Bulk Handling Facility Modeling and Simulation for Safeguards Analysis

Cipiti, Benjamin B.; Shoman, Nathan

doi:10.1155/2018/3967621

Title: Bulk Handling Facility Modeling and Simulation for Safeguards Analysis

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Abstract

The Separation and Safeguards Performance Model (SSPM) uses MATLAB/Simulink to provide a tool for safeguards analysis of bulk handling nuclear processing facilities. Models of aqueous and electrochemical reprocessing, enrichment, fuel fabrication, and molten salt reactor facilities have been developed to date. These models are used for designing the overall safeguards system, examining new safeguards approaches, virtually testing new measurement instrumentation, and analyzing diversion scenarios. The key metrics generated by the models include overall measurement uncertainty and detection probability for various material diversion or facility misuse scenarios. Safeguards modeling allows for rapid and cost-effective analysis for Safeguards by Design. The models are currently being used to explore alternative safeguards approaches, including more reliance on process monitoring data to reduce the need for destructive analysis that adds considerable burden to international safeguards. Machine learning techniques are being applied, but these techniques need large amounts of data for training and testing the algorithms. The SSPM can provide that training data. This paper will describe the SSPM and its use for applying both traditional nuclear material accountancy and newer machine learning options.

Authors:

^[1];

^[1]

Sandia National Laboratories, Albuquerque, NM 87185, USA

Publication Date:: Thu Oct 04 00:00:00 EDT 2018

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:: USDOE; USDOE Office of Nuclear Energy (NE), Fuel Cycle Technologies (NE-5)

OSTI Identifier:: 1476138

Alternate Identifier(s):: OSTI ID: 1478068

Report Number(s):: SAND-2018-7314J
Journal ID: ISSN 1687-6075; PII: 3967621; 3967621

Grant/Contract Number:: AC04-94AL85000

Resource Type:: Published Article

Journal Name:: Science and Technology of Nuclear Installations

Additional Journal Information:: Journal Name: Science and Technology of Nuclear Installations Journal Volume: 2018; Journal ID: ISSN 1687-6075

Publisher:: Hindawi Publishing Corporation

Country of Publication:: Egypt

Language:: English

Subject:: 98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION

Citation Formats


                    Cipiti, Benjamin B., and Shoman, Nathan. Bulk Handling Facility Modeling and Simulation for Safeguards Analysis.  Egypt: N. p., 2018. 
Web.  doi:10.1155/2018/3967621.

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                    Cipiti, Benjamin B., & Shoman, Nathan. Bulk Handling Facility Modeling and Simulation for Safeguards Analysis.  Egypt.  https://doi.org/10.1155/2018/3967621

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                    Cipiti, Benjamin B., and Shoman, Nathan. Thu .  
"Bulk Handling Facility Modeling and Simulation for Safeguards Analysis".  Egypt.  https://doi.org/10.1155/2018/3967621.

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@article{osti_1476138,

  title        = {Bulk Handling Facility Modeling and Simulation for Safeguards Analysis},

  author       = {Cipiti, Benjamin B. and Shoman, Nathan},

  abstractNote = {The Separation and Safeguards Performance Model (SSPM) uses MATLAB/Simulink to provide a tool for safeguards analysis of bulk handling nuclear processing facilities. Models of aqueous and electrochemical reprocessing, enrichment, fuel fabrication, and molten salt reactor facilities have been developed to date. These models are used for designing the overall safeguards system, examining new safeguards approaches, virtually testing new measurement instrumentation, and analyzing diversion scenarios. The key metrics generated by the models include overall measurement uncertainty and detection probability for various material diversion or facility misuse scenarios. Safeguards modeling allows for rapid and cost-effective analysis for Safeguards by Design. The models are currently being used to explore alternative safeguards approaches, including more reliance on process monitoring data to reduce the need for destructive analysis that adds considerable burden to international safeguards. Machine learning techniques are being applied, but these techniques need large amounts of data for training and testing the algorithms. The SSPM can provide that training data. This paper will describe the SSPM and its use for applying both traditional nuclear material accountancy and newer machine learning options.},

  doi          = {10.1155/2018/3967621},

  journal      = {Science and Technology of Nuclear Installations},

  number       = ,

  volume       = 2018,

  place        = {Egypt},

  year         = {Thu Oct 04 00:00:00 EDT 2018},

  month        = {Thu Oct 04 00:00:00 EDT 2018}

}

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