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Estimation of the radial tungsten concentration profiles from soft X-ray measurements at WEST with Bayesian integrated data analysis

Hao Wu (UGent) , Axel Jardin, Didier Mazon and Geert Verdoolaege (UGent)
(2024) JOURNAL OF FUSION ENERGY. 43(1).
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Abstract
The accumulation of heavy impurities like tungsten in the plasma core of fusion devices can cause significant radiative power losses or even lead to a disruption. It is therefore crucial to monitor the tungsten impurity concentration. In this paper, we follow the integrated data analysis approach using Bayesian probability theory to jointly estimate tungsten concentration profiles and kinetic profiles from soft X-ray, interferometry and electron cyclotron emission measurements. As the full Bayesian inference using Markov chain Monte Carlo sampling is time-consuming, we also discuss emulation of the inference process using neural networks, with a view to real-time implementation.
Keywords
Nuclear Energy and Engineering, Nuclear and High Energy Physics, Bayesian inference, Tokamak, Integrated data analysis, Tungsten, Tomography, Gaussian process, Neural network, NUMERICAL-METHODS, SYSTEM

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MLA
Wu, Hao, et al. “Estimation of the Radial Tungsten Concentration Profiles from Soft X-Ray Measurements at WEST with Bayesian Integrated Data Analysis.” JOURNAL OF FUSION ENERGY, vol. 43, no. 1, 2024, doi:10.1007/s10894-024-00402-1.
APA
Wu, H., Jardin, A., Mazon, D., & Verdoolaege, G. (2024). Estimation of the radial tungsten concentration profiles from soft X-ray measurements at WEST with Bayesian integrated data analysis. JOURNAL OF FUSION ENERGY, 43(1). https://doi.org/10.1007/s10894-024-00402-1
Chicago author-date
Wu, Hao, Axel Jardin, Didier Mazon, and Geert Verdoolaege. 2024. “Estimation of the Radial Tungsten Concentration Profiles from Soft X-Ray Measurements at WEST with Bayesian Integrated Data Analysis.” JOURNAL OF FUSION ENERGY 43 (1). https://doi.org/10.1007/s10894-024-00402-1.
Chicago author-date (all authors)
Wu, Hao, Axel Jardin, Didier Mazon, and Geert Verdoolaege. 2024. “Estimation of the Radial Tungsten Concentration Profiles from Soft X-Ray Measurements at WEST with Bayesian Integrated Data Analysis.” JOURNAL OF FUSION ENERGY 43 (1). doi:10.1007/s10894-024-00402-1.
Vancouver
1.
Wu H, Jardin A, Mazon D, Verdoolaege G. Estimation of the radial tungsten concentration profiles from soft X-ray measurements at WEST with Bayesian integrated data analysis. JOURNAL OF FUSION ENERGY. 2024;43(1).
IEEE
[1]
H. Wu, A. Jardin, D. Mazon, and G. Verdoolaege, “Estimation of the radial tungsten concentration profiles from soft X-ray measurements at WEST with Bayesian integrated data analysis,” JOURNAL OF FUSION ENERGY, vol. 43, no. 1, 2024.
@article{01HW04HWH8R2EZQSS8NGP9YXRB,
  abstract     = {{The accumulation of heavy impurities like tungsten in the plasma core of fusion devices can cause significant radiative power losses or even lead to a disruption. It is therefore crucial to monitor the tungsten impurity concentration. In this paper, we follow the integrated data analysis approach using Bayesian probability theory to jointly estimate tungsten concentration profiles and kinetic profiles from soft X-ray, interferometry and electron cyclotron emission measurements. As the full Bayesian inference using Markov chain Monte Carlo sampling is time-consuming, we also discuss emulation of the inference process using neural networks, with a view to real-time implementation.}},
  articleno    = {{9}},
  author       = {{Wu, Hao and Jardin, Axel and Mazon, Didier and Verdoolaege, Geert}},
  issn         = {{0164-0313}},
  journal      = {{JOURNAL OF FUSION ENERGY}},
  keywords     = {{Nuclear Energy and Engineering,Nuclear and High Energy Physics,Bayesian inference,Tokamak,Integrated data analysis,Tungsten,Tomography,Gaussian process,Neural network,NUMERICAL-METHODS,SYSTEM}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{13}},
  title        = {{Estimation of the radial tungsten concentration profiles from soft X-ray measurements at WEST with Bayesian integrated data analysis}},
  url          = {{http://doi.org/10.1007/s10894-024-00402-1}},
  volume       = {{43}},
  year         = {{2024}},
}
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