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Application of Data Assimilation to Transport Simulations of Tokamak Plasmas

Ryu E. ICHIKAWA¹⁾, Yuya MORISHITA¹⁾, Emi NARITA¹⁾, Mitsuru HONDA²⁾, Hajime URANO³⁾, Sadayoshi MURAKAMI¹⁾

1)

Department of Nuclear Engineering, Kyoto University, Nishikyo, Kyoto 615-8540, Japan

2)

Graduate School of Engineering, Kyoto University, Nishikyo, Kyoto 615-8530, Japan

3)

Naka Institute for Fusion Science and Technology, National Institutes for Quantum Science and Technology, Naka, Ibaraki 311-0193, Japan

(Received 15 January 2025 / Accepted 16 May 2025 / Published 13 August 2025)

Abstract

Data assimilation, a technique that uses actual measurements to optimize simulation models, is a powerful approach for achieving fast and accurate predictions of fusion plasma behavior. In this study, we validate the effectiveness of the data assimilation technique in the integrated simulation of tokamak plasmas. We use the data assimilation system ASTI, which has been successfully applied to real-time prediction and control of helical plasmas. We extend ASTI for transport simulation of tokamak plasmas and introduce a new data assimilation method that incorporates measurement error information. In this paper, we present simulation results using measurements from JT-60U plasma heated by neutral beam injection. Comparisons of several turbulent transport models are also provided. The results demonstrate that the data assimilation method is effective in tokamak simulation as well and expected to be useful for real-time prediction and control in the future.

Copyright (c) 2025 The Japan Society of Plasma Science and Nuclear Fusion Research

Keywords

integrated simulation, data assimilation, the ensemble Kalman filter, JT-60U, NBI heating

DOI: 10.1585/pfr.20.1403036

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References

• [1] F. Felici et al., Plasma Phys. Control. Fusion 54, 025002 (2012).
• [2] F. Felici et al., Nucl. Fusion 58, 096006 (2018).
• [3] J. Artaud et al., Nucl. Fusion 58, 105001 (2018).
• [4] Y. Morishita et al., Nucl. Fusion 60, (2020).
• [5] Y. Morishita et al., Comput. Phys. Commun. 274, 108287 (2022).
• [6] Y. Morishita et al., J. Comput. Sci. 72, 102079 (2023).
• [7] Y. Morishita et al., Sci. Rep. 14, 137 (2024).
• [8] T. Blanken et al., Fusion Eng. Des. 126, 87 (2018).
• [9] T. Bosman et al., Fusion Eng. Des. 170, 112510 (2021).
• [10] M.C.C. Messmer et al., Plasma Phys. Control. Fusion 61, 035011 (2019).
• [11] G. Evensen, Ocean Dyn. 53, 343 (2003).
• [12] A. Fukuyama, Proc. of 20th Fusion Energy Conf. (Vilamoura, Portugal, 2004).
• [13] K. Tani et al., J. Phys. Soc. Jpn. 50, 1726 (1981).
• [14] M. Hughes et al., J. Comput. Phys. 28, 43 (1978).
• [15] C.S. Chang et al., Phys. Fluids 25, 1493 (1982).
• [16] C.S. Chang et al., Phys. Fluids 29, 3314 (1986).
• [17] A. Fukuyama et al., Plasma Phys. Control. Fusion 37, 611 (1995).
• [18] M. Honda et al., Nucl. Fusion 46, 580 (2006).
• [19] M. Kikuchi et al., Plasma Phys. Control. Fusion 37, 1215 (1995).
• [20] H. Shirai et al., Plasma Phys. Control. Fusion 42, 1193 (2000).
• [21] H. Urano et al., Nucl. Fusion 53, 083003 (2013).
• [22] G. Ueno et al., Q. J. R. Meteorol. Soc. 142, 2055 (2016).
• [23] P. Gohil et al., Nucl. Fusion 38, 425 (1998).
• [24] Y. Koide et al., Rev. Sci. Instrum. 72, 119 (2001).
• [25] N. Hayashi et al., Nucl. Fusion 57, 126037 (2017).