Synthetic diagnostics platform for fusion plasmas (invited)

Ling, Shi; Valeo, E. J.; Tobias, B.; Kramer, G. J.; Hausammann, Loïc; Tang, W. M.; Chen, M.

doi:10.1063/1.4961553

Cited by 34 publications

(23 citation statements)

References 23 publications

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“…For this comparison, GTC has been interfaced with the open source Synthetic Diagnostic Platform (SDP) [14], where GTC simulation data is processed through SDP. Figure 11(a) shows |δT e |/T e0 , obtained from GTC via SDP, for both the kinetic EFIT (black) and kick model (magenta) fast-ion density profiles and the experimental data (red).…”

Section: Comparison To Experimental Valuesmentioning

confidence: 99%

Verification and validation of integrated simulation of energetic particles in fusion plasmas

et al. 2019

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This paper reports verification and validation of linear simulations of Alfvén eigenmodes in the current ramp phase of DIII-D L-mode discharge #159243 using gyrokinetic, gyrokinetic-MHD hybrid, and eigenvalue codes. Using a classical fast ion profile, all simulation codes find that reversed shear Alfvén eigenmodes (RSAE) are the dominant instability. The real frequencies from all codes have a coefficient of variation of less than 5% for the most unstable modes with toroidal mode number n = 4 and 5. The simulated RSAE frequencies agree with experimental measurements if the minimum safety factor q min is adjusted, within experimental errors. The simulated growth rates exhibit greater variation, and simulations find that pressure gradients of thermal plasmas make a significant contribution to the growth rates. Mode structures of the dominant modes agree well among all codes. Moreover, using a calculated fast ion profile that takes into account the diffusion by multiple unstable modes, a toroidal Alfvén eigenmode (TAE) with n = 6 is found to be unstable in the outer edge, consistent with the experimental observations. Variations of the real frequencies and growth rates of the TAE are slightly larger than those of the RSAE. Finally, electron temperature fluctuations and radial phase shifts from simulations show no significant differences with the experimental data for the strong n = 4 RSAE, but significant differences for the weak n = 6 TAE. The verification and validation for the linear Alfvén eigenmodes is the first step to develop an integrated simulation of energetic particles confinement in burning plasmas incorporating multiple physical processes. Nuclear Fusion

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Section: Comparison To Experimental Valuesmentioning

confidence: 99%

Verification and validation of integrated simulation of energetic particles in fusion plasmas

et al. 2019

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“…Such tools have been adopted for modeling of plasma instruments and data interpretation. The multiphysics model for an instrument is sometimes called a synthetic diagnostic [207][208][209]. In parallel to hardware-based instruments for diagnosis of a real plasma, a synthetic diagnostic can be regarded as a numerical instrument for diagnosis of a numerical model of a plasma, as illustrated in Fig.…”

Section: Synthetic Instruments and Datamentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

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Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasmaprocessing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

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“…Creating a virtual model by theoretical calculations and measurements with a virtual instrument on a computer is called synthetic diagnostics. The virtual experiment environment platform, including them, is called the synthetic diagnostics platform [11]. The synthetic diagnostics enables us to compare theoretical and experimental values, which is used to identify parameters that cannot be directly measured in the experiment and verify a theory.…”

Section: Development Of Synthetic Diagnostics Platformmentioning

confidence: 99%

Visible Light Tomography Considering Reflection Light in a Small Tokamak Device PHiX

Munechika

Tsutsui

Tsuji‐Iio

2021

Plasma and Fusion Research

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In a small tokamak, the visible light emission is observed and used to investigate plasmas' behavior with a fast visible camera. However, the reflected light causes a systemic error in measuring visible light emitted from the plasma. In this paper, we managed to overcome the reflection effect with the ray-tracing technique which is utilized in a synthetic diagnostic platform of the small tokamak device PHiX at Tokyo Institute of Technology using Raysect and CHERAB python libraries. We successfully evaluated the amount of reflected light and obtained tomographic reconstruction images from simulated and experimental data with the Tikhonov-Phillips regularization and the L-curve method to choose an optimal regularization parameter. We also proposed to project the contour of a reconstruction image onto a camera image to validate tomography results.

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Synthetic diagnostics platform for fusion plasmas (invited)

Cited by 34 publications

References 23 publications

Verification and validation of integrated simulation of energetic particles in fusion plasmas

Verification and validation of integrated simulation of energetic particles in fusion plasmas

2022 Review of Data-Driven Plasma Science

Visible Light Tomography Considering Reflection Light in a Small Tokamak Device PHiX

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