Monitoring the plasma radiation profile with real-time bolometer tomography at JET

Abstract

The use of real-time tomography at JET opens up new possibilities for monitoring the plasma radiation profile and for taking preventive or mitigating actions against impending disruptions. By monitoring the radiated power in different plasma regions, such as core, edge and divertor, it is possible to set up multiple alarms for the radiative phenomena that usually precede major disruptions. The approach is based on the signals provided by the bolometer diagnostic. Reconstructing the plasma radiation profile from these signals is a computationally intensive task, which is typically performed during post-pulse analysis. To reconstruct the radiation profile in real-time, we use machine learning to train a surrogate model that performs matrix multiplication over the bolometer signals. The model is trained on a large number of sample reconstructions, and is able to compute the plasma radiation profile within a few milliseconds in real-time. The implementation has been further optimized by computing the radiated power only in the regions of interest. Experimental results show that, during uncontrolled termination, there is an impurity accumulation at the plasma core, which eventually leads to a disruption. A threshold-based alarm on core radiation, among other options, is able to anticipate a significant fraction of such disruptions.