Plasma and Fusion Research

Volume 20, 1403034 (2025)

Regular Articles

Development of Data Assimilation System for Temperature and Density Control in Helical Plasmas

Yuya MORISHITA¹⁾, Sadayoshi MURAKAMI¹⁾, Naoki KENMOCHI^2,4), Masayuki YOKOYAMA^2,4), Genta UENO^3,4), Masaki OSAKABE^2,4)

1): Department of Nuclear Engineering, Kyoto University, Kyoto 615-8540, Japan
2): National Institute for Fusion Science, National Institutes of Natural Sciences, Toki 509-5292, Japan
3): The Institute of Statistical Mathematics, Tokyo 190-8562, Japan
4): The Graduate University for Advanced Studies, SOKENDAI, Toki 509-5292 and Tokyo 190-8562, Japan

(Received 20 February 2025 / Accepted 8 May 2025 / Published 4 July 2025)

Abstract

We develop a real-time adaptive predictive control system based on data assimilation (DA) for the temperature and density of helical fusion plasmas. The DA-based control approach enables the harmonious integration of measurement, heating, fueling, and simulation and can provide a flexible platform for adaptive model predictive control. The core part of the control system, ASTI, is built upon the integrated simulation code TASK3D and a data assimilation framework DACS. DACS integrates adaptation of the predictive model (digital twin) to the actual system using real-time measurements and control estimation that is robust against model and observation uncertainties. We perform numerical experiments using ASTI to control the electron temperature profile and density of a virtual plasma generated by TASK3D. The results demonstrate that ASTI can effectively drive the virtual plasma state toward the target state while bridging the gap between the digital twin and the virtual plasma. Furthermore, the numerical experiments clarify the effects of hyperparameters in the DA-based control approach on control performance.

Keywords

ASTI, adaptive model predictive control, data assimilation, integrated simulation, real-time prediction

DOI: 10.1585/pfr.20.1403034

Full Text

PDF format (6.3Mbytes) PDF Download

References

[1] M. Liu et al., J. Manuf. Syst. 58, 346 (2021).
[2] F. Emmert-Streib et al., PNAS nexus 3, pgae456 (2024).
[3] Z. Lei et al., Nat. Commun. 14, 5604 (2023).
[4] Y. Morishita et al., J. Comput. Sci. 72, 102079 (2023).
[5] A. Gettelman et al., Sci. Adv. 8, eabn3488 (2022).
[6] M. Li et al., Nat. Geosci. 1, 1299 (2024).
[7] G. Ueno et al., Sola 3, 5 (2007).
[8] Y. Morishita et al., Sci. Rep. 14, 137 (2024).
[9] Y. Morishita et al., Comput. Phys. Commun. 274, 108287 (2022).
[10] S. Murakami et al., Plasma Phys. Control. Fusion 57, 054009 (2015).
[11] A. Sakai et al., Plasma Fusion Res. 10, 3403048 (2015).
[12] J. Seo et al., Nature 626, 746 (2024).
[13] J. Degrave et al., Nature 602, 414 (2022).
[14] J. Kates-Harbeck et al., Nature 568, 526 (2019).
[15] A. Murari et al., Nucl. Fusion 61, 036027 (2021).
[16] G. Evensen, Ocean Dyn. 53, 343 (2003).
[17] G. Kitagawa, J. Comput. Graph. Stat. 5, 1 (1996).
[18] S. Hirshman et al., Comput. Phys. Commun. 43, 143 (1986).
[19] Y. Morishita et al., J. Fusion Energy 41, 1 (2022).
[20] M. Hughes and D. Post, J. Comput. Phys. 28, 43 (1978).
[21] M. Honda, Comput. Phys. Commun. 231, 94 (2018).
[22] H. Funaba et al., Sci. Rep. 12, 15112 (2022).