Abstract
Disruptions in tokamaks are instabilities events which can damage the machine components. The avoidance and mitigation of these events is desirable in present machines as well as in Next Step devices (such as ITER). A neural network has been developed to predict the occurrence of disruptions caused by edge cooling mechanisms in ASDEX Upgrade. The network works reliably and is able to predict the majority (85%) of the disruptions. The neural network has been trained to predict the time interval up to the disruption and this makes it suitable to be used on-line either to avoid disruptions (by means of auxiliary heating and reduction of gas puffing) or to mitigate the unavoidable ones. For this last purpose, a solid pellet injector has been developed and tested; the injected impurity pellets have been shown to reduce the vertical forces and the conductive fluxes to the divertor.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.