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Division Spotlight
Fusion Energy
This division promotes the development and timely introduction of fusion energy as a sustainable energy source with favorable economic, environmental, and safety attributes. The division cooperates with other organizations on common issues of multidisciplinary fusion science and technology, conducts professional meetings, and disseminates technical information in support of these goals. Members focus on the assessment and resolution of critical developmental issues for practical fusion energy applications.
Meeting Spotlight
Utility Working Conference and Vendor Technology Expo (UWC 2024)
August 4–7, 2024
Marco Island, FL|JW Marriott Marco Island
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
NRC engineers share their expertise at the University of Puerto Rico
Robert Roche-Rivera and Marcos Rolón-Acevedo are licensed professional engineers who work at the U.S. Nuclear Regulatory Commission. They are also alumni of the University of Puerto Rico–Mayagüez (UPRM) and have been sharing their knowledge and experience with students at their alma mater since last year, serving as adjunct professors in the university’s Department of Mechanical Engineering. During the 2023–2024 school year, they each taught two courses: Fundamentals of Nuclear Science and Engineering, and Nuclear Power Plant Engineering.
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.