IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0302578.html
   My bibliography  Save this article

Accelerating electrostatic particle-in-cell simulation: A novel FPGA-based approach for efficient plasma investigations

Author

Listed:
  • Abedalmuhdi Almomany
  • Muhammed Sutcu
  • Babul Salam K S M Kader Ibrahim

Abstract

Particle-in-cell (PIC) simulation serves as a widely employed method for investigating plasma, a prevalent state of matter in the universe. This simulation approach is instrumental in exploring characteristics such as particle acceleration by turbulence and fluid, as well as delving into the properties of plasma at both the kinetic scale and macroscopic processes. However, the simulation itself imposes a significant computational burden. This research proposes a novel implementation approach to address the computationally intensive phase of the electrostatic PIC simulation, specifically the Particle-to-Interpolation phase. This is achieved by utilizing a high-speed Field Programmable Gate Array (FPGA) computation platform. The suggested approach incorporates various optimization techniques and diminishes memory access latency by leveraging the flexibility and performance attributes of the Intel FPGA device. The results obtained from our study highlight the effectiveness of the proposed design, showcasing the capability to execute hundreds of functional operations in each clock cycle. This stands in contrast to the limited operations performed in a general-purpose single-core computation platform (CPU). The suggested hardware approach is also scalable and can be deployed on more advanced FPGAs with higher capabilities, resulting in a significant improvement in performance.

Suggested Citation

  • Abedalmuhdi Almomany & Muhammed Sutcu & Babul Salam K S M Kader Ibrahim, 2024. "Accelerating electrostatic particle-in-cell simulation: A novel FPGA-based approach for efficient plasma investigations," PLOS ONE, Public Library of Science, vol. 19(6), pages 1-16, June.
  • Handle: RePEc:plo:pone00:0302578
    DOI: 10.1371/journal.pone.0302578
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0302578
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0302578&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0302578?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ibrahim Tumay Gulbahar & Muhammed Sutcu & Abedalmuhdi Almomany & Babul Salam KSM Kader Ibrahim, 2023. "Optimizing Electric Vehicle Charging Station Location on Highways: A Decision Model for Meeting Intercity Travel Demand," Sustainability, MDPI, vol. 15(24), pages 1-17, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ayşe Tuğba Yapıcı & Nurettin Abut & Tarık Erfidan, 2025. "Comparing the Effectiveness of Deep Learning Approaches for Charging Time Prediction in Electric Vehicles: Kocaeli Example," Energies, MDPI, vol. 18(8), pages 1-21, April.
    2. Alexander Mutiso Mutua & Ruairí de Fréin, 2024. "Sustainable Mobility: Machine Learning-Driven Deployment of EV Charging Points in Dublin," Sustainability, MDPI, vol. 16(22), pages 1-37, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0302578. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.