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Preprint
Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak
arXiv.org
30 Nov 2023
Abstract
Active feedback control in magnetic confinement fusion devices is desirable
to mitigate plasma instabilities and enable robust operation. Optical
high-speed cameras provide a powerful, non-invasive diagnostic and can be
suitable for these applications. In this study, we process fast camera data, at
rates exceeding 100kfps, on $\textit{in situ}$ Field Programmable Gate Array
(FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate
control signals in real-time. Our system utilizes a convolutional neural
network (CNN) model which predicts the $n$=1 MHD mode amplitude and phase using
camera images with better accuracy than other tested non-deep-learning-based
methods. By implementing this model directly within the standard FPGA readout
hardware of the high-speed camera diagnostic, our mode tracking system achieves
a total trigger-to-output latency of 17.6$\mu$s and a throughput of up to
120kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment
demonstrates an FPGA-based high-speed camera data acquisition and processing
system, enabling application in real-time machine-learning-based tokamak
diagnostic and control as well as potential applications in other scientific
domains.
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Details
- Title
- Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak
- Creators
- Yumou WeiRyan F Forelli - Lehigh UniversityChris HansenJeffrey P LevesqueNhan Tran - Fermi National Accelerator LaboratoryJoshua C Agar - Drexel UniversityGiuseppe Di Guglielmo - Fermi National Accelerator LaboratoryMichael E MauelGerald A Navratil
- Publication Details
- arXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Mechanical Engineering and Mechanics
- Other Identifier
- 991021878114004721