Conveners
Prediction & Avoidance
- Gabriella Pautasso
Prediction & Avoidance
- Carlo Sozzi (Istituto per la Scienza e Tecnologia dei Plasmi ISTP-CNR Milano Italy)
Prediction & Avoidance
- Carlo Sozzi (Istituto per la Scienza e Tecnologia dei Plasmi ISTP-CNR Milano Italy)
Prediction & Avoidance
- Gabriella Pautasso
The roadmap for the commissioning and first operations of superconductive tokamaks envisages the possibility of running discharges with fairly elongated plasmas before the complete installation of the in-vessel components, including vertical stabilization coils, or any other specific sets of coils to be used for the magnetic control of fast transients.
In the absence of dedicated...
Steady, non-disruptive tokamak plasmas have been produced in the Madison Symmetric Torus (MST) with an electron density up to an order of magnitude above the Greenwald limit [Hurst et al., PRL, accepted for publication]. This result is made possible in part by a high-voltage, feedback-controlled power supply driving the toroidal plasma current. Also important may be the thick, stabilizing,...
A novel database study of the L-mode Density Limit (LDL) in metal- and carbon-wall devices (Alcator C-Mod, AUG, DIII-D, and TCV) identifies a two-variable, dimensionless stability boundary that predicts the LDL with significantly higher accuracy than the widely-utilized Greenwald limit. Historically, there has been broad interest in understanding the operational boundary imposed by the...
Resistive wall tearing modes (RWTM) are closely related to resistive wall modes (RWMs). RWTMs are tearing modes whose linear and nonlinear growth rate depend on the resistive wall penetration time.
The consequence for ITER, with wall penetration time of $250 ms,$ compared to $ \sim 5 ms$ in JET and DIII-D, is that the thermal quench
timescale could be much longer than previously...
The maximum allowable vertical displacement which can be recovered by the magnetic control system is a fundamental quantity for tokamak magnetic control (Gribov 2015 Nucl. Fusion 55 073021). This figure of merit is usually defined relying on a mass-less assumption, i.e. the reaction currents in wall structures are considered to vary in order to guarantee MHD equilibrium during the plasma...
Currently machine learning disruption predictor is the most promising way of solving the disruption mitigation triggering problem. But it does need data from the target machine to be trained. However, the future machine may not be able to provide enough data both in quality and quantity to satisfy the training. In this paper we first explained why just simply mixing limited data from target...
A deep learning-based disruption prediction algorithm has been implemented on a new tokamak, HL-3, for the first time. An Area Under receiver-operator characteristic Curve (AUC) of 0.940 has been realized, despite the limited training data obtained during the first two campaigns. Besides the well-known issue of lacking training data, a new issue is addressed that the data environment of new...
Disruptions are one of the most critical issues in tokamak operation. In fact, the rapid termination of plasma magnetic confinement leads to significant heat and electromagnetic loads on the plasma-facing components, threatening the integrity of the reactor. Moreover, continuous early terminations of plasma discharge can cause variations in the energy production, limiting the amount of energy...
Artificial Intelligence (AI) techniques, such as Machine learning and Deep Learning, have been extensively investigated for the construction of disruptions predictive models in tokamaks. Although the excellent performance has demonstrated the applicability of the paradigm to the experimental machines currently in service, the development of cross-tokamak models is still in its infancy [1]....
The application of machine learning methods has aided to improve the accuracy of disruption predictors in the last 15 years. However, these models are normally just a trigger and they do not provide a crucial piece information: the remaining time to the disruption. This is detrimental for their practical utility in order to develop efficient control actions.
This study tackles this...
Rapid plasma dynamics preceding some disruptions in tokamak devices can be inferred through the electron temperature profile evolution due to the fast thermal transport along the field lines. In particular, local collapses in the electron temperature profile are the signature of nonlinear events such as flux surface tearing, observed due to the sudden thermal transport that follows changes in...
This work explores the development and preliminary calibration of an off-normal warning system for SPARC, the aim of which is to minimize disruption loads and maximize operation time via the detection, interpretation, and pacification (i.e. avoidance and mitigation) of anomalous events. Similar systems have been implemented for existing tokamaks like DIII-D [1], NSTX [2], and TCV [3], but the...
Author: A. Kumar,
Co-authors: C. Clauser, T. Golfinopoulos, D.T. Garnier, J. Wai, D. Boyer, A. Saperstein, R. Granetz, C. Rea
Given the demanding requirements of the SPARC high-field tokamak (B_{0}=12.2 T) and its operation with high elongated plasma (\kappa_{sep}=1.97), robust real-time-compatible vertical stability observers are paramount. In this work, we present the fast...
