Publication

Poloidal asymmetry in the narrow heat flux feature in the TCV scrape-off layer

Basil Duval, Christian Gabriel Theiler, Stefano Coda, Ivo Furno, Paolo Ricci, Holger Reimerdes, Benoît Labit, Joaquim Loizu Cisquella, Jan Horacek, Federico Nespoli, Federico David Halpern, Cedric Kar-Wai Tsui
2017
Journal Articles

Abstract

Heat flux profiles inferred from a reciprocating probe at the outer midplane of the TCV tokamak during inner wall limited discharges feature radial fall-off lengths that shorten near the last closed flux surface (LCFS) consistent with the so-called narrow feature. The narrow feature is significantly wider on the outboard side compared with that measured on the inner wall by infrared thermography, so it is difficult to discern from the main scrape-off layer feature. After small shifts were applied for alignment, the fraction of the power contained in the narrow feature matches between inboard and outboard measurements, and they scale together with plasma current I-p, suggesting that we are observing the same phenomenon. The outboard side fall-off length within the narrow feature is found to scale closely with the radial correlation length of the edge turbulence as expected if the narrow feature arises due to radially sheared E x B flows. This is found to hold true even for cases where the narrow feature is weak and the fall-off lengths are approaching that of the far scrape-off layer. After the small shifts for alignment, non-zero floating potential profiles were found to match between inboard and outboard sides. A simple model of polarization and diamagnetic cross-field currents is described, which is consistent with the shape of these floating potential profiles. The model predicts that the floating potential at the LCFS must be negative, which supports the argument to shift the upstream measurements. The predicted currents are also consistent with the E x B flows believed to cause the narrow feature. The model is used to predict the magnitude of the floating potential of the LCFS, and the results are found to match measurements for all values of Ip. This paper therefore demonstrates consistency between the measurements of the narrow feature on the inboard and outboard sides of the plasma, as well as consistency between the measurements, non-linear turbulence simulations, and analytical models of the narrow feature arising from sheared E x B flows.

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Abstract

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Tokamak

A tokamak (ˈtoʊkəmæk; токамáк) is a device which uses a powerful magnetic field to confine plasma in the shape of a torus. The tokamak is one of several types of magnetic confinement devices being developed to produce controlled thermonuclear fusion power. , it was the leading candidate for a practical fusion reactor. Tokamaks were initially conceptualized in the 1950s by Soviet physicists Igor Tamm and Andrei Sakharov, inspired by a letter by Oleg Lavrentiev. The first working tokamak was attributed to the work of Natan Yavlinsky on the T-1 in 1958.

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Predictive analytics is a form of business analytics applying machine learning to generate a predictive model for certain business applications. As such, it encompasses a variety of statistical techniques from predictive modeling and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. It represents a major subset of machine learning applications; in some contexts, it is synonymous with machine learning.

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Plasma () is one of four fundamental states of matter, characterized by the presence of a significant portion of charged particles in any combination of ions or electrons. It is the most abundant form of ordinary matter in the universe, being mostly associated with stars, including the Sun. Extending to the rarefied intracluster medium and possibly to intergalactic regions, plasma can be artificially generated by heating a neutral gas or subjecting it to a strong electromagnetic field.

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