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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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June 2024
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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.
Yuxuan Liu, Brendan Kochunas, Tat Nghia Nguyen, Hubert Ley, Richard Vilim
Nuclear Technology | Volume 208 | Number 12 | December 2022 | Pages 1832-1846
Technical Paper | doi.org/10.1080/00295450.2022.2092357
Articles are hosted by Taylor and Francis Online.
Advances in reducing operations and maintenance (O&M) costs are crucial to improving the viability of the nuclear energy industry. One of the important aspects to reduce the cost of maintenance activities in nuclear power plants is to automate equipment monitoring and fault diagnoses. As an inverse problem to fault diagnoses, finding a suitable population of sensors that enable a requisite degree of monitoring capability, preferably at low cost, is a prerequisite that ensures a successful monitoring and diagnosis capability. This work develops an optimization tool for the sensor assignment problem of thermal-hydraulic systems that minimizes the cost for a required diagnosing capability. The optimization is driven by a genetic algorithm (GA), with its parameters tuned by Bayesian optimization (BO). Compared to the conventional GA parameter-tuning approach based on experimental designs, the BO-tuned parameters show better performance for the test problem with various allocated computing resources. It is also verified that the BO-tuned parameters perform better for several problem variants based on the original test problem, which has practical values in meeting additional engineering goals in the sensor assignment process.