Neutral-beam injection of up to 2.5 MW into plasmas in the ISX-B tokamak (R0 = 0.93 m, a = 0.27 m, BT = 0.9–1.5 T, Ip = 70–210 kA, n̄e = 2.5–10×1013 cm−3) has created plasmas with volume-averaged beta of up to ∼ 2.5%, peak beta values of up to ∼ 9%, and root-mean-square beta values of up to ∼ 3.5%. Energy confinement time is observed to decrease by about a factor of two as beam power goes from 0 to 2.5 MW; the decrease is caused predominantly by the electron confinement time falling below the predictions of ‘Alcator scaling’ by a factor of 3–4 at high beam power. An empirical relationship of the form fits our measurements over a wide range of plasma parameters. The function f(Pb), where Pb is the beam power, is linear for Pb ≤ 1.2 MW but tends to saturate for 1.2 MW ≤ Pb ≤ 2.5 MW. Although the equilibria attained in ISX-B are predicted to be above the threshold for the ideal magnetohydrodynamic (MHD) ballooning instability, no evidence of these modes is observed.

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The first result of applying the machine/deep learning technique to the fluid closure problem is presented in this letter. As a start, three different types of neural networks (multilayer perceptron (MLP), convolutional neural network (CNN) and two-layer discrete Fourier transform (DFT) network) were constructed and trained to learn the well-known Hammett-Perkins Landau fluid closure in configuration space. We found that in order to train a well-preformed network, a minimum size of training data set is needed; MLP also requires a minimum number of neurons in the hidden layers equals to the degrees of freedom in Fourier space despite training data is fed in configuration space. Out of three models DFT performs the best for the clean data most likely due to the existence of nice Fourier expression for Hammett-Perkins closure but it is least robust with respect to input noise. Overall, with appropriate tuning and optimization, all three neural networks are able to accurately predict Hammett-Perkins closure and reproduce the inherit nonlocal feature, suggesting a promising path to calculate more sophisticated closures with the machine/deep learning technique.

Global gyrokinetic simulations with self-consistent coupling of neoclassical and turbulent dynamics show that turbulence can significantly affect plasma self-driven mean current generation in tokamaks. The current amplitude, profile and associated phase space structures can all be modified. Turbulence can significantly reduce the current generation in the collisionless regime, generate current profile corrugation near the rational magnetic surface and nonlocally drive current in the linearly stable region—all these are expected to have a radical impact on broad tokamak physics. Both electron parallel acceleration and residual stress from turbulence play crucial roles in turbulence-induced current generation.