Abstract

In this paper we perform nuclear data uncertain propagation with Total Monte Carlo, where the transport simulation is repeated for random evaluations of the data. The Oktavian Iron, Oktavian Nickel, and the Frascati Neutron Generator (FNG) neutron streaming SINBAD benchmarks were evaluated with OpenMC. Gaussian random deviates were drawn from the ENDF/B-VII.1 and TENDL-2017 libraries where the covariances were available. Uncertainty from multiple nuclides was propagated simultaneously assuming inter-nuclide independence. When the individual statistical uncertainty is negligible compared to the data uncertainty, then standard probability theory may be applied. If this is not the case and both need to be considered, we use Imprecise Probabilities (IP) to perform further analysis. We show how uncertain experimental data may be compared to uncertain simulation in the context of IP, and show how an uncertainty-based sensitivity analysis can be performed with IP.

摘要

在本文中，我们使用 Total Monte Carlo 执行核数据不确定传播，其中重复传输模拟以随机评估数据。使用 OpenMC 评估 Oktavian Iron、Oktavian Nickel 和 Frascati Neutron Generator (FNG) 中子流 SINBAD 基准。高斯随机偏差取自 ENDF/B-VII.1 和 TENDL-2017 库，其中协方差可用。假设核素间独立性，来自多个核素的不确定性同时传播。当个体统计不确定性与数据不确定性相比可以忽略不计时，则可以应用标准概率论。如果情况并非如此并且两者都需要考虑，我们将使用不精确概率 (IP) 进行进一步分析。