Intelligent Data Processing for Fusion Plasma Physics
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".
Deadline for manuscript submissions: closed (25 October 2021) | Viewed by 4763
Special Issue Editor
Special Issue Information
Dear Colleagues,
During the last decade, nuclear fusion experiments have risen to the next level, in both technology and dedication. As the start-up of the world’s largest fusion device, ITER, is closing in, and present-day experiments are reaching plasma sustainment times well above 10 seconds. New tokamak experiments are designed for pulsed operation and aim for 100-second-long operation times, while stellarators, which are theoretically capable of continuous operation, target 1000 seconds or more.
Diagnostics, especially multi-channel imaging systems, are facing a continuously growing challenge to keep up with the increasing operation times—on the one hand, fast framing systems can only be operated for a few seconds because of memory limitations; on the other hand, the storage of the resulting tremendous data is a huge problem in itself, while their processing needs to be thought about and managed in a new way.
Intelligent data processing is a straightforward candidate to relieve this stress, capable of addressing all aspects of the problem. The increased availability of machine learning techniques to a wider audience can offer effective and powerful tools for analyzing large data sets, but they can also be used to compile resource-effective calculations for real-time analysis applications, which can be applied to reduce stored data on-the-fly.
This Special Issue of Applied Sciences, titled “Intelligent Data Processing for Plasma Physics”, aims to gather a selection of peer-reviewed scientific papers, outlining how novel data processing techniques such as machine learning can bring about a new era for fusion diagnostics, offering the capability of fast real-time data processing and/or the evaluation of large and complex datasets.
Dr. Tamás Zoltán Szepesi
Guest Editor
Manuscript Submission Information
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Keywords
- experimental fusion plasma physics
- machine learning
- intelligent data processing
- image processing
- visible video diagnostics
- infrared video diagnostics
- spectroscopy
- tomography
