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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
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.
Yuxuan Liu, Brendan Kochunas, Tat Nghia Nguyen, Hubert Ley, Richard Vilim
Nuclear Technology | Volume 208 | Number 12 | December 2022 | Pages 1832-1846
Technical Paper | doi.org/10.1080/00295450.2022.2092357
Articles are hosted by Taylor and Francis Online.
Advances in reducing operations and maintenance (O&M) costs are crucial to improving the viability of the nuclear energy industry. One of the important aspects to reduce the cost of maintenance activities in nuclear power plants is to automate equipment monitoring and fault diagnoses. As an inverse problem to fault diagnoses, finding a suitable population of sensors that enable a requisite degree of monitoring capability, preferably at low cost, is a prerequisite that ensures a successful monitoring and diagnosis capability. This work develops an optimization tool for the sensor assignment problem of thermal-hydraulic systems that minimizes the cost for a required diagnosing capability. The optimization is driven by a genetic algorithm (GA), with its parameters tuned by Bayesian optimization (BO). Compared to the conventional GA parameter-tuning approach based on experimental designs, the BO-tuned parameters show better performance for the test problem with various allocated computing resources. It is also verified that the BO-tuned parameters perform better for several problem variants based on the original test problem, which has practical values in meeting additional engineering goals in the sensor assignment process.