Comparison of a fast low spatial resolution inversion method and peaking factors for the detection of anomalous radiation patterns and disruption prediction

The prediction of a disruptive event is a fundamental task for future fusion reactors. On current tokamaks, most remedial actions have the aim of mitigating their effects, but in future machines avoiding such events will be indispensable. As reported in the literature, especially in metallic machines, electron temperature anomalies play a significant role in the destabilisation of MHD modes, leading to disruptions. Plasma radiation has a strong influence on the shape of the electron temperature profile but it is measured by bolometers integrating along viewing cones; therefore tomographic inversion methods are required to obtain local radiation information. Unfortunately, tomographic algorithms are usually slow and not applicable in real-time, implying that they cannot be used for disruption prediction. In this work, we propose a simple, low spatial resolution but fast inversion method that allows calculating the radiation power in the most important regions of the vessel. The method proposed is compared with traditional indicators based on radiation peaking factors. It is shown that, with this fast tomographic algorithm, it is possible to detect and classify anomalous radiation patterns, such as core radiation and MARFEs, and to predict upcoming electron temperature anomalies with much better accuracy and reliability than using simple peaking factors.