Plasma and Fusion Research

Volume 19, 1203006 (2024)

Rapid Communications

Plausible Model Improvement Utilizing the Information Obtained from Data Assimilation
Masayuki YOKOYAMA1,2,3), Yuya MORISHITA4) and Sadayoshi MURAKAMI4)
1)
National Institute for Fusion Science/Rokkasho Research Center, National Institutes of Natural Sciences, Rokkasho, Aomori 039-3212, Japan
2)
The Graduate University for Advanced Studies, SOKENDAI, Kanagawa 240-0115, Japan
3)
The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa 190-8562, Japan (visiting)
4)
Department of Nuclear Engineering, Kyoto University, Kyoto 615-8540, Japan
(Received 31 October 2023 / Accepted 13 December 2023 / Published 8 February 2024)

Abstract

Data assimilation technique implemented in fusion research has enhanced the modeling capability. The quantitative "gap" between the original model (typically based on physics considerations and/or empirical approach) and the optimized model (obtained through data assimilation) can be utilized to improve the original model to align with the measured data. Such a procedure is proposed here by taking the model of the heat diffusivity of plasmas as an example. It successfully elucidates relevant parameters recognized in the experiment but were missing in the original model, demonstrating the efficiency of the proposed procedure.

Keywords

model improvement, data assimilation, multivariate regression, information criterion

DOI: 10.1585/pfr.19.1203006

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References

  • [1] Y. Morishita et al., Nucl. Fusion 60, 056001 (2020).
  • [2] Y. Morishita et al., J. Comp. Science 72, 102079 (2023).
  • [3] M. Osakabe et al., Nucl. Fusion 62, 042019 (2022).
  • [4] S. Murakami et al., Plasma Phys. Control. Fusion 57, 054009 (2015).
  • [5] Y. Morishita et al., Comp. Phys. Comm. 274, 108287 (2022).
  • [6] H. Akaike, "Information theory and an extension of the maximum likelihood principle", 1973 Proceedings of the 2nd International Symposium on Information Theory, Petrov, B.N., and Caski, F. (eds.), Akadimiai Kiado, Budapest 267.
  • [7] N. Sugiura, Communications in Statistics - Theory and Methods 7, 13 (1978).
  • [8] H. Takahashi et al., Nucl. Fusion 57, 086029 (2017).
  • [9] M. Yokoyama and H. Yamaguchi, Nucl. Fusion 60, 106024 (2020).