ABSTRACT

A modeling calculation methodology for estimating the radionuclide composition in the wastes generated at the Fukushima Daiichi nuclear power station has been upgraded by introducing an approach using Bayesian inference. The developed stochastic method describes the credible interval of the regression curve for the log-normal distribution of the measured transport ratio, which is used to calibrate the radionuclide transport parameters included in the modeling calculation. Consequently, the method can predict the probability distribution of the radionuclide composition in the Fukushima Daiichi wastes. The notable feature of the developed method is that it can explicitly investigate the improvement in the accuracy and confidence (degree of belief) of the estimation of the waste inventory using Bayesian inference. Specifically, the developed method can update and improve the degree of belief of the estimation of the radionuclide composition by visualizing the reduction in the width of uncertainty in the radionuclide transport parameters in the modeling calculation in accordance with the accumulation of analytically measured data. Further investigation is expected to improve the credibility of waste inventory estimation through iteration between modeling calculations and analytical measurements and to reduce excessive conservativeness in the estimated waste inventory dataset.

摘要

通过引入使用贝叶斯推断的方法，对用于估算福岛第一核电站产生的废物中放射性核素组成的建模计算方法进行了升级。所开发的随机方法描述了测得的传输比的对数正态分布的回归曲线的可靠区间，该区间用于校准建模计算中包括的放射性核素传输参数。因此，该方法可以预测福岛第一核电站废物中放射性核素组成的概率分布。所开发方法的显着特征是，它可以使用贝叶斯推断显式地研究废物清单估计的准确性和置信度（置信度）的提高。具体来说，通过分析分析数据的累积，通过在建模计算中可视化放射性核素传输参数不确定性宽度的减小，所开发的方法可以更新和改善放射性核素成分估算的可信度。通过模型计算和分析测量之间的迭代，有望进行进一步的研究以提高废物清单估计的可信度，并减少估计的废物清单数据集中的过度保守性。