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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.
Dan G. Cacuci
Nuclear Science and Engineering | Volume 184 | Number 1 | September 2016 | Pages 16-30
Technical Paper | doi.org/10.13182/NSE16-16
Articles are hosted by Taylor and Francis Online.
The use of adjoint methods for computing first-order sensitivities (i.e., functional derivatives) of results (responses) produced by a computational model to the model’s parameters was initiated in the nuclear engineering sciences in the 1940s. The field of nuclear science and engineering also provided pioneering works, during the 1970s, for computing second-order response sensitivities of responses associated with the adjoint neutron and radiation transport and/or diffusion equations. These works generally indicated that the second-order sensitivities of responses such as reaction rates and the system’s effective multiplication factor to cross sections were computationally intensive to obtain, requiring O(Nα2) large-scale computations per response, for a system comprising Nα model parameters, and were considerably smaller than the corresponding first-order sensitivities. These results likely gave rise to the generally held opinion that second-order sensitivities are generally insignificant in reactor physics, which may, in turn, have led to diminishing interest in developing efficient methods for computing second-order sensitivities for nuclear engineering systems.
This work presents the second-order adjoint sensitivity analysis methodology (2nd-ASAM) for nonlinear systems, which yields exactly and efficiently the second-order functional derivatives of physical system responses (i.e., system performance parameters) to the system’s model parameters. For a physical system comprising Nα parameters, forward methods require a total of (Nα2/2+3Nα/2) large-scale computations for obtaining all of the first- and second-order sensitivities, for all system responses. In contradistinction, the 2nd-ASAM requires one large-scale computation using the first-level adjoint sensitivity system (1st-LASS) for obtaining all of the first-order sensitivities, followed by at most Nα large-scale computations using the second-level adjoint sensitivity systems, for obtaining exactly all of the second-order sensitivities of a functional-type response. The construction, implementation, and solution of the 2nd-ASAM require very little additional effort beyond the construction of the 1st-LASS needed for computing the first-order sensitivities. Furthermore, because of the symmetry properties of the second-order sensitivities, the 2nd-ASAM comprises the inherent automatic solution verification of the correctness and accuracy of the second-level adjoint functions used for the efficient and exact computation of the second-order sensitivities. The use of the 2nd-ASAM to compute exactly all of the second-order response sensitivities to model input parameters is expected to enable significant advances in related scientific disciplines, particularly the areas of uncertainty quantification and predictive modeling, including model validation, reduced-order modeling, data assimilation, model calibration, and extrapolation.