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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
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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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Fusion Science and Technology
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.
G. A. Rattá, J. Vega, A. Murari
Fusion Science and Technology | Volume 74 | Number 1 | July-August 2018 | Pages 13-22
Technical Paper | doi.org/10.1080/15361055.2017.1390390
Articles are hosted by Taylor and Francis Online.
Models that apply machine learning (ML) techniques for disruption prediction have improved detection rates and warning times in JET and other tokamaks. However, these models require an already stored database to develop them. Therefore, a significant problem arises at the time of training ML-based systems for ITER. To tackle this problem, this work computes a genetic algorithm–optimized predictor inspired by a previous study using initially only ASDEX-Upgrade (AUG) data and tested with the wide database of JET. This smaller-to-larger tokamak approach pursues the future extrapolation of this technique to ITER. The outcomes of direct application of a cross predictor resulted in 30.03% false alarms and more than 42% premature alarms, which indicates the need for different input parameters or at least some information about the target device to achieve reasonable performance.
In a second approach, a new model was created with the AUG database plus one disruptive and one nondisruptive pulse of JET. The final cross predictions (over the chronologically first 564 shots after training, 52 of them were disruptive) reached 100% of total detected disruptions (all of them with anticipation times up to 10 ms). The false alarms were 7.42%. The results decayed at the time newer shots were tested. This aging effect is a known phenomenon, and it can be tackled by periodic retraining of the system. As proof of principle, a final predictor was created in an adaptive approach, obtaining in the following 1000 pulses (52 of them disruptive) 91.75% detections with at least 10 ms of warning times and less than 1% false alarms.