Neural network performance enhancement for limited nuclear fusion experiment observations supported by simulations

Nuclear Fushion

article

Marko Blatzheim¹  Daniel Böckenhoff¹  Hauke Hölbe¹  Thomas Sunn Pedersen¹  Roger Labahn²  The W7-X Team¹ 

[1] Max Planck Institute for Plasma Physics;Institute for Mathematics, University of Rostock

关键词: neural networks;    Wendelstein 7-X;    plasma edge;    magnetic topology;   

DOI  :  10.1088/1741-4326/aaefaf

来源: Institute of Physics Publishing Ltd.

【 摘 要 】

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) (Böckenhoffet al2018Nucl. Fusion58056009), 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.

【 授权许可】

Unknown