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A disruption predictor based on a 1.5-dimensional convolutional neural network in HL-2A.
- Source :
-
Nuclear Fusion . Jan2020, Vol. 60 Issue 1, p1-1. 1p. - Publication Year :
- 2020
-
Abstract
- Disruption means a sudden loss of confinement during a discharge in fusion reactors. Due to the huge electromagnetic loading and thermal loading on the facility and a large number of runaway electrons generated during disruptions, it is essential to find a method to predict the disruptions, so that measures like massive gas injection can be taken to mitigate or to avoid these harmful effects. In this research, a machine learning model mainly based on a 1.5-dimensional convolutional neural network, which is good at dealing with signals from multi-channels with great divergence, is trained to predict disruptions in the HL-2A tokamak. The disruption predictor uses shots 20000–29999 in HL-2A to train the machine learning model, and uses shots 30000–31999 to optimize hyper parameters. When tested on shots 32000–36000 in HL-2A, it reaches a true positive rate of 92.2% and a true negative rate of 97.5% with 30 ms before the disruption. [ABSTRACT FROM AUTHOR]
- Subjects :
- *ARTIFICIAL neural networks
*FUSION reactors
*GAS injection
*MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 00295515
- Volume :
- 60
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Nuclear Fusion
- Publication Type :
- Academic Journal
- Accession number :
- 140282346
- Full Text :
- https://doi.org/10.1088/1741-4326/ab4b6f