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This division promotes the development and timely introduction of fusion energy as a sustainable energy source with favorable economic, environmental, and safety attributes. The division cooperates with other organizations on common issues of multidisciplinary fusion science and technology, conducts professional meetings, and disseminates technical information in support of these goals. Members focus on the assessment and resolution of critical developmental issues for practical fusion energy applications.
Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2021)
February 9–11, 2021
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Former NRC chairs issue vaccine timeline recommendation to CDC
Five former chairmen of the U.S. Nuclear Regulatory Commission—Stephen Burns, Allison Macfarlane, Nils Diaz, Richard Meserve, and Dale Klein—signed a letter to José Romero, Arkansas health secretary and chair of the Centers for Disease Control and Prevention (CDC) immunization advisory committee, requesting that the advisory committee update its recommendation for COVID-19 vaccine allocation guidance for the energy workforce (including nuclear energy workers).
Currently, the CDC has four phases for the COVID-19 vaccine rollout. Those phases are numbered:
G. A. Rattá, J. Vega, A. Murari
Fusion Science and Technology | Volume 74 | Number 1 | July-August 2018 | Pages 13-22
Technical Paper | dx.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.