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Division Spotlight
Radiation Protection & Shielding
The Radiation Protection and Shielding Division is developing and promoting radiation protection and shielding aspects of nuclear science and technology — including interaction of nuclear radiation with materials and biological systems, instruments and techniques for the measurement of nuclear radiation fields, and radiation shield design and evaluation.
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.
Hyeonmin Kim, Seo-Ryong Koo, Geon-Pil Choi, Jung Taek Kim (KAERI)
Proceedings | Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technolgies (NPIC&HMIT 2019) | Orlando, FL, February 9-14, 2019 | Pages 563-572
There are five operating modes of Nuclear Power Plants (NPPs): refueling, startup, low power, normal power, and shutdown. In these operating modes, the startup and the shutdown operating modes of NPPs are completely manually operated. From the Operational Performance Information System (OPIS) for NPPs, which is the overall database for controlling safety performance, human error under startup and shutdown was found to be 9% during last 20 years in South Korea. For reducing the operator’s load from the startup and shutdown operations of existing NPPs, it is necessary to develop an operator support system based on Artificial Intelligence (AI). Recently, AI technology has facilitated a breakthrough by accumulating data, advanced algorithms, and growing computing power. Among these factors, the key technology of the breakthrough is deep learning that leads current AI technology. In many technical fields, the development of automation and autonomous systems has been studied by using deep learning. Therefore, in this study, an automation system for the startup and shutdown in NPPs develop using deep learning. The automation system is based on an expert system due to characteristics of the startup and shutdown operating modes, and a variety of operating controls depending on each operator are simulated by deep learning. A feasibility study is conducted by using the Compact Nuclear Simulator (CNS) that is a simulator based on Westinghouse 3-loop NPPs. The target scenario for the feasibility study is bubble creation in a pressurizer under startup. In addition, a selected deep-learning algorithm is a Recurrent Neural Network (RNN), which is a robust method for time series analysis.