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Education, Training & Workforce Development
The Education, Training & Workforce Development Division provides communication among the academic, industrial, and governmental communities through the exchange of views and information on matters related to education, training and workforce development in nuclear and radiological science, engineering, and technology. Industry leaders, education and training professionals, and interested students work together through Society-sponsored meetings and publications, to enrich their professional development, to educate the general public, and to advance nuclear and radiological science and engineering.
Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2023)
February 6–9, 2023
Amelia Island, FL|Omni Amelia Island Resort
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
Fusion Science and Technology
Nuclear energy: enabling production of food, fiber, hydrocarbon biofuels, and negative carbon emissions
In the 1960s, Alvin Weinberg at Oak Ridge National Laboratory initiated a series of studies on nuclear agro-industrial complexes1 to address the needs of the world’s growing population. Agriculture was a central component of these studies, as it must be. Much of the emphasis was on desalination of seawater to provide fresh water for irrigation of crops. Remarkable advances have lowered the cost of desalination to make that option viable in countries like Israel. Later studies2 asked the question, are there sufficient minerals (potassium, phosphorous, copper, nickel, etc.) to enable a prosperous global society assuming sufficient nuclear energy? The answer was a qualified “yes,” with the caveat that mineral resources will limit some technological options. These studies were defined by the characteristic of looking across agricultural and industrial sectors to address multiple challenges using nuclear energy.
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.