1. Deep Learning for the Analysis of Disruption Precursors Based on Plasma Tomography
- Author
-
Pedro J. Carvalho, Carlo Sozzi, Peter J. Lomas, Jet Contributors, and Diogo R. Ferreira
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Nuclear and High Energy Physics ,Materials science ,020209 energy ,FOS: Physical sciences ,02 engineering and technology ,01 natural sciences ,Machine Learning (cs.LG) ,010305 fluids & plasmas ,0103 physical sciences ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,Civil and Structural Engineering ,Fusion ,Jet (fluid) ,Pulse (signal processing) ,business.industry ,Mechanical Engineering ,Deep learning ,Plasma ,Fusion power ,Physics - Plasma Physics ,anomaly detection ,Power (physics) ,Plasma Physics (physics.plasm-ph) ,machine learning ,Nuclear Energy and Engineering ,13. Climate action ,Tomography ,Artificial intelligence ,Plasma tomography ,business ,Biomedical engineering - Abstract
The JET baseline scenario is being developed to achieve high fusion performance and sustained fusion power. However, with higher plasma current and higher input power, an increase in pulse disruptivity is being observed. Although there is a wide range of possible disruption causes, the present disruptions seem to be closely related to radiative phenomena such as impurity accumulation, core radiation, and radiative collapse. In this work, we focus on bolometer tomography to reconstruct the plasma radiation profile and, on top of it, we apply anomaly detection to identify the radiation patterns that precede major disruptions. The approach makes extensive use of machine learning. First, we train a surrogate model for plasma tomography based on matrix multiplication, which provides a fast method to compute the plasma radiation profiles across the full extent of any given pulse. Then, we train a variational autoencoder to reproduce the radiation profiles by encoding them into a latent distribution and subsequently decoding them. As an anomaly detector, the variational autoencoder struggles to reproduce unusual behaviors, which includes not only the actual disruptions but their precursors as well. These precursors are identified based on an analysis of the anomaly score across all baseline pulses in two recent campaigns at JET., (to appear)
- Published
- 2020
- Full Text
- View/download PDF