Alfvén eigenmode detection using Long-Short Term Memory Networks and CO2 Interferometer data on the DIII-D National Fusion Facility
- Resource Type
- Conference
- Authors
- Garcia, Alvin V.; Jalalvand, Azarakhsh; Steiner, Peter; Rothstein, Andrew; Van Zeeland, Michael; Heidbrink, William W.; Kolemen, Egemen
- Source
- 2023 International Joint Conference on Neural Networks (IJCNN) Neural Networks (IJCNN), 2023 International Joint Conference on. :1-8 Jun, 2023
- Subject
- Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Training
Recurrent neural networks
Databases
Linear regression
Predictive models
Tokamak devices
Real-time systems
Fusion Energy
Machine Learning Classification
Alfven Eigenmodes
CO2 Interferometry
DIII-D Tokamak
- Language
- ISSN
- 2161-4407
The successful steady-state operation of burning fusion plasmas in planned future devices such as the ITER tokamak requires understanding of fast-ion physics. Alfven eigenmodes are special cases of plasma waves driven by fast ions that are important to identify and control since they can lead to loss of confinement and potential damage to the inner walls of a plasma device. The goal of this work is to compare machine learning-based systems trained to classify Alfven eigenmodes using CO 2 interferometer data from a labelled database on the DIII-D tokamak. A Long-Short Term Memory (LSTM) network is trained from scratch using simple spectrogram representations of the CO 2 phase data. The model is trained using a single chord (sequence) per training step. Results show a total true positive rate of = 90% and a false positive rate of = 18%. This paper demonstrates the potential of applying machine learning models to detect and identify different classes of Alfven eigenmodes for real-time applications in steady-state plasma operations that could potentially drive actuators to mitigate Alfven eigenmode impacts.