Reliability Engineering Approach to Probabilistic Proliferation Resistance Analysis of the Example Sodium Fast Reactor Fuel Cycle Facility



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International Atomic Energy Agency (IAEA) safeguards are one method of proliferation resistance which is applied at most nuclear facilities worldwide. IAEA safeguards act to prevent the diversion of nuclear materials from a facility through the deterrence of detection. However, even with IAEA safeguards present at a facility, the country where the facility is located may still attempt to proliferate nuclear material by exploiting weaknesses in the safeguards system. The IAEA's mission is to detect the diversion of nuclear materials as soon as possible and ideally before it can be weaponized. Modern IAEA safeguards utilize unattended monitoring systems (UMS) to perform nuclear material accountancy and maintain the continuity of knowledge with regards to the position of nuclear material at a facility. This research focuses on evaluating the reliability of unattended monitoring systems and integrating the probabilistic failure of these systems into the comprehensive probabilistic proliferation resistance model of a facility.

To accomplish this, this research applies reliability engineering analysis methods to probabilistic proliferation resistance modeling. This approach is demonstrated through the analysis of a safeguards design for the Example Sodium Fast Reactor Fuel Cycle Facility (ESFR FCF).

The ESFR FCF UMS were analyzed to demonstrate the analysis and design processes that an analyst or designer would go through when evaluating/designing the proliferation resistance component of a safeguards system. When comparing the mean time to failure (MTTF) for the system without redundancies versus one with redundancies, it is apparent that redundancies are necessary to achieve a design without routine failures.

A reliability engineering approach to probabilistic safeguards system analysis and design can be used to reach meaningful conclusions regarding the proliferation resistance of a UMS. The methods developed in this research provide analysts and designers alike a process to follow to evaluate the reliability of a UMS.