UO2 versus MOX: Propagated Nuclear Data Uncertainty for keff, with Burnup
UO2 versus MOX: Propagated Nuclear Data Uncertainty for keff, with Burnup
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UO2 与 MOX：keff 的传播核数据不确定性（燃耗）
DOI：
10.13182/nse13-48
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发表时间：
2014
影响因子：
1.2
通讯作者：
A. Koning
中科院分区：
文献类型：
--
作者：
P. Helgesson;D. Rochman;H. Sjöstrand;E. Alhassan;A. Koning
精确评估反应堆整体量中传播的核数据不确定性对于新反应堆的开发以及改进的使用是必要的，例如，在常规热反应堆中用混合氧化物燃料取代二氧化铀燃料时。本文比较了UO 2燃料的两种类型的混合氧化物燃料的传播核数据的不确定性，主要是在keff，通过应用快速总蒙特卡罗方法（快速TMC）的一个典型的压水反应堆钉细胞模型在蛇，包括燃耗。大量的核数据被考虑在内，包括运输和活化数据的105种核素，裂变产率为13锕系元素，和热散射数据为H在H2O。在keff中传播的核数据不确定性确实存在显著差异;在零燃耗时，二氧化铀的不确定性为0.6%，混合氧化物燃料的不确定性为10.1%。差别随燃耗而减小。裂变燃料核素中的不饱和度和热散射是造成这种差异的最重要原因，并对此进行了理解和解释。因此，这项工作表明，在确定不确定性裕度方面，二氧化铀和混合氧化物之间可能存在着重大差异。然而，这是很难估计的简化模型的影响，不确定性应传播在任何考虑系统的更复杂的模型。然而，快速TMC允许这一点，而不会增加太多的计算时间。
Abstract Precise assessment of propagated nuclear data uncertainties in integral reactor quantities is necessary for the development of new reactors as well as for modified use, e.g., when replacing UO2 fuel by mixed-oxide (MOX) fuel in conventional thermal reactors. This paper compares UO2 fuel to two types of MOX fuel with respect to propagated nuclear data uncertainty, primarily in keff, by applying the Fast Total Monte Carlo method (Fast TMC) to a typical pressurized water reactor pin cell model in Serpent, including burnup. An extensive amount of nuclear data is taken into account, including transport and activation data for 105 nuclides, fission yields for 13 actinides, and thermal scattering data for H in H2O. There is indeed a significant difference in propagated nuclear data uncertainty in keff; at zero burnup, the uncertainty is 0.6% for UO2 and ˜ 1% for the MOX fuels. The difference decreases with burnup. Uncertainties in fissile fuel nuclides and thermal scattering are the most important for the difference, and the reasons for this are understood and explained. This work thus suggests that there can be an important difference between UO2 and MOX for the determination of uncertainty margins. However, it is difficult to estimate the effects of the simplified model; uncertainties should be propagated in more complicated models of any considered system. Fast TMC, however, allows for this without adding much computational time.