Measuring the electron temperature and identifying plasma detachment using machine learning and spectroscopy.

A machine learning approach has been implemented to measure the electron temperature directly from the emission spectra of a tokamak plasma. This approach utilized a neural network (NN) trained on a dataset of 1865 time slices from operation of the DIII-D tokamak using extreme ultraviolet/vacuum ultraviolet emission spectroscopy matched with high-accuracy divertor Thomson scattering measurements of the electron temperature, T. This NN is shown to be particularly good at predicting T at low temperatures (T < 10 eV) where the NN demonstrated a mean average error of less than 1 eV.

First Evidence of Local E×B Drift in the Divertor Influencing the Structure and Stability of Confined Plasma near the Edge of Fusion Devices.

The structure of the edge plasma in a magnetic confinement system has a strong impact on the overall plasma performance. We uncover for the first time a magnetic-field-direction dependent density shelf, i.e.

E×B Flux Driven Detachment Bifurcation in the DIII-D Tokamak.

A bifurcative step transition from low-density, high-temperature, attached divertor conditions to high-density, low-temperature, detached divertor conditions is experimentally observed in DIII-D tokamak plasmas as density is increased. The step transition is only observed in the high confinement mode and only when the B×∇B drift is directed towards the divertor. This work reports for the first time a theoretical explanation and numerical simulations that qualitatively reproduce this bifurcation and its dependence on the toroidal field direction.