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

Energies ◽  
2015 ◽  
Vol 8 (1) ◽  
pp. 501-528 ◽  
Author(s):  
Tom Burr ◽  
Michael Hamada ◽  
Larry Ticknor ◽  
James Sprinkle
Keyword(s):  
Process Monitoring ◽  
Nuclear Material ◽  
Statistical Testing ◽  
Monitoring Data ◽  
Nuclear Safeguards ◽  
Accounting Data ◽  
Material Accounting

scholarly journals Revisiting Statistical Aspects of Nuclear Material Accounting

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
T. Burr ◽  
M. S. Hamada
Keyword(s):  
Process Monitoring ◽  
Measurement Errors ◽  
Nuclear Material ◽  
Data Driven ◽  
Testing Period ◽  
The Difference ◽  
One Year ◽  
The Impact ◽  
Material Accounting

Nuclear material accounting (NMA) is the only safeguards system whose benefits are routinely quantified. Process monitoring (PM) is another safeguards system that is increasingly used, and one challenge is how to quantify its benefit. This paper considers PM in the role of enabling frequent NMA, which is referred to as near-real-time accounting (NRTA). We quantify NRTA benefits using period-driven and data-driven testing. Period-driven testing makes a decision to alarm or not at fixed periods. Data-driven testing decides as the data arrives whether to alarm or continue testing. The difference between period-driven and datad-riven viewpoints is illustrated by using one-year and two-year periods. For both one-year and two-year periods, period-driven NMA using once-per-year cumulative material unaccounted for (CUMUF) testing is compared to more frequent Shewhart and joint sequential cusum testing using either MUF or standardized, independently transformed MUF (SITMUF) data. We show that the data-driven viewpoint is appropriate for NRTA and that it can be used to compare safeguards effectiveness. In addition to providing period-driven and data-driven viewpoints, new features include assessing the impact of uncertainty in the estimated covariance matrix of the MUF sequence and the impact of both random and systematic measurement errors.

scholarly journals Bayesian Updating of Material Balances Covariance Matrices Using Training Data

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
T. Burr ◽  
M.S. Hamada
Keyword(s):  
Measurement Errors ◽  
Control Process ◽  
Quantitative Measure ◽  
Nuclear Material ◽  
Statistical Testing ◽  
Training Data ◽  
Nuclear Safeguards ◽  
Training Period ◽  
Material Loss ◽  
Normal Behavior

The main quantitative measure of nuclear safeguards effectiveness is nuclear material accounting (NMA), which consists of sequences of measured material balances that should be close to zero if there is no loss of special nuclear material such as Pu. NMA is essentially “accounting with measurement errors,” which arise from good, but imperfect, measurements. The covariance matrix MB of a sequence of material balances is the key quantity that determines the probability to detect loss. There is a recent push to include process monitoring (PM) data along with material balances from NMA in new schemes to monitor for material loss. PM data includes near-real-time measurements by the operator to monitor and control process operations. One concern regarding PM data is the need to estimate normal behavior of PM residuals, which requires a training period prior to ongoing testing for material loss. Assuming that a training period is used for PM data prior to its use in statistical testing for loss, that same training period could also be used for improving the estimate of MB that is used in NMA. We consider updating MB using training data with a Bayesian approach. A simulation study assesses the improvement gained with larger amounts of training data.

scholarly journals Using in-situ process monitoring data to identify defective layers in Ti-6Al-4V additively manufactured porous biomaterials

2021 ◽  
Vol 64 ◽  
pp. 1248-1254
Author(s):  
Darragh S. Egan ◽  
Caitríona M. Ryan ◽  
Andrew C. Parnell ◽  
Denis P. Dowling
Keyword(s):  
Process Monitoring ◽  
Monitoring Data ◽  
Porous Biomaterials
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