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June 16–19, 2024
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Latest News
NRC updating GEIS rule for new nuclear technology
The Nuclear Regulatory Agency is issuing a proposed generic environmental impact statement (GEIS) for use in reviewing applications for new nuclear reactors.
In an April 17 memo, NRC secretary Carrie Safford wrote that the commission approved NRC staff’s recommendation to publish in the Federal Register a proposed rule amending 10 CFR Part 51, “Environmental Protection Regulations for Domestic Licensing and Related Regulatory Functions.”
Zeyun Wu, Jingang Liang, Xingjie Peng, Hany S. Abdel-Khalik
Nuclear Technology | Volume 205 | Number 7 | July 2019 | Pages 912-927
Regular Technical Paper | doi.org/10.1080/00295450.2018.1556062
Articles are hosted by Taylor and Francis Online.
This paper extends the applicability of the generalized perturbation theory (GPT)–free methodology, earlier developed for deterministic models, to Monte Carlo stochastic models. The objective of the GPT-free method is to calculate nuclear data sensitivity coefficients for generalized responses without solving the GPT response-specific inhomogeneous adjoint eigenvalue problem. The GPT-free methodology requires the capability to generate the eigenvalue sensitivity coefficients. This capability is readily available in several of the state-of-the-art Monte Carlo codes. The eigenvalue sensitivity coefficients are sampled using a statistical approach to construct a subspace of small dimension that is subsequently sampled for sensitivity information using a forward sensitivity analysis. A boiling water reactor assembly model is developed using the Oak Ridge National Laboratory Monte Carlo code KENO to demonstrate the application of the GPT-free methodology in Monte Carlo models. The response variations estimated by the GPT-free agree with the exact variations calculated by direct forward perturbations. The GPT-free method is also implemented in OpenMC and tested with the Godiva model to show its capability and feasibility in the estimation of the energy-dependent sensitivity coefficients for generalized responses in Monte Carlo models. The sensitivity results are compared against the ones acquired by the standard GPT-based methodologies. A higher order of accuracy in the sensitivity estimation is observed in the GPT-free method.