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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
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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
Proving DRACO will deliver
The United States is now closer than it has been in over five decades to launching the first nuclear thermal rocket into space, thanks to DRACO—the Demonstration Rocket for Agile Cislunar Orbit.
Junyong Bae, Jeeyea Ahn, Seung Jun Lee
Nuclear Technology | Volume 206 | Number 7 | July 2020 | Pages 951-961
Technical Paper – Special section on the 2019 ANS Student Conference | doi.org/10.1080/00295450.2019.1693215
Articles are hosted by Taylor and Francis Online.
Human operators always have the possibility to commit human errors, and in safety-critical infrastructures such as a nuclear power plant, human error could cause serious consequences. Since nuclear plant operations involve highly complex and mentally taxing activities, especially in emergency situations, it is important to detect human errors to maintain plant safety. This work proposes a method to predict the future trends of important plant parameters to determine whether a performed action is an error or not. To achieve this prediction, a recursive strategy is adopted that employs an artificial neural network as its prediction model. Two artificial neural networks were selected and compared: multilayer perceptron and long short-term memory (LSTM). Model training was accomplished using emergency operation data from a nuclear power plant simulator. From the comparison results, it was observed that the future trends of plant parameters were quite accurately predicted through the LSTM model. It is expected that the plant parameter prediction function proposed in this work can give useful information for detecting and recovering human errors.