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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.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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
Argonne scientists use AI to detect hidden defects in stainless steel
Imagine you’re constructing a bridge or designing an airplane, and everything appears flawless on the outside. However, microscopic flaws beneath the surface could weaken the entire structure over time.
These hidden defects can be difficult to detect with traditional inspection methods, but a new technology developed by scientists at the U.S. Department of Energy’s Argonne National Laboratory is changing that. Using artificial intelligence and advanced imaging techniques, researchers have developed a method to reveal these tiny flaws before they become critical problems.
S. González, J. Vega, A. Murari, A. Pereira, JET-EFDA Contributors
Fusion Science and Technology | Volume 62 | Number 3 | November 2012 | Pages 403-408
Selected Paper from Seventh Fusion Data Validation Workshop 2012 (Part 2) | doi.org/10.13182/FST12-A15339
Articles are hosted by Taylor and Francis Online.
New automated analysis methods allow the analysis of large amounts of data without human interaction. Tokamak machines, such as JET, are perfect candidates to apply data mining techniques in order to obtain results with high statistical relevance. In this paper, an automated technique to analyze the pedestal edge gradient is introduced. This technique does not require human intervention and therefore can be applied to many pulses. The pedestal edge gradient is the temperature gradient corresponding to the edge transport barrier at the edge of high-confinement-mode plasmas. This gradient is quantified using the temperature profiles obtained from the electron cyclotron emission diagnostic. An automated technique to locate events in plasma pulses is applied in order to locate edge-localized modes (ELMs), and then the evolution of the edge pedestal gradient is analyzed during the ELMs. The degradation of the edge pedestal gradient during an ELM is quantified using the edge pedestal gradient 2 ms before the ELM as a reference of the amplitude of the gradient. This technique has been applied to a JET database containing >700 pulses and >46 000 ELMs.