Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.