MARKOV Model Application to Proliferation Risk Reduction of an Advanced Nuclear System
Abstract
The Generation IV International Forum (GIF) emphasizes proliferation resistance and physical protection (PR&PP) as a main goal for future nuclear energy systems. The GIF PR&PP Working Group has developed a methodology for the evaluation of these systems. As an application of the methodology, Markov model has been developed for the evaluation of proliferation resistance and is demonstrated for a hypothetical Example Sodium Fast Reactor (ESFR) system. This paper presents the case of diversion by the facility owner/operator to obtain material that could be used in a nuclear weapon. The Markov model is applied to evaluate material diversion strategies. The following features of the Markov model are presented here: (1) An effective detection rate has been introduced to account for the implementation of multiple safeguards approaches at a given strategic point; (2) Technical failure to divert material is modeled as intrinsic barriers related to the design of the facility or the properties of the material in the facility; and (3) Concealment to defeat or degrade the performance of safeguards is recognized in the Markov model. Three proliferation risk measures are calculated directly by the Markov model: the detection probability, technical failure probability, and proliferation time. The material type is indicated bymore »
- Authors:
- Publication Date:
- Research Org.:
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Org.:
- Doe - Nuclear Energy,Science, & Technology
- OSTI Identifier:
- 941614
- Report Number(s):
- BNL-81552-2008-CP
R&D Project: 13953; TRN: US0807467
- DOE Contract Number:
- DE-AC02-98CH10886
- Resource Type:
- Conference
- Resource Relation:
- Conference: INMM 49th Annual Meeting; Nashville, TN; 20080713 through 20080717
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS; 29 ENERGY PLANNING, POLICY AND ECONOMY; 45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEFENSE; DESIGN; DETECTION; EVALUATION; FAST REACTORS; IMPLEMENTATION; NUCLEAR ENERGY; NUCLEAR WEAPONS; PERFORMANCE; PHYSICAL PROTECTION; PROBABILITY; PROLIFERATION; SAFEGUARDS; SENSITIVITY; SIMULATION; SODIUM; STRATEGIC POINTS
Citation Formats
Bari, R A. MARKOV Model Application to Proliferation Risk Reduction of an Advanced Nuclear System. United States: N. p., 2008.
Web.
Bari, R A. MARKOV Model Application to Proliferation Risk Reduction of an Advanced Nuclear System. United States.
Bari, R A. Sun .
"MARKOV Model Application to Proliferation Risk Reduction of an Advanced Nuclear System". United States. https://www.osti.gov/servlets/purl/941614.
@article{osti_941614,
title = {MARKOV Model Application to Proliferation Risk Reduction of an Advanced Nuclear System},
author = {Bari, R A},
abstractNote = {The Generation IV International Forum (GIF) emphasizes proliferation resistance and physical protection (PR&PP) as a main goal for future nuclear energy systems. The GIF PR&PP Working Group has developed a methodology for the evaluation of these systems. As an application of the methodology, Markov model has been developed for the evaluation of proliferation resistance and is demonstrated for a hypothetical Example Sodium Fast Reactor (ESFR) system. This paper presents the case of diversion by the facility owner/operator to obtain material that could be used in a nuclear weapon. The Markov model is applied to evaluate material diversion strategies. The following features of the Markov model are presented here: (1) An effective detection rate has been introduced to account for the implementation of multiple safeguards approaches at a given strategic point; (2) Technical failure to divert material is modeled as intrinsic barriers related to the design of the facility or the properties of the material in the facility; and (3) Concealment to defeat or degrade the performance of safeguards is recognized in the Markov model. Three proliferation risk measures are calculated directly by the Markov model: the detection probability, technical failure probability, and proliferation time. The material type is indicated by an index that is based on the quality of material diverted. Sensitivity cases have been done to demonstrate the effects of different modeling features on the measures of proliferation resistance.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2008},
month = {7}
}