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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.
Mohammad Pourgol-Mohamad, Mohammad Modarres, Ali Mosleh
Nuclear Technology | Volume 165 | Number 3 | March 2009 | Pages 333-359
Technical Paper | Thermal Hydraulics | doi.org/10.13182/NT165-333
Articles are hosted by Taylor and Francis Online.
This paper discusses an integrated thermal-hydraulic (TH) uncertainty analysis methodology with an application to the Loss-of-Fluid Test (LOFT) test facility large-break loss-of-coolant accident (LBLOCA) transient. The methodology is intended for applications to best-estimate analyses of complex TH codes. The goal is to develop an integrated method to make such codes capable of comprehensively supporting the uncertainty assessment with the ability to handle important accident transients. The proposed methodology considers the TH code structural uncertainties (generally known as model uncertainty) explicitly by treating internal submodel uncertainties and by propagating such model uncertainties in the code calculations, including uncertainties about input parameters. The methodology is probabilistic, using the Bayesian approach for incorporating available evidence in quantifying uncertainties in the TH code predictions. The types of information considered include experimental data, expert opinion, and limited field data, in treating both model and input parameter uncertainties. The code output is further updated through additional Bayesian updating with available experimental data from the integrated test facilities. The methodology uses an efficient Monte Carlo sampling technique for the propagation of uncertainty, in which a modified Wilks' sampling criteria of tolerance limits is used to significantly reduce the number of simulations. This paper describes the key elements of the uncertainty analysis methodology and summarizes its application to the LOFT test facility LBLOCA.