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Calculation of k-Eigenvalues and Multi-Group Eigenfunctions Using the Hybrid 'Functional Monte Carlo' Method

机译:使用混合“函数蒙特卡洛”方法计算k特征值和多组特征函数

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The Functional Monte Carlo (FMC) method is a recent hybrid technique for neutron transport simulations in which Monte Carlo (MC) methods do not directly estimate the neutron flux. Instead, MC is used to estimate nonlinear functionals, which occur in low-order equations that are derived without approximation from the high-order Boltzmann transport equation. (The deterministic solution of the low-order equations yields the FMC estimates of the eigenvalue and eigenfunction.) Because the low-order equations are derived without approximation from the Boltzmann transport equation, the FMC solution contains only statistical errors. Also, because the MC-estimated nonlinear functionals have much smaller statistical errors than the MC-estimated fluxes, the resulting FMC solution has much smaller statistical errors than the standard MC solution. In this paper, we generalize the FMC method to account for a high-order continuous-energy transport problem and a low-order spatially-discrete multigroup diffusion problem. (In our previous work, the low-order problem was energy-integrated.) We present numerical results showing that the resulting multigroup FMC fluxes and eigenvalue are much more accurate than the standard MC fluxes and eigenvalue.
机译:功能蒙特卡洛(FMC)方法是一种用于中子输运模拟的最新混合技术,其中蒙特卡洛(MC)方法不能直接估算中子通量。取而代之的是,使用MC来估计非线性函数,这些函数出现在低阶方程中,该方程不从高阶玻尔兹曼输运方程中近似得出。 (低阶方程的确定性解可得出特征值和特征函数的FMC估计值。)由于低阶方程是从玻耳兹曼输运方程中近似得出的,因此FMC解仅包含统计误差。而且,由于MC估计的非线性泛函的统计误差比MC估计的通量小得多,因此所得的FMC解决方案的统计误差比标准MC解决方案小得多。在本文中,我们推广了FMC方法来解决高阶连续能量传输问题和低阶空间离散多组扩散问题。 (在我们以前的工作中,低阶问题是能量积分的。)我们提供的数值结果表明,所得的多组FMC通量和特征值比标准MC通量和特征值精确得多。

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