Physical model assessment of the energy confinement time scaling in stellarators

Dinklage, A.; Maaßberg, H.; Preuss, R.; Turkin, Y.; Yamada, H.; Ascasíbar, E.; Beidler, C. D.; Funaba, H.; Harris, J. H.; Kus, A.; Murakami, S.; Okamura, S.; Sano, F.; Stroth, U.; Suzuki, Y.; Talmadge, J. N.; Tribaldos, V.; Watanabe, K. Y.; Werner, A.; Weller, A.; Yokoyama, M.

doi:10.1088/0029-5515/47/9/025

Cited by 40 publications

(38 citation statements)

References 22 publications

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“…The situation becomes different if the ions dominate the energy flux. First predictive transport simulations for the W7-X "standard" configuration with both density and power scans (for ECRH, p-NBI and n-NBI) follow roughly the τ E of ISS04 but with an improvement factor of 2 to 3 [34] which reflects the very good neoclassical confinement. In Fig.…”

Section: B the Magnetic Configurationsmentioning

confidence: 84%

“…In the range 2 MW ≤ P ≤ 10 MW, however, τ E (P ) is approximated in this form leading to α P = −0.64 for TJ-II, α P = −0.55 for LHD and α P = −0.48 for the W7-X configuration. These values can be compared with simple theory expectations [34]: for the 1/ν-regime (with the most unfavorable temperature dependence of the transport coefficients), α P = −3/5 for the plateau regime (this is the Lackner-Gottardi scaling for tokamaks), α P = 0 for transport coefficients independent of temperature, α P = 1/3 for the stellarator √ ν-regime, and, finally, α P = 1 in the tokamak banana regime. In case of the ion and electron energy fluxes being comparable but with both species in different transport regimes, τ E cannot be expressed in the form of a simple scaling law [34] (which is only possible if the particles dominating the total energy flux are in a "pure" transport regime).…”

Section: Comparison Of Neoclassical Confinementmentioning

confidence: 99%

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Neoclassical transport simulations for stellarators

et al. 2011

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The benchmarking of the thermal neoclassical transport coefficients is described using examples of the Large Helical Device (LHD) and TJ-II stellarators. The thermal coefficients are evaluated by energy convolution of the mono-energetic coefficients obtained by direct interpolation or neural network techniques from databases precalculated by different codes. The temperature profiles are calculated by a predictive transport code from the energy balance equations with the ambipolar radial electric field estimated from a diffusion equation to guarantee a unique and smooth solution although several solutions of the ambipolarity condition may exist when root-finding is invoked; the density profiles are fixed. The thermal transport coefficients as well as the ambipolar radial electric field are compared and very reasonable agreement is found for both configurations.Together with an additional W7-X case, these configurations represent very different degrees of neoclassical confinement at low collisionalities. The impact of the neoclassical optimization on the energy confinement time is evaluated and the confinement times for different devices predicted by transport modeling are compared with the standard scaling for stellarators. Finally, all configurations are scaled to the same volume for a direct comparison of the volume-averaged pressure and the neoclassical degree of optimization.

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Section: B the Magnetic Configurationsmentioning

confidence: 84%

Section: Comparison Of Neoclassical Confinementmentioning

confidence: 99%

Neoclassical transport simulations for stellarators

et al. 2011

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“…Since the treatment of the particle balance must rely on anomalous transport modelling (the main recycling sources are at the outer radii with low neoclassical transport), fixed density profiles are assumed in these simulations. Modules for NBI and ECR heating and current drive are self-consistently coupled to the transport code [34,35]. is very small).…”

Section: Bootstrap Current Simulations For W7-xmentioning

confidence: 99%

Momentum correction techniques for neoclassical transport in stellarators

Maaßberg¹,

Beidler²,

Turkin³

2009

Physics of Plasmas

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In the traditional neoclassical ordering for stellarators, mono-energetic transport coefficients are evaluated using the simplified Lorentz form of the pitch-angle collision operator which violates momentum conservation. In this paper, the parallel momentum balance with radial parallel momentum transport and viscosity terms is analysed, in particular with respect to the radial electric field. Next, the impact of momentum conservation in the stellarator lmfp-regime is estimated for the radial transport and the parallel electric conductivity. Two different momentum correction techniques are described based on mono-energetic transport coefficients calulated by the DKES code [W.I. van Rij and S.P. Hirshman, Phys. Fluids B 1, 563 (1989)]. The benchmarking of the parallel electric conductivity and of the bootstrap current is presented for a tokamak as well as for two W7-X stellarator configurations [G. Grieger et al., Phys. Fluids B 4, 2081(1992]. Finally, the impact of the momentum correction on the expected total bootstrap current is briefly analysed for two W7-X scenarios.

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“…Fokker-Planck kinetic equation) and energy confinement scaling laws. Model differences were found, e.g., in different β regimes of stellarator confinement data [16]. Artificial neural networks have several applications.…”

Section: More Applications Of Bayesian Probability Theory In Fusionmentioning

confidence: 99%

Integrated Data Analysis for Fusion: A Bayesian Tutorial for Fusion Diagnosticians

Dinklage

Dreier

Fischer

et al. 2008

AIP Conference Proceedings

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Abstract. Integrated Data Analysis (IDA) offers a unified way of combining information relevant to fusion experiments. Thereby, IDA meets with typical issues arising in fusion data analysis. In IDA, all information is consistently formulated as probability density functions quantifying uncertainties in the analysis within the Bayesian probability theory. For a single diagnostic, IDA allows the identification of faulty measurements and improvements in the setup. For a set of diagnostics, IDA gives joint error distributions allowing the comparison and integration of different diagnostics results. Validation of physics models can be performed by model comparison techniques. Typical data analysis applications benefit from IDA capabilities of nonlinear error propagation, the inclusion of systematic effects and the comparison of different physics models. Applications range from outlier detection, background discrimination, model assessment and design of diagnostics. In order to cope with next step fusion device requirements, appropriate techniques are explored for fast analysis applications. Keywords DATA ANALYSIS FOR MAGNETIC CONFINEMENT FUSION DEVICESNext step fusion devices define several new requirements for data analysis. Since the demonstration of fusion reactor capabilities aims at steady-state operation, the data processing paradigm needs to be shifted from a shoot-and-collect to a view-and-react philosophy. Therefore, fast and reliable physics information is mandatory to use the experimental devices most efficiently and off-line analysis schemes as in pulsed devices will restrict operational capabilities. Long-time scales due to plasma-wall effects and magnetic field relaxation will require intelligent control schemes which employ physical models in multi-variate adaptive controllers.The major challenge for data analysis in magnetic fusion experiments is to link many different heterogeneous information sources [1]. The reason for the heterogeneity lies in the variety of methods in plasma diagnostics which is required to

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Physical model assessment of the energy confinement time scaling in stellarators

Cited by 40 publications

References 22 publications

Neoclassical transport simulations for stellarators

Neoclassical transport simulations for stellarators

Momentum correction techniques for neoclassical transport in stellarators

Integrated Data Analysis for Fusion: A Bayesian Tutorial for Fusion Diagnosticians

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