Impact of Uncertainty Reduction on Lead-Bismuth Coolant in Accelerator-Driven System Using Sample Reactivity Experiments

In this study, we have demonstrated that data assimilation (DA) using lead and bismuth sample reactivities measured in the Kyoto University Critical Assembly A-core can successfully reduce the uncertainty of the coolant void reactivity in accelerator-driven systems (ADSs) derived from inelastic scattering cross sections of lead and bismuth. We reevaluated and highlighted the experimental uncertainties and correlations of the sample reactivities for the DA formula. We used the MCNP6.2 code to evaluate the sample reactivities and their uncertainties and performed DA using the reactor analysis code system MARBLE. The high-sensitivity coefficients of the sample reactivities to lead and bismuth allowed us to reduce the cross-section–induced uncertainty of the void reactivity of the ADS from 6.3% to 4.8%, achieving a provisional target accuracy of 5% in this study. Furthermore, we demonstrated that the uncertainties arising from other dominant factors, such as minor actinides and steel, can be effectively reduced by using integral experimental data sets for the unified cross-section dataset ADJ2017.

本研究证实，利用京都大学临界装置A堆芯中测得的铅与铋样品反应性开展数据同化（data assimilation），可有效降低由铅、铋非弹性散射截面引发的加速器驱动次临界系统（accelerator-driven systems）冷却剂空泡反应性不确定性。我们重新评估并明确了数据同化公式中样品反应性的实验不确定性与相关性。采用MCNP6.2程序计算样品反应性及其不确定性，并借助反应堆分析代码系统MARBLE完成数据同化。样品反应性对铅、铋具有较高的灵敏度系数，使得ADS空泡反应性由截面引发的不确定性从6.3%降至4.8%，达成了本研究设定的5%暂定目标精度。此外，本研究证实，借助统一截面数据集ADJ2017的积分实验数据集，可有效降低由次要次锕系核素与结构钢等其他主导因素引发的不确定性。