tailieunhanh - The impact of metrology study sample size on uncertainty in IAEA safeguards calculations

Quantitative conclusions by the International Atomic Energy Agency (IAEA) regarding States' nuclear material inventories and flows are provided in the form of material balance evaluations (MBEs). MBEs use facility estimates of the material unaccounted for together with verification data to monitor for possible nuclear material diversion. | The impact of metrology study sample size on uncertainty in IAEA safeguards calculations EPJ Nuclear Sci. Technol. 2 36 2016 Nuclear Sciences T. Burr et al. published by EDP Sciences 2016 amp Technologies DOI epjn 2016026 Available online at http REGULAR ARTICLE The impact of metrology study sample size on uncertainty in IAEA safeguards calculations Tom Burr Thomas Krieger Claude Norman and Ke Zhao SGIM Nuclear Fuel Cycle Information Analysis International Atomic Energy Agency Vienna International Centre PO Box 100 1400 Vienna Austria Received 4 January 2016 Accepted 23 June 2016 Abstract. Quantitative conclusions by the International Atomic Energy Agency IAEA regarding States nuclear material inventories and flows are provided in the form of material balance evaluations MBEs . MBEs use facility estimates of the material unaccounted for together with verification data to monitor for possible nuclear material diversion. Verification data consist of paired measurements usually operators declarations and inspectors verification results that are analysed one-item-at-a-time to detect significant differences. Also to check for patterns an overall difference of the operator-inspector values using a D difference statistic is used. The estimated DP and false alarm probability FAP depend on the assumed measurement error model and its random and systematic error variances which are estimated using data from previous inspections which are used for metrology studies to characterize measurement error variance components . Therefore the sample sizes in both the previous and current inspections will impact the estimated DP and FAP as is illustrated by simulated numerical examples. The examples include application of a new expression for the variance of the D statistic assuming the measurement error model is multiplicative and new application of both random and systematic error variances in one-item-at-a-time testing. 1 Introduction background and implications To