibility, numerical instabilities in axially heterogeneous systems, and axial mesh mismatches during control rod movements. To address these limitations, the random ray method (TRRM) was proposed, which dynamically generates rays and uniformly samples the starting points and directions of each ray. This method eliminates the need for storing ray information and reduces ray density required by deterministic MOC. Despite its advantages, TRRM suffers from slow convergence due to the statistical variance inherent in random sampling, with a convergence rate of O(N−1/2). This study preliminarily developed a 3D neutron transport calculation code based on TRRM and applies it to the 3D C5G7 benchmark calculation. Firstly, a sensitivity analysis of ray parameters, including the number of rays, the tracking distance, and the length of dead zone, was performed to determine optimal calculation configuration. Secondly, three core configurations of the 3D C5G7 benchmark were calculated by TRRM, and key parameters, including the effective multiplication factor (keff), pin power distributions, and axial power profiles, were compared with multi-group Monte Carlo (MGMC) to validate accuracy. Finally, a quasi-random variant of TRRM (QRRM) was tested, where low-discrepancy sequences replaced uniform random sampling to improve convergence. Numerical results demonstrate that TRRM achieves excellent agreement with Monte Carlo calculations, where the maximum deviation in keff is within 20 pcm, the averaged pin power relative deviation is below 0.2%, and the maximum pin power relative deviation is within 1%. In addition, the relative deviations of axial power profiles are less than 0.1%. QRRM significantly accelerates the convergence of keff, reducing the required number of active batches from 228 (TRRM) to 27 to achieve a standard deviation of 10 pcm. However, QRRM exhibits no notable improvement in pin power convergence, likely because the rays need to traverse a certain distance in the core and reduces the advantages of low-discrepancy sequences. Future efforts should focus on coupling TRRM with coarse-mesh finite difference (CMFD) method during active batches to provide a stabilized fission source distributions and enhance convergence rates for localized quantities such as pin power. This study validates the accuracy of the TRRM in three-dimensional transport calculations. The sensitivity analysis of ray parameters and the application of quasi-random numbers provide an effective approach for further optimizing TRRM computations.

     

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