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Transient thermal analysis for optimal armor material in upgraded mockup for PST using MOOSE Framework

  • Habib Ur Rahman , Muhammad Ilyas ORCID logo EMAIL logo , Shahab Ud-Din Khan , Manzoor Ahmed , Muhammad Abdullah and Ali Hussain
Published/Copyright: November 19, 2024
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Abstract

The divertor mockup design for the Pakistan Spherical Tokamak (PST) needs several improvements. These include reducing the total number of upper and lower divertors to 32 and enhancing the heat load capacity to 1 MW/m2. This study involves a thermal analysis of the upgraded divertor design for PST through simulation using MOOSE Framework. The simulation involves two steps – validation and thermal analysis. Validation is achieved by modeling 1D and 2D conduction problems in MOOSE Framework and comparing the results with the analytical solutions. The thermal analysis of a mockup of the upgraded PST divertor configurations with tungsten and graphite as armor materials under peak thermal heat load conditions of 1 MW/m2 is performed. The results of the transient thermal analysis reveal that the mockup surface temperature rise for tungsten remains lower than that for graphite. The findings of the study indicate that both tungsten and graphite mockups can sustain a specific heat flux of 1 MW/m2 for up to 3 s. However, even being a good conductor of heat, tungsten’s high atomic number (Z) poses challenges in plasma stability. On the other hand, graphite when paired with an efficient cooling system for controlling surface temperature rise is a superior armor material due to its affordability, ease of availability, and better compatibility with plasma environments. It is recommended as the preferred armor material for the upgraded divertor mockup in the PST system.


Corresponding author: Muhammad Ilyas, Pakistan Institute of Engineering and Applied Sciences (PIEAS), P. O. Nilore, 45650, Islamabad, Pakistan, E-mail:

Acknowledgments

The authors acknowledge the support extended by the Pakistan Tokamak Plasma Research Institute (PTPRI) in conducting this research work. The computational resources provided by the Pakistan Institute of Engineering and Applied Sciences (PIEAS) are highly appreciated.

  1. Research ethics: The authors are strong advocates of the research ethics.

  2. Informed consent: Informed consent was obtained from all individuals included in this study.

  3. Author contributions: All authors have contributed to this research work. They accept the responsibility for the entire content of this manuscript and have approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors have no conflict of interest with the editors and reviewers.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2024-07-03
Accepted: 2024-10-25
Published Online: 2024-11-19
Published in Print: 2024-12-17

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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