It has recently been shown that artificial neural networks (NNs) are able to establish nontrivial connections between the heat fluxes and the magnetic topology at the edge of Wendelstein 7-X (W7-X) (Böckenhoff et al 2018 Nucl. Fusion58 056009), a first step in the direction of real-time control of heat fluxes in this device. We report here on progress on improving the performance of these NNs. A particular challenge here is that of generating a suitable training set for the NN. At present, experimental data are sparse, and simulated data, which are much more abundant, do not match the experimental data closely. It is found that the NNs show significantly improved performance on experimental data when experimental and simulated data are combined into a common training set, relative to training performed on only one of the two data sets. It is also found that appropriate pre-processing of the data improves performance. The architecture of the NN is also discussed. Overall a significant improvement in NN performance was seen—the normalized error reduced by more than a factor of three over the previous results. These results are important since heat flux control in a W7-X, as well as in a future fusion power plant, is likely a key issue, and must start with a very limited set of experimental training data, complemented by a larger, but not necessarily fully realistic, set of simulated data.
Learning control coil currents from heat-flux images using convolutional neural networks at Wendelstein 7-X