ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
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Division Spotlight
Thermal Hydraulics
The division provides a forum for focused technical dialogue on thermal hydraulic technology in the nuclear industry. Specifically, this will include heat transfer and fluid mechanics involved in the utilization of nuclear energy. It is intended to attract the highest quality of theoretical and experimental work to ANS, including research on basic phenomena and application to nuclear system design.
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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Nuclear Science and Engineering
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Latest News
Norway’s Halden reactor takes first step toward decommissioning
The government of Norway has granted the transfer of the Halden research reactor from the Institute for Energy Technology (IFE) to the state agency Norwegian Nuclear Decommissioning (NND). The 25-MWt Halden boiling water reactor operated from 1958 to 2018 and was used in the research of nuclear fuel, reactor internals, plant procedures and monitoring, and human factors.
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.