Phenomenology of high density disruptions in the TFTR tokamak

Fredrickson, E.; McGuire, K.; Bell, M.G.; Bush, C. E.; Budny, R.; Janos, A.; Mansfield, D. K.; Nagayama, Y.; Park, H.K.; Schivell, J.; Taylor, G.; Zarnstorff, M.C.

doi:10.1088/0029-5515/33/1/i13

Cited by 24 publications

(13 citation statements)

References 14 publications

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“…Interestingly, in both prediction cases the experimental data points used by the net to mode. This interpretation reminds us the works already done in tokamaks suggesting that the disruptive instabilities would be caused by a "cold bubble" moving towards the plasma center, as it could be experimentally observed in the electron temperature profile [19,20].…”

Section: Disruption Forecastsupporting

confidence: 84%

“…some disturbance phenomena develop first around the central part of the plasma column (visualized by the soft X ray central detector), and then the plasma region within the q = 2 magnetic surface (monitored by the Mirnov coils) is affected, destabilizing the m = 2 MHD mode. This interpretation even reminds us of some work already done in tokamaks suggesting that the disruptive instabilities are caused by a 'cold bubble' moving towards the plasma centre, as can already be observed experimentally in the electron temperature profile [19,20]. However, before drawing any definite conclusion, further and more careful investigations will be necessary in order to shed more light about the reality or not of this process.…”

Section: Disruption Forecastsupporting

confidence: 71%

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Forecast of TEXT plasma disruptions using soft X rays as input signal in a neural network

1999

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A feed-forward neural network with two hidden layers is used in this work to forecast major and minor disruptive instabilities in TEXT discharges. Using soft X-ray signals as input data, the neural net is trained with one disruptive plasma pulse, and a different disruptive discharge is used for validation. After being properly trained the networks, with the same set of weights, is then used to forecast disruptions in two others different plasma pulses. It is observed that the neural net is able to predict the incoming of a disruption more than 3 ms in advance. This time interval is almost three times longer than the one already obtained previously when magnetic signal from a Mirnov coil was used to feed the neural networks with. To our own eye we fail to see any indication of an upcoming disruption from the experimental data this far back from the time of disruption. Finally, from what we observe in the predictive behavior of our network, speculations are made whether the disruption triggering mechanism would be associated to an increase of the m = 2 magnetic island, that disturbs the central part of the plasma column afterwards or, in face of the results from this work, the initial perturbation would have occurred first in the central part of the plasma column, within the q = 1 magnetic surface, and then the m = 2 MHD mode would be destabilized afterwards.

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Section: Disruption Forecastsupporting

confidence: 84%

Section: Disruption Forecastsupporting

confidence: 71%

Forecast of TEXT plasma disruptions using soft X rays as input signal in a neural network

1999

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“…The EQ signals the beginning of the disruption, but it will be shown later that there is not a precise time for its onset, when it is measured with high time resolution. The erosion of the electron temperature profile has been observed in different tokamaks 3,5,18,19 to have an m/nϭ1/1 structure. We will show in this paper that this m/nϭ1/1 erosion is precipitated by an off-axis erosion starting from the O point of the m/nϭ2/1 mode at the low field side ͑LFS͒.…”

Section: Introductionmentioning

confidence: 99%

High resolution temperature and density profiles during the energy quench of density limit disruptions in Rijnhuizen tokamak project

Salzedas¹,

Hokin²,

Schüller³

et al. 2002

Physics of Plasmas

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Measurements of the electron temperature, T e , and density, n e , during the energy quench of a major disruption showed that the onset of T e erosion in the neighborhood of the m/nϭ2/1 O point at the low field side ͑LFS͒ accelerates the well-known m/nϭ1/1 erosion of the core temperature. During this phase T e (r) is only partially flat in the region between the qϭ2 and the qϭ1 surfaces and n e (r) decreases in the core and increases inside the m/nϭ2/1 island. Immediately after the flattening of T e (r) a large peak in T e and to a lesser extent in n e has been observed. This peak is radially localized at the qϭ2 radius at the LFS, is very short lived and is poloidally asymmetric. T e profiles measured by the heterodyne radiometer and the Thomson scattering agree very well up to the time T e (r) flattens but afterwards can be a factor of two different.

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“…A large m = 1 kink mode moves a bubble of cold plasma from the edge into the centre and leads to the formation of a hollow current density profile and to destabilization of the m = 2, n = 1 mode. This model describes typical features of density limit disruptions in the TFTR plasma (m = 1 cold bubble, final destabilization of the m = 2, n = 1 mode) [19]. However, simulations of the current profile redistribution before the density limit disruptions in NUCLEAR FUSION, Vo1.…”

Section: Introductionmentioning

confidence: 99%

Coupling of internal m=1 and m=2 modes at density limit disruptions in the T-10 tokamak

et al. 1994

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A soft X ray imaging system consisting of three arrays of silicon surface barrier diodes is applied to the tomographic analysis of internal plasma perturbations in the T-10 tokamak (R, = 1.5 m, a = 0.32 m). It is found that density limit disruptions in a plasma with a high safety factor at the edge (qa = 3.5-4.5) are associated with joint rotation of the m = 2, n = 1 and m = 1, n = 1 modes, overlapping at the energy quench stage. Electron cyclotron resonance heating is used to prevent density limit disruptions or to recover stable operation of the discharge after the energy quench at the density limit,

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Phenomenology of high density disruptions in the TFTR tokamak

Cited by 24 publications

References 14 publications

Forecast of TEXT plasma disruptions using soft X rays as input signal in a neural network

Forecast of TEXT plasma disruptions using soft X rays as input signal in a neural network

High resolution temperature and density profiles during the energy quench of density limit disruptions in Rijnhuizen tokamak project

Coupling of internal m=1 and m=2 modes at density limit disruptions in the T-10 tokamak

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