Analysis of pedestal gradient characteristic on the Experimental Advanced Superconducting Tokamak

Wang, Teng Fei; Han, Xuan; Zang, Qing; Xiao, Siyou; Tian, Biao; Hu, Aijun; Zhao, Jun

doi:10.1063/1.4948484

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…It is usually found in experiment that pedestal stored energy is proportional to total energy [39], which is attributed to core profile stiffness. The experimental results have shown that core density and temperature profiles are actually stiff on EAST [40]. Therefore, it is reasonable to assume ΔW ped,inter-ELM ∝ ΔW MHD,inter-ELM , where ΔW MHD,inter-ELM represents the increment of W MHD between two ELMs.…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

Effect of pedestal fluctuation on ELM frequency in the EAST tokamak

et al. 2018

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The dependence of ELM frequency on heating power has been studied on the Experimental Advanced Superconducting Tokamak (EAST). It is found that the ELM frequency (f ELM ) generally increases with the power through separatrix (P sep ), indicating type-I ELM in these plasmas. However, there are two data points, named 'abnormal ELM' in the present paper, which have much lower f ELM than the 'normal ELM', while both types of ELM have similar ELM energy losses. The 'abnormal ELM' occurs at a phase with increased radiation power due to metal impurity influx events. The increased radiation power cannot explain the much lower f ELM for 'abnormal ELM', since the reduction of P sep is weaker than proportional to the observed reduction of the ELM frequency. The 'abnormal ELM' feature can be attributed to the enhanced amplitude of a coherent mode in the pedestal region. Comparing the pedestal evolutions for the two types of ELM with similar separatrix power P sep , it is actually found that the more pronounced pedestal coherent mode in the plasma with 'abnormal ELM' leads to a slower pressure pedestal recovery during the inter-ELM phase. This experimental result implies that the physical mechanism for 'abnormal ELM' is that the more pronounced pedestal fluctuation induces larger outward transport, slows down the pedestal evolution and leads to longer inter-ELM phase, i.e. lower ELM frequency.

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Section: Discussion Of the Resultsmentioning

confidence: 99%

Effect of pedestal fluctuation on ELM frequency in the EAST tokamak

et al. 2018

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“…i On the other hand, large n e usually corresponds higher density pedestal [17], which can provide more bootstrap current, leading to lower l .…”

Section: Lhcdmentioning

confidence: 99%

Dependence of the internal inductance on the radial distance between the primary and secondary X-point surfaces in the EAST tokamak

Wang

Yang

Sheng

2018

Plasma Sci. Technol.

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Dynamic Nonlinear Expression Recognition Technology Using Neural Network and Attention Mechanism

Zhang

Binsawad

et al. 2022

Fractals

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The purpose is to apply deep learning to facial expression recognition and improve the efficiency of facial expression recognition in real scenes: first, convolutional neural network (CNN) theoretical model is proposed. On this basis, a new CNN model is proposed, which has six learning layers, including three convolutional layers and three fully connected layers. Then, based on the shortcomings of the model, weight decay and dropout optimization methods are proposed. Moreover, the attention module is designed based on the theory of attention mechanism. After each convolutional layer, a hybrid attention module (channel domain attention and spatial domain attention) is added. Finally, the frame error rate (FER) 2013 dataset and Cohn–Kanade (CK)+ dataset are used to test and compare the performance of the model. The results show that after 50Epoch, the accuracy rate of the original model fluctuates greatly in the process of convergence; however, after attention mechanism is added, the fluctuation of the accuracy of the model is obviously reduced. In the CK+ dataset, the accuracy of each model is maintained at about 95%, while the accuracy of FER2013 dataset is about 71%. After attention mechanism is added, the recognition rate of dynamic nonlinear expression is higher than that of basic model. The recognition rate of the original model and the optimized model with attention mechanism for the dynamic nonlinear expression with occlusion decreases in varying degrees, but the reduction of the recognition rate of the latter is alleviated.

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Analysis of pedestal gradient characteristic on the Experimental Advanced Superconducting Tokamak

Cited by 3 publications

References 24 publications

Effect of pedestal fluctuation on ELM frequency in the EAST tokamak

Effect of pedestal fluctuation on ELM frequency in the EAST tokamak

Dependence of the internal inductance on the radial distance between the primary and secondary X-point surfaces in the EAST tokamak

Dynamic Nonlinear Expression Recognition Technology Using Neural Network and Attention Mechanism

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