MHD driven current redistribution events in reverse magnetic shear plasmas

An early phase of reverse magnetic shear (RMS) is often followed by some MHD events leading to current redistribution and monotonic q-profiles (safety factor) in NSTX discharges. We have developed a machine learning algorithm (MSE-ML) that clustered a large database of NSTX shots by distinguishing the q-profiles into the groups of strong and weak RMS. The algorithm is capable of detecting changes in the q-profile and identifying the transitions between RMS regimes. We observed that abrupt MHD activities often are associated with termination of strong RMS during the current ramp-up and early in the flat-top phases of the discharge. Since the development of MHD-stable plasma is crucial for the entire NSTX-U program, we are working to include the discharge characteristics into the MSE-ML framework to recognize the conditions that will lead to a long-lived reversed magnetic shear (RMS) discharge. We aim to achieve an understanding of MHD events to sustain the reverse magnetic shear for future experiments.