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Analysis of Fast Neutron Data from a Fresh Fuel Assembly

机译:新燃料组件快中子数据分析

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Understanding data better is always an unsolved problem. This is particularly true when employing neutron coincidence counting for the non-destructive assay of, e.g. ~(235)U in fresh fuel assemblies. For the infinite time limit Bohnel theory that is usually appropriate for ~3He based neutron detectors, we have developed an analysis that employs a sequential Bayesian sampling-importance-resampling particle filter to make non-parametric estimates of the posterior probability distributions for the theory's parameters: mass of the isotope that undergoes spontaneous-fission, multiplication, detection efficiency, and neutron diffusion time. List-mode, time-tagged neutron multiplicity data provides opportunities to optimize one's ability to extract the underlying source information. For low-count rate systems, the likelihood function we use is based on the distribution of waiting times between neutrons. For high-count rate systems, counting distributions form the basis of our likelihood function. Our algorithm can assay highly-enriched uranium (HEU) in a few minutes with just 500 neutrons, is self-calibrating, and quantifies uncertainties. For fast neutron counting using liquid scintillators, e.g., the fast-neutron coincidence collar (FNCL), the likelihood function needs to be different because we must analyze the data against the full time-dependent statistical theory of fission chains. The time-of-flight to the detector. λ_(TOF)~(-1), is usually longer than the time scale t between fissions. For this reason, neutrons arrive at the detector spread out by the time scale λ_(TOF)~(-1) and neutrons from different fissions in the chain are interleaved in time. In the case of a fresh fuel assembly (FFA) for a reactor, the ~(235)U content is very small compared to ~(238)U so the neutrons scatter around for a long time before inducing a fission. The FFA is essentially a neutron scattering "pinball machine." When λ_(TOF)~(-1) «τ, it becomes necessary to add prompt neutron leakage to the theory. Multiple neutrons from a single induced fission can leak "instantaneously" compared to the time scale t. This paper will discuss our modification to the statistical theory of fission chains and our statistically sophisticated data analysis methods.
机译:更好地理解数据始终是一个尚未解决的问题。当采用中子符合计数对新鲜燃料组件中的~(235)U进行无损检测时,尤其如此。对于通常适用于~3He基中子探测器的无限时间限制Bohnel理论,我们开发了一种分析方法,该方法使用序贯贝叶斯抽样重要性重采样粒子过滤器对该理论参数的后验概率分布进行非参数估计:经历自发裂变、增殖的同位素质量,探测效率和中子扩散时间。列表模式下,时间标记中子多重性数据提供了优化提取潜在源信息能力的机会。对于低计数率系统,我们使用的似然函数是基于中子之间等待时间的分布。对于高计数率系统,计数分布构成了似然函数的基础。我们的算法可以在几分钟内用500个中子测定高浓缩铀(HEU),是自校准的,并且可以量化不确定性。对于使用液体闪烁体的快中子计数,例如快中子符合环(FNCL),似然函数需要不同,因为我们必须根据裂变链的全时相关统计理论分析数据。探测器的飞行时间。λ(TOF)~(-1),通常长于裂变之间的时间尺度t。由于这个原因,中子到达探测器时按时间标度λ(TOF)~(-1)展开,来自链中不同裂变的中子在时间上交错。在反应堆新燃料组件(FFA)的情况下,~(235)U的含量与~(238)U相比非常小,因此中子在引发裂变之前分散了很长一段时间。FFA本质上是一台中子散射“弹球机”当λ(TOF)~(-1)~τ时,有必要在理论中加入瞬发中子泄漏。与时间标度t相比,一次诱发裂变产生的多个中子可以“瞬间”泄漏。本文将讨论我们对裂变链统计理论的修改,以及我们在统计上复杂的数据分析方法。

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