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Division Spotlight
Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
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
Commercial nuclear innovation "new space" age
In early 2006, a start-up company launched a small rocket from a tiny island in the Pacific. It exploded, showering the island with debris. A year later, a second launch attempt sent a rocket to space but failed to make orbit, burning up in the atmosphere. Another year brought a third attempt—and a third failure. The following month, in September 2008, the company used the last of its funds to launch a fourth rocket. It reached orbit, making history as the first privately funded liquid-fueled rocket to do so.
Pedro Mena, R. A. Borrelli, Leslie Kerby
Nuclear Technology | Volume 208 | Number 2 | February 2022 | Pages 232-245
Technical Paper | doi.org/10.1080/00295450.2021.1905470
Articles are hosted by Taylor and Francis Online.
Artificial intelligence is becoming a larger part of operations for many industries. One industry where this is occurring rapidly is the nuclear industry. Researchers from around the world are looking to implement this technology in various areas of the nuclear industry. This paper explores the use of machine learning to diagnose problems. This project makes use of synthetic data collected from a Generic Pressurized Water Reactor (GPWR) simulator on whether a reactor is operating normally or experiencing one of four different transient events. A dataset was created consisting of over 30 000 reactor operational states. The data were explored and wrangled using Python and the Pandas package, using a variety of methods. Once ready, the data were randomly shuffled, with half the data being used for training and the other half being used for testing. Six different machine learning models were created using scikit-learn and the AutoML package Tree-based Pipeline Optimization Tool (TPOT). These models were created using six data scaling methods along with six feature reduction/selection methods. These models were validated using accuracy, precision, recall, and F1 score. The accuracy of the individual transients was also calculated. All six of the models had validation scores above 95%, with the decision tree and logistic regression models performing the best. These results are promising for the possible future use of machine learning in reactor diagnostics.