Machine learning techniques have been applied successfully in EAST plasma equilibrium reconstruction and disruption prediction. Regression neural network models are trained to identify the plasma center position and calculate equilibrium plasma parameters including li,β_p,κ,q_0 and a_minor with magnetic diagnostic signals as input features [1][2]. The results on test dataset show good...
Present contribution aims at comparing the different kinds of disruptions that occurred in the last JET with ITER-like wall (ILW) campaigns with Tritium and Deuterium-Tritium fuels.
Last campaigns performed in JET-ILW with a D fuel showed that the majority (around 80%) of disruptions follow two main paths [1]. The first path (temperature hollowing, TH) is strictly related with the influx of...
Control is necessary to keep fusion plasmas stable. This requests a set of real-time diagnostics. These sensors and/or data acquisition systems are prone to failure, especially under the demanding environments of a fusion reactor that has cryogenic and extreme hot conditions, high neutron production and high magnetic fields. Current real-time control algorithm assume the sensors as correct...
Disruption prediction requires an understanding of the routes that a plasma may take from being in a healthy state to a disruption, such as the analysis carried out on JET [1]. Of particular concern are those routes that give very little warning of an imminent disruption because they give little potential to either take avoiding or mitigating action. We will use the MAST high speed visible...
The capability to terminate plasma pulses safely is an important goal towards the optimization of operational scenarios in tokamaks, so it is of great importance to study the physical phenomena involved in plasma disruptions and to develop precursors for avoidance and/or mitigation actions. The development of tearing modes (TMs) inside the plasma is a major cause of disruptions. It has been...
Disruption prediction and avoidance is critical for ITER and reactor-scale tokamaks to maintain steady plasma operation and to avoid damage to device components. Physics-based disruption event characterization and forecasting (DECAF) research determines the relation of events leading to disruption, and forecasts event onset. The analysis has access to data from multiple tokamaks to best...
Disruption is a major obstacle for tokamaks to be commercially viable reactors. Accurately predicting an incoming disruption and deploying disruption mitigation system is one of the keys to solve this problem. Today’s machine learning based disruption predictors do have great performance if given good enough data to train. But future tokamak will not provide good enough data before damaging...
Achieving acceptably low disruptivity on ITER and future reactors will require active monitoring and control of the proximity of operating points to unsafe regions. Although mitigation strategies can protect the infrastructure from disruptions and engineering limits, maximizing scientific or economic output of a device demands avoiding triggering mitigation systems while optimizing...
ITER will require exceptionally low disruptivity while pushing the limits of plasma performance. Ensuring robust stability will require a comprehensive strategy, and must include the continuous regulation of the proximity to stability and controllability limits, also called “Proximity Control.” DIII-D has been developing a Proximity Control architecture [1] which modifies control targets and...
Although the stabilization of locked islands using RF-driven currents has been demonstrated experimentally in a pioneering series of experiments [1], experimental and theoretical research on RF island stabilization has continued to focus almost exclusively on stabilization during the rotating phase, before locking occurs. An emphasis on avoiding island locking has emerged from a concern about...
