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Accelerator Applications
The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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Fusion Science and Technology
Latest News
Garrish up for repeat term as DOE’s nuclear energy secretary
Garrish
Theodore “Ted” Garrish—who has spent more than four decades working in nuclear—is President Donald Trump’s nominee to serve as the Department of Energy’s assistant secretary for nuclear energy, or, NE-1.
The nomination was referred to the U.S. Senate’s Committee on Energy and Natural Resources on February 3. Garrish previously held the office from 1987 to 1989 under President Ronald Reagan. Most recently, Kathryn Huff held the NE-1 post, and Michael Goff has served as interim assistant secretary since Huff stepped down in May 2024.
Garrish’s most recent term in public office was as assistant secretary for the Office of International Affairs at the Energy Department, from 2018 to 2021, during Trump’s first term. Supporters say Garrish’s 40-plus years working in the nuclear industry and in nuclear energy oversight positions makes him more than qualified to serve in the DOE office again.
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.