The numerical tokamak project: simulation of turbulent transport

Cohen, B. I.; Barnes, D. C.; Dawson, J. M.; Hammett, G. W.; Lee, W.W.; Kerbel, G. D.; Leboeuf, J. N.; Liewer, P. C.; Tajima, T.; Waltz, R. E.

doi:10.1016/0010-4655(94)00166-y

Cited by 24 publications

(6 citation statements)

References 31 publications

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“…Figure 1 shows the results of applying this diagnostic to the calculation of the terms in equation ( 7) for a PG3EQ simulation using the parameters of [34], with the adiabatic-electron model of [35] (also used in [8,9]). (The terms of equation ( 8) have also been examined and yield qualitatively similar conclusions x from the simulation shown in figure 1.…”

Section: Itg Turbulence: Sources and Sinks Of Zonal Flowmentioning

confidence: 99%

Gyrokinetic simulations of ETG and ITG turbulence

et al. 2007

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We report on the resolution of a significant discrepancy between published continuum-code simulations and subsequent global particle-in-cell (PIC) simulations of electron-temperature-gradient (ETG) turbulence. Our investigations, using gyrokinetic δf -PIC-and continuum-code simulations and analytical theory, strongly support the conclusion from the earlier continuum-code simulations that ETG turbulence can drive the electron thermal conductivity χ e large enough to be significant in some tokamaks. A successful ETG-turbulence benchmark between δf -PIC and continuum codes for ETG turbulence has also been completed. Scans in the magnetic shear show an abrupt transition to a high-χ e state as the shear is increased from the benchmark value of s = 0.1 to above s = 0.4. When nonadiabatic ions are used, this abrupt transition is absent, and χ e reaches values consistent with transport analyses of DIII-D, JET, JT60-U and NSTX discharges. The balances of zonal-flow driving and damping terms in late-time quasi-steady phase of ITG turbulence have been unfolded using a new run-time gyrokinetic-simulation diagnostic. The zonal flow level is set by a balance of large driving and damping terms which almost cancel each other. The driving is found to be mostly by the Reynolds stress, while the dissipation is mostly by the linear (transittime) damping terms. It is also shown that useful zonal-flow-balance information can be obtained with spatially localized samples at as few as four poloidal locations. Real-geometry simulations have been undertaken, using the nonlinear δf -PIC gyrokinetic code SUMMIT/PG3EQ NC, of the DIII-D 'Cyclone' shot #81499 and of shot #118561, which had broad-wavenumber-range density fluctuation measurements. Real geometry is found to have a significant effect on the transport rates, even though the effect on the linear growth rates is often modest.

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Section: Itg Turbulence: Sources and Sinks Of Zonal Flowmentioning

confidence: 99%

Gyrokinetic simulations of ETG and ITG turbulence

et al. 2007

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“…Hence, χ GB ≡(ρ/L T )ρv th . The nd Revision WMN -4-ETG Benchmark ion thermal conductivity observed in numerical simulations of ITG turbulence rarely exceeds two ion gyro-Bohms [5][6][7][8][9] and there has been substantial controversy regarding how strong ETG turbulence is and whether it can produce sufficiently large electron thermal transport to be experimentally relevant. Some workers 10-12 have reported relatively low levels of electron thermal transport, while others [13][14][15][16][17][18][19][20] report electron thermal conductivities from microturbulence simulations exceeding ten electron gyro-Bohms.…”

Section: Introductionmentioning

confidence: 99%

Characterizing electron temperature gradient turbulence via numerical simulation

et al. 2006

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Numerical simulations of electron temperature gradient (ETG) turbulence are presented which characterize the ETG fluctuation spectrum, establish limits to the validity of the adiabatic ion model often employed in studying ETG turbulence, and support the tentative conclusion that plasmaoperating regimes exist in which ETG turbulence produces sufficient electron heat transport to be experimentally relevant. We resolve prior

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“…Multiphysics simulation tools are now available to design and simulate plasma experiments, up to the full-scale experiments in realistic geometries [206]. Such tools have been adopted for modeling of plasma instruments and data interpretation.…”

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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The numerical tokamak project: simulation of turbulent transport

Cited by 24 publications

References 31 publications

Gyrokinetic simulations of ETG and ITG turbulence

Gyrokinetic simulations of ETG and ITG turbulence

Characterizing electron temperature gradient turbulence via numerical simulation

2022 Review of Data-Driven Plasma Science

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