An artificial neural network, combining signals from a large number of plasma diagnostics, was used to estimate the high- beta disruption boundary in the DIII-D tokamak. It is shown that inclusion of many diagnostic measurements results in a much more accurate prediction of the disruption boundary than that provided by the traditional Troyon limit. A trained neural network constitutes a non-linear, non-parametric model of the disruption boundary. Through the analysis of the input-output sensitivities, the relative statistical significance of various diagnostic measurements (plasma parameters) for the determination of the disruption boundary is directly assessed and the number of diagnostics used by the neural network model is reduced to the necessary minimum. The neural network is trained to map the disruption boundary throughout most of the discharge. As a result, it can predict the high- beta disruption boundary on a time-scale of the order of 100 ms (much longer than the precursor growth time), which makes this approach ideally suitable for real time application in a disruption avoidance scheme. Owing to the relative simplicity of the required computations, the neural network is easily implemented in a real time system. A prototype of the neural network disruption alarm was installed within the DIII-D digital plasma control system, and its real time operation, with a typical time resolution of 10 ms, was demonstrated

The electron temperature perturbation produced by internal disruptions in the center of the Oak Ridge Tokamak (ORMAK) is followed with a multi-chord soft x-ray detector array. The space-time evolution is found to be diffusive in •character, wi ~h a .conduction coefficient larger by a factor of 2.5 -15 than that implied by the energy containment time, apparently because it is a measurement for the small group of electrons whose energies exceed the cut-off energy of the detectors.A useful model for understanding the energy transport governing the behavior of tokamak discharges is a three-region plasma model. The central core region (r < aD, the disruption radius) suffers internal d ..

The authors study the hypothesis that sawtooth oscillations or internal disruptions are the result of a cyclic process in which the plasma core is resistively heated until the safety factor drops below unity, causing the m = 1 tearing mode to become unstable, to grow with an accelerating growth rate, and ultimately to flatten the electron temperature and safety factor profiles. A model based on this hypothesis compares favourably with experimental data from the Oak Ridge Tokamak (ORMAK) in explaining (1) the rate at which a sawtooth rises, (2) the radial dependence of the precursor and main sawtooth oscillation amplitudes, (3) the accelerating growth of the m = 1 precursor oscillations, and (4) the repetition time of the sawteeth. The heat lost from the central region during the internal disruption is found to transport diffusively through the exterior plasma with a conduction coefficient that agrees within a factor of two with the value inferred from the observed electron power balance.