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Division Spotlight
Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
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.
Peter L. Angelo
Nuclear Technology | Volume 189 | Number 3 | March 2015 | Pages 219-240
Technical Paper | Criticality Safety | doi.org/10.13182/NT14-44
Articles are hosted by Taylor and Francis Online.
A feedforward artificial neural network (ANN) is constructed using select nuclear criticality excursion experiment data sets from the French Consequences Radiologiques d’un Accident de Criticité (CRAC) and SILENE reactor campaigns. The ability to represent initial spike characteristics by an ANN provides a new method that is aligned to excursion data more directly and to a wider variable data set than traditional analytic approaches. The ANN is configured, trained, validated, and tested to 85 unique highly enriched uranium (HEU) excursion experiments, considering six input variables and two output variables (specific power and energy). The fidelity of the ANN is enhanced by normalizing the input and output data. The trained ANN is then used to determine output values for 19 select Kinetic Energy Water Boiler experiments and 14 additional CRAC excursions not used in the ANN construction. Furthermore, the same trained ANN is also used for an extensive comparison (80 cases) for a combination of uranium concentrations, ramp feed reactivity insertion rates, system volumes, and vertical container sizes. The specific spike energy and power ranges determined are bracketed by published experiment results and are more realistically represented than results derived from well-known analytical methods. The ability to predict initial peak fissions by an ANN does not require determining, a priori, a volume-dependent energy quench parameter (“b”) specific to HEU solutions. The results derived from the ANN can aid in designing realistic emergency planning constructs or criticality accident alarm system hardware placements without undue penalty for fission source term uncertainties. Neither excursion characteristics after the initial spike nor explicit time dependencies are modeled by an ANN at this time. The extension of the methods presented is left for further work.