Pathway Aggregation in the Risk Assessment of Proliferation Resistance and Physical Protection (PR&PP) of Nuclear Energy Systems

Aldemir, Tunc ; Denning, Richard ; Catalyurek, Umit ; Yilmaz, Alper ; Yue, Meng ; Cheng, Lap-Yan

关键词: NUCLEAR ENERGY;    BNL;    PROLIFERATION;    LMFBR Type Reactors;    ALGORITHMS;    PHYSICAL PROTECTION;    PROBABILISTIC ESTIMATION;    RISK ASSESSMENT;    HAZARDS;    O CODES;    P CODES;    EVALUATION;    VULNERABILITY;   

DOI  :  10.2172/1172631
RP-ID  :  DOE/NEUP--11-3162
PID  :  OSTI ID: 1172631
Others  :  Other: 11-3162
Others  :  TRN: US1500039

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来源: SciTech Connect

【 摘 要 】

The framework for Proliferation Resistance and Physical Protection (PR & PP) evaluation is to define a set of challenges, to obtain the system responses, and to assess the outcomes. The assessment of outcomes heavily relies on pathways, defined as sequences of events or actions that could potentially be followed by a State or a group of individuals in order to achieve a proliferation objective, with the defined threats as initiating events. There may be large number of segments connecting pathway stages (e.g. acquisition, processing, and fabrication for PR) which can lead to even larger number of pathways or scenarios through possible different combinations of segment connections, each with associated probabilities contributing to the overall risk. Clustering of these scenarios in specified stage attribute intervals is important for their tractable analysis and outcome assessment. A software tool for scenario generation and clustering (OSUPR) is developed that utilizes the PRCALC code developed at the Brookhaven National Laboratory for scenario generation and the K- means, mean shift and adaptive mean shift algorithms as possible clustering schemes. The results of the study using the Example Sodium Fast Breeder as an example system show that clustering facilitates the probabilistic or deterministic analysis of scenarios to identify system vulnerabilities and communication of the major risk contributors to stakeholders. The results of the study also show that the mean shift algorithm has the most potential for assisting the analysis of the scenarios generated by PRCALC.