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Developing a new regulatory framework for advanced reactors: Update on Part 53
White
The American Nuclear Society’s Risk-informed, Performance-based Principles and Policy Committee (RP3C) on March 29 held another presentation in its monthly Community of Practice (CoP) series. The presenter, Patrick White with the Nuclear Innovation Alliance (NIA), talked about the current status of efforts to develop a new regulatory framework for advanced reactors—known as 10 CFR Part 53 or simply Part 53. White serves as the research director of the NIA, where he leads their research as well as analysis-based stakeholder and policymaker engagement and education. White’s March 29 presentation is publicly available on YouTube and at ANS’s publication platform Nuclear Science and Technology Open Research (NSTOR).
RP3C chair N. Prasad Kadambi opened the CoP with brief introductory remarks about the RP3C before he welcomed White as the session’s presenter.
White covered three main topics: the history of the existing regulatory frameworks for new reactors, progress to date on the development of the Part 53 rule for advanced reactors, and the current status and next steps for the Part 53 rulemaking process.
Laurian Dinca, Tunc Aldemir
Nuclear Science and Engineering | Volume 127 | Number 2 | October 1997 | Pages 199-219
Technical Paper | doi.org/10.13182/NSE97-A28597
Articles are hosted by Taylor and Francis Online.
A model-based parameter estimation method for nonlinear systems that does not require the linearization of the system equations and that can account for uncertainties in the monitored data as well as the parameters (e.g., random variations) is described. The method is particularly suitable for fault diagnosis because of its capability to assign probabilities of occurrence to user-specified parameter magnitude intervals that may be associated with system faults. The method regards system evolution in time as transitions between these intervals as well as user-specified magnitude intervals of the dynamic variables. These transition rates are obtained on-line from the system model and the monitored dynamic variable data and constitute a Markov chain in discrete time. The method then compares predicted and observed data at a given time step to narrow the estimated parameter range in the next time step. Implementations using a second-order van der Pol oscillator and a third-order system describing temporal xenon oscillations in a hypothetical reactor indicate that the method is computationally efficient and can be used for multiparameter estimation with incomplete information on the system state.