Exploring data-driven models for spatiotemporally local classification of Alfvén eigenmodes

Kaptanoglu, Alan A.; Jalalvand, Azarakhsh; Garcia, Alvin; Austin, M. E.; Verdoolaege, Geert; Schneider, Jeff; Hansen, Chris; Brunton, Steven L.; Heidbrink, W. W.; Kolemen, Egemen

doi:10.1088/1741-4326/ac8a03

Cited by 6 publications

(2 citation statements)

References 63 publications

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“…Additionally, some mode identification problems have been solved with AI methods. For example, the DIII-D team used a neural network model to analyze 40 ECE signal arrays and identify different types of Alfvén eigenmodes (AEs) [20].…”

Section: Introductionmentioning

confidence: 99%

Identification of MHD modes on EAST using a deep learning framework

Kong,

Guo,

Shen

et al. 2023

Plasma Phys. Control. Fusion

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The improvement of plasma parameters is severely limited by magnetohydrodynamic (MHD) instabilities. The identification of MHD modes is crucial for the study and control of MHD instabilities. In this study, an MHD mode identifier is developed based on a temporal convolutional network and long short-term memory (LSTM) network. The identifier is trained and tested on a small dataset containing 33 shots. Firstly, the temporal convolutional network encodes 27 diagnostic signals and then decodes them using LSTM network with different parameters to obtain the MHD modes and their frequency and intensity. The identifier exhibits an accuracy of approximately 98.38% on the test set and can accurately calculate the frequency and intensity of the MHD modes. To further examine the performance of the identifier, seven shots outside the dataset are used for shot-by-shot testing. The identifier can accurately identify the time period of tearing modes, and the identification accuracies of 2/1 and 3/2 tearing modes are 92.7% and 100%, respectively. The identification accuracy of the fishbone mode is slightly worse, only 82.1%. This is because the fishbone mode occurs intermittently. The frequent switching between the fishbone mode and no MHD behavior affects the identification of the fishbone mode. Overall, through the training of the small datasets, the identifier exhibits a good identification performance for the MHD modes. The proposed data-driven identifier can serve as a reference for establishing a large MHD mode database of EAST as well as a real-time MHD identification and control algorithm.

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Section: Introductionmentioning

confidence: 99%

Identification of MHD modes on EAST using a deep learning framework

Kong,

Guo,

Shen

et al. 2023

Plasma Phys. Control. Fusion

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“…Originally, in-shot variation of neutral beam energy showed promise for AE control [34], then the first active real-time control of AEs in a tokamak utilized modulated beams to tune the drive for AEs using feedback from high resolution ECE signals [15]. Shortly after, the Large 2009-2017 DIII-D AE Energetic Particle Database [35] was created to better understand low frequency AEs and was later used for ML analysis in two papers [36,37]. Deep Neural Networks were trained using ECE data in both studies.…”

Section: Introductionmentioning

confidence: 99%

Comparison of machine learning systems trained to detect Alfvén eigenmodes using the CO₂ interferometer on DIII-D

Garcia,

Jalalvand,

Steiner

et al. 2023

Nucl. Fusion

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A Machine-learning (ML) based detection scheme that automatically detects Alfvén eigenmodes (AE) in a labelled DIII-D database is presented here. Controlling AEs is important for the success of planned burning plasma devices such as ITER, since resonant fast ions can drive AEs unstable and degrade the performance of the plasma or damage the first walls of the machine vessel. Artificial Intelligence (AI) could be useful for real-time detection and control of AEs in steady-state plasma scenarios by implementing ML-based models into control algorithms that drive actuators for mitigation of AE impacts. Thus, the objective is to compare differences in performance between using two different recurrent neural network systems (Reservoir Computing Network and Long Short Term Memory Network) and two different representations of the CO2 phase data (simple and crosspower spectrograms). All CO2 interferometer chords are used to train both models, but only one is processed during each training step. The results from the model and data comparison show higher performance for the RCN model (True Positive Rate = 90% and False Positive Rate = 14%), and that using simple magnitude spectrograms is sufficient to detect AEs. Also, the vertical CO2 interferometer chord passing near the center is better for Machine Learning-based detection of AEs.

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Segmentation of MHD modes using Fourier transform, wavelets and computer vision algorithms

Zapata-Cornejo,

Zarzoso,

Pinches

et al. 2024

Plasma Phys. Control. Fusion

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MHD activity in fusion devices is typically analyzed by examining time-frequency spectrograms obtained from various diagnostics. MHD modes often co-exist with various types of noise and complex patterns generated by other events like pellet injection or active diagnostics. Traditionally, identifying MHD modes has been a manual task, making it labor-intensive. To overcome this issue, this study proposes the use of computer vision algorithms for noise removal and automatic feature extraction. First, the automatic detection of straight-line patterns is achieved by applying the Hough transform. Then, the discrete wavelet transform is proposed to break down spectrograms into sub-images of different scales, removing broadband noise and pellet injection signatures. The multiscale decomposition is subsequently extended to multiple directions using either 2D Fourier transforms or curvelets, achieving a high signal-to-noise ratio in spectrograms and eliminating undesired frequency sweeps of TAE antenna. Once MHD activity is successfully enhanced, a pipeline of algorithms for ridge detection, thresholding and labeling perform a segmentation of the image, automatically labeling individual modes. This study demonstrates the effectiveness of computer vision algorithms for the identification of MHD modes. The use of such algorithms may potentially help in the analysis process and the creation of large databases of modes.

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Exploring data-driven models for spatiotemporally local classification of Alfvén eigenmodes

Cited by 6 publications

References 63 publications

Identification of MHD modes on EAST using a deep learning framework

Identification of MHD modes on EAST using a deep learning framework

Comparison of machine learning systems trained to detect Alfvén eigenmodes using the CO₂ interferometer on DIII-D

Segmentation of MHD modes using Fourier transform, wavelets and computer vision algorithms

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Exploring data-driven models for spatiotemporally local classification of Alfvén eigenmodes

Cited by 6 publications

References 63 publications

Identification of MHD modes on EAST using a deep learning framework

Identification of MHD modes on EAST using a deep learning framework

Comparison of machine learning systems trained to detect Alfvén eigenmodes using the CO2 interferometer on DIII-D

Segmentation of MHD modes using Fourier transform, wavelets and computer vision algorithms

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Comparison of machine learning systems trained to detect Alfvén eigenmodes using the CO₂ interferometer on DIII-D