ASTI: Data assimilation system for particle and heat transport in toroidal plasmas

Morishita, Yuya; Murakami, S.; Yokoyama, M.; Ueno, G.

doi:10.1016/j.cpc.2022.108287

Cited by 4 publications

(7 citation statements)

References 21 publications

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“…The proposed control system for LHD comprises a DA system “ASTI” (Assimilation System for Toroidal plasma Integrated simulation) 21 , which uses an integrated transport simulation code TASK3D 22 , 23 as the system model for helical fusion plasmas and implements the EnKF and PF as DA techniques. TASK3D computes the heat and particle transport in a toroidal fusion plasma as a one-dimensional (1D) problem for the normalized minor radius.…”

Section: Methodsmentioning

confidence: 99%

“…Table 1 lists the state ( and ), target state ( ), and observation variables ( ) used in the experiment. The radial profiles of the state variables were defined on 11 grid points ( ) in the state vector, and B-spline interpolation 30 was used to transform the radial profiles to those on the TASK3D grid (60 grid points) 21 . The factors for the thermal diffusivities and were introduced to optimize and with large uncertainties.…”

Section: Methodsmentioning

confidence: 99%

“…The standard deviation of the system noise was fixed at the values listed in Table 2 . The values of the standard deviation for , , , and were determined based on a previous study on data assimilation for the LHD plasmas 18 , 21 . The noises for were set to large values because ASTI did not compute the density transport.…”

Section: Methodsmentioning

confidence: 99%

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First application of data assimilation-based control to fusion plasma

Morishita,

Murakami,

Kenmochi

et al. 2024

Sci Rep

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Magnetic fusion plasmas, which are complex systems comprising numerous interacting elements, have large uncertainties. Therefore, future fusion reactors require prediction-based advanced control systems with an adaptive system model and control estimation robust to uncertainties in the model and observations. To address this challenge, we introduced a control approach based on data assimilation (DA), which describes the system model adaptation and control estimation based on the state probability distribution. The first implementation of a DA-based control system was achieved at the Large Helical Device to control the high temperature plasma. The experimental results indicate that the control system enhanced the predictive capability using real-time observations and adjusted the electron cyclotron heating power for a target temperature. The DA-based control system provides a flexible platform for advanced control in future fusion reactors.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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First application of data assimilation-based control to fusion plasma

Morishita,

Murakami,

Kenmochi

et al. 2024

Sci Rep

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show abstract

“…In an application at LHD, an ensemble Kalman filter and smoother was used for prediction of density and temperature profiles, based on the TASK3D integrated modeling code as a system model. The method was implemented in the ASTI data assimilation system and results of numerical experiments were shown, aiming at control of the central electron and ion temperatures, using electron cyclotron heating power as a control parameter [16]. The technique will be used for control of real plasmas at LHD and possibly other devices.…”

Section: Api and Statistics (Api)-g Verdoolaegementioning

confidence: 99%

Summary report of the 4th IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis (FDPVA)

Vicente

Mazon

et al. 2023

Nucl. Fusion

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The objective of the fourth Technical Meeting on Fusion Data Processing, Validation and Analysis was to provide a platform during which a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolating needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics and synthetic diagnostics data analysis (including image processing, regression analysis, inverse problems, deep learning, machine learning, big data and physics-based models for control) reported at the meeting. The progress in these areas highlight trends observed in current major fusion confinement devices. A special focus is dedicated on data analysis requirements for ITER and DEMO with a particular attention paid to Artificial Intelligence for automatization and improving reliability of control processes.

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“…The radially constant factor, C i , was estimated by best fitting the measured T i profiles of the relevant cases. However, it was found that this model cannot reproduce the actual temporal change of T i [5].…”

mentioning

confidence: 92%

Plausible Model Improvement Utilizing the Information Obtained from Data Assimilation

YOKOYAMA,

MORISHITA,

MURAKAMI

2024

Plasma and Fusion Research

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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. c

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ASTI: Data assimilation system for particle and heat transport in toroidal plasmas

Cited by 4 publications

References 21 publications

First application of data assimilation-based control to fusion plasma

First application of data assimilation-based control to fusion plasma

Summary report of the 4th IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis (FDPVA)

Plausible Model Improvement Utilizing the Information Obtained from Data Assimilation

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