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Energies 2015, 8(1), 501-528; doi:10.3390/en8010501

Hybrid Statistical Testing for Nuclear Material Accounting Data and/or Process Monitoring Data in Nuclear Safeguards

1,†,* , 1,†
,
1,†
and
2,†
1
Statistical Sciences, F600, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
2
Systems Design and Analysis, C921, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
The authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Erich Schneider
Received: 13 November 2014 / Accepted: 15 December 2014 / Published: 13 January 2015
(This article belongs to the Special Issue Advances in Nuclear Reactor and Fuel Cycle Technologies)
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Abstract

The aim of nuclear safeguards is to ensure that special nuclear material is used for peaceful purposes. Historically, nuclear material accounting (NMA) has provided the quantitative basis for monitoring for nuclear material loss or diversion, and process monitoring (PM) data is collected by the operator to monitor the process. PM data typically support NMA in various ways, often by providing a basis to estimate some of the in-process nuclear material inventory. We develop options for combining PM residuals and NMA residuals (residual = measurement − prediction), using a hybrid of period-driven and data-driven hypothesis testing. The modified statistical tests can be used on time series of NMA residuals (the NMA residual is the familiar material balance), or on a combination of PM and NMA residuals. The PM residuals can be generated on a fixed time schedule or as events occur. View Full-Text
Keywords: data driven; hybrid method; nuclear material accounting; period driven; process monitoring; residuals; statistical methods; time series data driven; hybrid method; nuclear material accounting; period driven; process monitoring; residuals; statistical methods; time series
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Burr, T.; Hamada, M.S.; Ticknor, L.; Sprinkle, J. Hybrid Statistical Testing for Nuclear Material Accounting Data and/or Process Monitoring Data in Nuclear Safeguards. Energies 2015, 8, 501-528.

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