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arXiv 2501.06150physics.comp-ph
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用于中子输运模拟的随机射线法中的随机化拟蒙特卡洛采样

Randomized Quasi-Monte Carlo Sampling in The Random Ray Method for Neutron Transport Simulations

Samuel Pasmann, John Tramm

AI总结:

本文在OpenMC随机射线求解器中引入随机化拟蒙特卡洛采样,并通过2D C5G7基准验证其可降低中子输运模拟误差并提升运行效率。

摘要:

在中性粒子输运模拟的随机射线法中,每次迭代首先对一组射线进行采样,然后沿射线路径求解特征输运方程。历史上,在对射线起点和方向进行采样时,一直使用传统的伪随机数生成器。在本工作中,我们尝试使用随机化拟蒙特卡洛采样技术替代传统的完全随机技术,旨在降低模拟结果的方差。为评估新采样方案的数值性能和运行时性能,将其在OpenMC粒子输运应用的随机射线求解器中实现,并在2D C5G7基准模拟上进行测试。与现有最优方法相比,新方案使平均栅元误差降低超过10%,整体加速最高达8%,这既得益于不确定性的数值降低,也得益于共享内存争用的实际减少。

英文摘要:

In a random ray method of neutral particle transport simulation, each iteration begins by sampling a set of rays before proceeding to solve the characteristic transport equation along the linear paths the rays follow. Historically, traditional pseudorandom number generators have always been used when sampling ray starting points and directions. In the present work, we experiment with using a randomized Quasi-Monte Carlo sampling technique in place of the traditionally fully random technique with the goal of reducing the variance of simulation results. To evaluate the numerical and runtime performance of the new sampling scheme, it is implemented in the random ray solver of the OpenMC particle transport application and tested on the 2D C5G7 benchmark simulation. The new scheme delivered over a 10% reduction in the average pin-cell error and up to an 8% overall speedup compared to the state-of-the-art, owing both to numerical reductions in uncertainty as well as practical reductions in shared memory contention.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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