Development of intelligent control module for the J-TEXT electron cyclotron emission imaging system

Yang, Zhoujun; Zhou, Jianliang; Pan, Xiaolong; Cao, Jianyu; Yu, Guangyang; Domier, C.W.; Zhu, Yilun; Chang, F.Q.; Zhang, Z.C.; Gao, Yuan; Luhmann, N. C.; Xie, Xuemei

doi:10.1016/j.fusengdes.2020.111494

Cited by 6 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A new 256-channel electron cyclotron emission imaging (ECEI) system was installed on J-TEXT at the end of 2019 [12]. It is an innovative design which first adopts many digital control components to realize intelligent remote control [13,14]. The J-TEXT ECEI system consists of two receiving antenna arrays for different depth measurements at the same toroidal position, while operating in W-band (75-110 GHz) and F-band (90-140 GHz).…”

Section: Upgrade Of Diagnosticsmentioning

confidence: 99%

Advances in physics and applications of 3D magnetic perturbations on the J-TEXT tokamak

Wang¹,

Liang²,

Ding³

et al. 2022

Nucl. Fusion

Self Cite

View full text Add to dashboard Cite

In the recent two years, three major achievements have been made on J-TEXT in supporting for the expanded operation regions and diagnostic capabilities, e.g. the 105 GHz/500 kW/1 s ECRH system and the poloidal divertor configuration. Especially, the 400 kW ECW has also been successfully injected into the diverted plasma. The locked mode (LM), especially the 2/1 LM, is one of the biggest threats to the plasma operation. Both the thresholds of 2/1 and 3/1 LM are observed to vary non-monotonically on electron density. The electrode biasing (EB) was applied successfully to unlock the LM from either a rotating or static RMP field. In the presence of 2/1 LM, three kinds of standing wave (SW) structures have been observed to share a similar connection to the island structure, i.e. the nodes of the SWs locate around the O- or X- points of the 2/1 island. The control and mitigation of disruption is essential to the safe operation of ITER, and it has been systematically studied by applying RMP field, MGI and SPI on J-TEXT. When the RMP induced 2/1 LM is larger than a critical width, the MGI shutdown process can be significantly influenced. If the phase difference between the O-point of LM and the MGI valve is +90° (or -90°), the penetration depth and the assimilation of impurities can be enhanced (or suppressed) during the pre-TQ phase and result in a faster (or slower) thermal quench. A secondary MGI can also suppress the RE generation, if the additional high-Z impurity gas arrives at the plasma edge before TQ. When the secondary MGI has been applied after the formation of RE current plateau, the RE current can be dissipated, and the dissipation rate increases with the injected impurity quantity, and saturates with a maximum of 28 MA/s.

show abstract

Section: Upgrade Of Diagnosticsmentioning

confidence: 99%

Advances in physics and applications of 3D magnetic perturbations on the J-TEXT tokamak

Wang¹,

Liang²,

Ding³

et al. 2022

Nucl. Fusion

Self Cite

View full text Add to dashboard Cite

show abstract

“…Simplified emission model that does not include detection physics can lead to incorrect interpretation of the measurements [210]. A third function of a synthetic diagnostic is for experimental control and plasma parameter optimization [211].…”

Section: Synthetic Instruments and Datamentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

View full text Add to dashboard Cite

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasmaprocessing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

show abstract