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Division Spotlight
Nuclear Installations Safety
Devoted specifically to the safety of nuclear installations and the health and safety of the public, this division seeks a better understanding of the role of safety in the design, construction and operation of nuclear installation facilities. The division also promotes engineering and scientific technology advancement associated with the safety of such facilities.
Meeting Spotlight
ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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
First astatine-labeled compound shipped in the U.S.
The Department of Energy’s National Isotope Development Center (NIDC) on March 31 announced the successful long-distance shipment in the United States of a biologically active compound labeled with the medical radioisotope astatine-211 (At-211). Because previous shipments have included only the “bare” isotope, the NIDC has described the development as “unleashing medical innovation.”
Forrest Shriver, Cole Gentry, Justin Watson
Nuclear Science and Engineering | Volume 195 | Number 6 | June 2021 | Pages 626-647
Technical Paper | doi.org/10.1080/00295639.2020.1852021
Articles are hosted by Taylor and Francis Online.
Traditional light water reactor simulations are usually either high fidelity, requiring hundreds of node-hours, or low fidelity, requiring only seconds to run on a common workstation. In current research, it is desirable to combine the positive aspects of both of these simulation types while minimizing their associated negative costs. Because neural networks have shown significant success when applied to other fields, they could provide a means for combining these two classes of simulation. This paper describes a methodology for designing and training neural networks to predict normalized pin powers and within a reflective two-dimensional pressurized water reactor assembly model. The developed methodology combines computer vision approaches, modular neural network approaches, and hyperparameter optimization methods to intelligently design novel network architectures. This methodology has been used to develop a novel new architecture, LatticeNet, which is capable of predicting pin-resolved powers and at a high level of detail. The results produced by this novel architecture show the successful prediction of the target neutronics parameters under a variety of typical neutronics conditions, and they indicate a potential path forward for neural network–based model development.