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Efficient off-axis current drive is crucial for economic, steady-state tokamak fusion power plants. “Top Launch” electron cyclotron current drive (ECCD) is one promising method for driving strong off-axis current to achieve the desired broad current profile for the Advanced Tokamak regime. New simulations show that by launching the electron cyclotron (EC) waves downwards (or upwards) nearly parallel to the resonance plane with a large toroidal steering, higher current drive efficiency can be obtained at large radii owing to the large Doppler shift, wave-particle interactions on HFS of the plasma, and the long absorption path. Implementations on CFETR and DIII-D are simulated identifying clear benefits and optimal configurations. The design of a prototype test being installed on DIII-D is presented, which will experimentally validate whether top launch ECCD can be an improved efficiency current drive technique for future fusion reactors.

Multi-layer perceptron based neural networks (NNs) are trained to predict the Troyon no-wall beta limits due to the onset of low-n (n = 1–3, n is the toroidal mode number) ideal external kink instabilities in tokamak plasmas. It is found that a well trained NN can predict the n = 1 no-wall beta limit within 10% relative error. The NN performance is somewhat worse for the n = 2 and 3 no-wall beta limits, but still a relative error within 20% is achievable. The trained NNs well reproduce the known dependences of the no-wall beta limits on the plasma pressure (pressure peaking factor) and current (plasma internal inductance) profiles. Other scalings are also easily established with NNs, for parametric dependences such as on the aspect ratio, the elongation and triangularity of the plasma boundary shape. The results show that the semi-analytically generated training database can be used to train NNs for predicting the no-wall limit in realistic experiments. These NN-based Troyon beta limit predictors can be incorporated into integrated modeling platforms, or directly implemented as a real time stability estimator for the purpose of disruption avoidance or mitigation during experiments. [ABSTRACT FROM AUTHOR]