Nuclear Techniques ›› 2017, Vol. 40 ›› Issue (3): 30603-030603.doi: 10.11889/j.0253-3219.2017.hjs.40.030603

• NUCLEAR ENERGY SCIENCE AND ENGINEERING • Previous Articles     Next Articles

Applications of Bayesian deconvolution to the charge exchange recombination spectroscopy on EAST tokamak

JIANG Di1,2, LI Yingying2, YIN Xianghui2,3, FU Jia2, ZHANG Ling2, LYU Bo2, XU Guosheng2, GAO Xiang2   

  1. 1. School of Physics and Materials Science, Anhui University, Hefei 230601, China;
    2. Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China;
    3. School of Nuclear Science and Technology, University of Science and Technology of China, Hefei 230026, China
  • Received:2016-11-03 Revised:2016-11-30 Online:2017-03-10 Published:2017-03-11
  • Supported by:

    Supported by Nation Magnetic Confinement Fusion Science Program of China (No.2015GB103001, No.2015GB101002), National Natural Science Foundation of China (No.11405212, No.11535013)

Abstract:

Background: Charge eXchange Recombination Spectroscopy (CXRS) is a routine diagnostic method for the measurement of plasma ion temperature and rotation velocity on nuclear fusion devices. The experimental spectrum can be obviously broadened by the instrument function (IF) convoluted, thus the deconvolution is needed for accurate data analysis. Purpose: This study aims to improve accuracy of data analysis by using the Bayesian deconvolution and impurity spectrum identification. Methods: The deconvoluted method utilizes the Bayesian condition probability formula. Standardized neon lamp is applied to get spectrometric IF for deconvolution processing. Finally, the impurity spectra is identify fast-time-response extreme ultraviolet (EUV) to further improve the analysis accuracy. Results: Experimental results on Experimental Advanced Superconducting Tokamak (EAST) confirmed the reliability of Bayesian deconvolution that was previously verified by simulation study. Conclusion: Bayesian deconvolution combined with fast-time-response EUV can be effectively applied to the edge CXRS analysis on tokamak.

Key words: Bayesian deconvolution, CXRS, IF, Impurity spectra identification

CLC Number: 

  • TL99