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Conference Spotlight
2025 ANS Winter Conference & Expo
November 8–12, 2025
Washington, DC|Washington Hilton
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Fusion Science and Technology
Latest News
Ho Nieh, TVA board members, and nuclear fuel recycling bill head to Senate floor
Nieh
Ho Nieh, the Trump administration’s nominee to be a member of the Nuclear Regulatory Commission, and four new board members of the Tennessee Valley Authority were approved in a vote today by the Senate Environment and Public Works Committee and head to the Senate floor for a final vote.
The committee also voted to advance to the Senate floor the Nuclear REFUEL Act of 2025 (S. 2082), which would smooth the regulatory pathway for recycling used nuclear fuel.
President Donald nominated Nieh on July 30 to serve as NRC commissioner for the remainder of a term set to expire June 30, 2029, which was held by former NRC commissioner Chris Hanson, who Trump fired in June.
R. Moreno, J. Vega, S. Dormido-Canto, A. Pereira, A. Murari, JET Contributors
Fusion Science and Technology | Volume 69 | Number 2 | April 2016 | Pages 485-494
Technical Paper | doi.org/10.13182/FST15-167
Articles are hosted by Taylor and Francis Online.
The Advanced Predictor of Disruptions (APODIS) has been working in the JET real-time network since the beginning of the ITER-like wall (ILW) campaigns. APODIS is a data-driven system based on a multilayer structure of Support Vector Machines (SVM) classifiers. APODIS was trained with JET data corresponding to carbon wall discharges between April 2006 and October 2009, without any retraining in spite of its use with metallic wall discharges. This paper has two main parts. First, APODIS disruption prediction capabilities are evaluated during the ILW run period from July 2013 to October 2014. The results obtained from these experimental campaigns (more than 1059 nondisruptive discharges and 390 nonintentional disruptions) showed 2.46% false alarms and 85.38% success rate. Taking into account that ITER (International Thermonuclear Experimental Reactor) will work with a similar wall to the current ILW at JET, the purpose of the second part of this study is to compare predictors trained with data from JET ILW campaigns. The high computational cost that APODIS training requires and the great performance of SVM have motivated the development of a one-layer SVM predictor. Therefore, an APODIS version and a simpler one-layer predictor have been compared. They have been trained with data between September 2011 and July 2012 (1036 nondisruptive discharges and 201 nonintentional disruptions) and tested with experimental data in the period July 2013 to October 2014 (1051 nondisruptive discharges and 390 nonintentional disruptions). The one-layer predictor shows slightly better results than the APODIS structure.
