A New Modeling Technique to Analyze Safeguards Measurements in Large Systems
Abstract
SafeGuards Analysis (SGA) is a computational toolbox able to simulate different safeguards scenarios across a number of different fuel cycles and at many different scales within the Matlab Simulink framework. SGA functions by simulating Material Balance Areas (MBAs) under safeguards materials control and accountability and allows the user to define the uncertainty parameters of the associated flow and inventory measurements. The simulated safeguard system uses the uncertain measurement estimates to calculate a mass-balance across the MBA. This mass balance is then evaluated by one of a number of different statistical tests to determine if a significant amount of material has been removed from the MBA. This work describes the design of SGA, the results of testing each element of the toolbox, and a number of single MBA example scenarios. In all of the test cases SGA performed as expected, and produced acceptable results from the single MBA scenario.
- Authors:
-
- Colorado School of Mines, Golden, CO (United States). Nuclear Science and Engineering Program
- Publication Date:
- Research Org.:
- Colorado School of Mines, Golden, CO (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development (NA-22)
- OSTI Identifier:
- 1461840
- Grant/Contract Number:
- NA0001730
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- Nuclear Technology
- Additional Journal Information:
- Journal Volume: 199; Journal Issue: 2; Journal ID: ISSN 0029-5450
- Publisher:
- Taylor & Francis - formerly American Nuclear Society (ANS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS; 98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION; Safeguards; modeling; uncertainty analysis
Citation Formats
Shugart, Nicolas, and King, Jeffrey. A New Modeling Technique to Analyze Safeguards Measurements in Large Systems. United States: N. p., 2017.
Web. doi:10.1080/00295450.2017.1334435.
Shugart, Nicolas, & King, Jeffrey. A New Modeling Technique to Analyze Safeguards Measurements in Large Systems. United States. https://doi.org/10.1080/00295450.2017.1334435
Shugart, Nicolas, and King, Jeffrey. 2017.
"A New Modeling Technique to Analyze Safeguards Measurements in Large Systems". United States. https://doi.org/10.1080/00295450.2017.1334435. https://www.osti.gov/servlets/purl/1461840.
@article{osti_1461840,
title = {A New Modeling Technique to Analyze Safeguards Measurements in Large Systems},
author = {Shugart, Nicolas and King, Jeffrey},
abstractNote = {SafeGuards Analysis (SGA) is a computational toolbox able to simulate different safeguards scenarios across a number of different fuel cycles and at many different scales within the Matlab Simulink framework. SGA functions by simulating Material Balance Areas (MBAs) under safeguards materials control and accountability and allows the user to define the uncertainty parameters of the associated flow and inventory measurements. The simulated safeguard system uses the uncertain measurement estimates to calculate a mass-balance across the MBA. This mass balance is then evaluated by one of a number of different statistical tests to determine if a significant amount of material has been removed from the MBA. This work describes the design of SGA, the results of testing each element of the toolbox, and a number of single MBA example scenarios. In all of the test cases SGA performed as expected, and produced acceptable results from the single MBA scenario.},
doi = {10.1080/00295450.2017.1334435},
url = {https://www.osti.gov/biblio/1461840},
journal = {Nuclear Technology},
issn = {0029-5450},
number = 2,
volume = 199,
place = {United States},
year = {Wed Aug 02 00:00:00 EDT 2017},
month = {Wed Aug 02 00:00:00 EDT 2017}
}
Web of Science
Works referenced in this record:
Works referencing / citing this record:
Optimally configuring a measurement system to detect diversions from a nuclear fuel cycle
journal, June 2018
- Johnson, Benjamin L.; Porter, Aaron T.; King, Jeffrey C.
- Annals of Operations Research, Vol. 275, Issue 2
Optimizing Nuclear Material Accounting and Measurement Systems
journal, July 2018
- Shugart, Nicolas; Johnson, Benjamin; King, Jeffrey
- Nuclear Technology, Vol. 204, Issue 3