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Heterogeneous Online Computational Platform for GEM-Based Plasma Impurity Monitoring Systems

Author

Listed:
  • Paweł Linczuk

    (Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-650 Warsaw, Poland)

  • Andrzej Wojeński

    (Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-650 Warsaw, Poland)

  • Tomasz Czarski

    (Institute of Plasma Physics and Laser Microfusion, Hery 23, 01-497 Warsaw, Poland)

  • Piotr Kolasiński

    (Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-650 Warsaw, Poland)

  • Wojciech M. Zabołotny

    (Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-650 Warsaw, Poland)

  • Krzysztof Poźniak

    (Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-650 Warsaw, Poland)

  • Grzegorz Kasprowicz

    (Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-650 Warsaw, Poland)

  • Radosław Cieszewski

    (Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-650 Warsaw, Poland)

  • Maryna Chernyshova

    (Institute of Plasma Physics and Laser Microfusion, Hery 23, 01-497 Warsaw, Poland)

  • Karol Malinowski

    (Institute of Plasma Physics and Laser Microfusion, Hery 23, 01-497 Warsaw, Poland)

  • Didier Mazon

    (IRFM, CEA, F-13108 Saint-Paul-lez-Durance, France)

  • Julian Colnel

    (IRFM, CEA, F-13108 Saint-Paul-lez-Durance, France)

  • Denis Guibert

    (IRFM, CEA, F-13108 Saint-Paul-lez-Durance, France)

Abstract

The fusion energy research field presents many intricate challenges that require resolution. Many diagnostic systems employed in experiments are approaching the limits of current technology. Implementing efficient measurements requires using an appropriate set of tools to facilitate the optimal utilization of hardware. Fusion energy measurements must provide low latency processing with the capacity for future improvements and the ability to handle complex data flows efficiently. The presented work addresses these requirements and describes the implementation of a high-performance, low-latency software platform with convenient development for soft X-ray (SXR) plasma impurities emission tracing—the Asynchronous Complex Computation Platform (AC2P). This article presents the architectural design, implementation details, and performance and latency measurements based on the raw data acquired from the WEST tokamak and laboratory tests. AC2P provides the tools to develop low-latency, multi-core, multi-device complex data flow graph scale-up solutions for measuring impurities in hot plasmas. The system has been designed to operate online during experiments, calculate the energy distribution, position and occurrence time of SXR photons, monitor the data stream’s quality and archive any abnormalities for subsequent offline verification and algorithm improvement. This article presents AC2P, which operates as part of the SXR measurement system on the WEST tokamak.

Suggested Citation

  • Paweł Linczuk & Andrzej Wojeński & Tomasz Czarski & Piotr Kolasiński & Wojciech M. Zabołotny & Krzysztof Poźniak & Grzegorz Kasprowicz & Radosław Cieszewski & Maryna Chernyshova & Karol Malinowski & D, 2024. "Heterogeneous Online Computational Platform for GEM-Based Plasma Impurity Monitoring Systems," Energies, MDPI, vol. 17(22), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5539-:d:1514932
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    References listed on IDEAS

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    1. Jonas Degrave & Federico Felici & Jonas Buchli & Michael Neunert & Brendan Tracey & Francesco Carpanese & Timo Ewalds & Roland Hafner & Abbas Abdolmaleki & Diego de las Casas & Craig Donner & Leslie F, 2022. "Magnetic control of tokamak plasmas through deep reinforcement learning," Nature, Nature, vol. 602(7897), pages 414-419, February.
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