WeChat Mini Program
Old Version Features

    • High-k Turbulence Characteristics in Density Modulation Experiments of EAST

    F. Chen,P. Li,Y. D. Li, X. J. Wang,G. J. Wu,J. S. Geng,Y. H. Wang, N. Sun,P. J. Sun,M. Wang,B. J. Ding,C. B. Wu,E. Z. Li,T. F. Zhou,H. L. Zhao,Q. Zang,S. X. Wang,H. Q. Liu,Y. F. Jin,B. Lyu,J. G. Li

    AIP Advances（2024）

    Chinese Acad Sci

    Cited 1|Views6
    Abstract
    High-k turbulence (1 < kθρs < 5) spectrograms have been directly measured in density modulation experiments of the EAST tokamak using the collective Thomson scattering diagnostic system. Density-peaking modulation is achieved in Lower Hybrid Wave (LHW) modulation experiments, revealing that the broadband turbulence is sensitive to the power modulation of LHW and the concomitant variation of density-peaking. Additionally, the contributions of 2.45/4.6 GHz LHW to density peaking differ when Neutral Beam Injection (NBI) is on/off, displaying distinct responses of broadband turbulence in low/high frequency ranges. It is found by cross-coherence analysis that the phase angle is negative in cases without NBI, while in NBI cases it can be modulated from negative to positive, indicating a significant transition of radial turbulence propagation. These findings highlight the active control of density-peaking via auxiliary heating in tokamak plasmas and suggest the potential role of high-k turbulence in density-peaking modulation.
    More
    Translated text
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined