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Division Spotlight
Operations & Power
Members focus on the dissemination of knowledge and information in the area of power reactors with particular application to the production of electric power and process heat. The division sponsors meetings on the coverage of applied nuclear science and engineering as related to power plants, non-power reactors, and other nuclear facilities. It encourages and assists with the dissemination of knowledge pertinent to the safe and efficient operation of nuclear facilities through professional staff development, information exchange, and supporting the generation of viable solutions to current issues.
Meeting Spotlight
International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2025)
April 27–30, 2025
Denver, CO|The Westin Denver Downtown
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!
Latest Magazine Issues
Apr 2025
Jan 2025
Latest Journal Issues
Nuclear Science and Engineering
May 2025
Nuclear Technology
Fusion Science and Technology
Latest News
TerraPower begins U.K. regulatory approval process
Seattle-based TerraPower signaled its interest this week in building its Natrium small modular reactor in the United Kingdom, the company announced.
TerraPower sent a letter to the U.K.’s Department for Energy Security and Net Zero, formally establishing its intention to enter the U.K. generic design assessment (GDA) process. This is TerraPower’s first step in deployment of its Natrium technology—a 345-MW sodium fast reactor coupled with a molten salt energy storage unit—on the international stage.
Jeren Browning, Andrew Slaughter, Ross Kunz, Joshua Hansel, Bri Rolston, Katherine Wilsdon, Adam Pluth, Dillon McCardell
Nuclear Technology | Volume 208 | Number 7 | July 2022 | Pages 1089-1101
Technical Paper | doi.org/10.1080/00295450.2021.2011574
Articles are hosted by Taylor and Francis Online.
As the nuclear industry moves toward construction of microreactors and next-generation reactors, these efforts pose new challenges. A digital-twin tool will reduce costs and risk through integration of the disparate systems used in the design, construction, and operation of these reactors. Recent investments at Idaho National Laboratory (INL) in open-source digital engineering and multiphysics framework development provide a foundation from which to create and evaluate a digital twin for nuclear reactors. This digital-twin tool will use the Single Primary Heat Pipe Extraction and Removal Emulator (SPHERE) and Microreactor AGile Non-nuclear Experimental Testbed (MAGNET) as case studies to develop a digital twin with both single and 37 heat pipe test articles. The digital twin will provide the capabilities of remote monitoring and unattended operation (autonomous control) of these systems.
A digital twin is a digital replica of an operating asset that can display data received from live sensors, update a physics model for the asset with the received data, compute predictive results of operational status with artificial intelligence (AI) to aid in optimizing asset use, and apply asset control accordingly. This twin will be developed through integration of the open-source technologies Deep Lynx (a data-warehouse technology) and the Multiphysics Object-Oriented Simulation Environment (MOOSE), physical-asset sensors, and physical-asset controls. Specifically, the general AI will successfully predict the events described as MAGNET heat pipe article test cases (such as heat pipe failure) using integrated data from the MAGNET sensors and physics-based models, including developed meta models. The integration of open-source INL software and AI assets with sensor data from a test bed will lead to a repeatable framework and guide for the creation of future digital twins. The team will also perform AI model training and experimentation to determine what models and features are most important to enable intelligent, autonomous control as well as to evaluate and determine best practices for digital-twin cybersecurity.
