Development of SVD Algorithm for Turbulence Tomography

发布时间：2023-04-05 13:09

　　

【文章页数】：54 页

【学位级别】：硕士

【文章目录】：
Abstract
Chapter 1: Introduction and Review
    1.1 Plasma Science and Energy Problem
    1.2 Progress toward the Realization of Nuclear Fusion Reactor
    1.3 Review of Drift Wave and Zonal Flow
    1.4 Motivation
CHAPTER 2: Review of Tomography Algorithms
    2.1 Overview
    2.2 Direct Least Square Fitting
    2.3 Least square method and Regularization
    2.4. Regularization by means of Penalty Function Method
    2.5 Singular Value Decomposition (SVD)
    2.6 Phillips-Tikhonov Regularization and Generalized Cross Validation(GCV) Criterion
    2.7. Maximization Likelihood-Expectation Maximum (ML-EM)
CHAPTER 3: Test of Algorithms using Assumed Images
    3.1
        3.1.1 Assumed Emission Image and Tomography Configuration
        3.1.2 Development of SVD and Comparison with ML-EM
    3.2
        3.2.1 Assumed Emission Image Part2
        3.2.2 Development of SVD and Comparison with ML-EM Part2
    3.3
        3.3.1 Assumed Emission Image Part3
        3.3.2 Development of SVD and Comparison with ML-EM Part3
    3.4. Consideration of Detector Alignment
    3.5 Brief Summary of Tomography Algorithms and Detector Configuration
CHAPTER 4: Application of SVD Algorithm on Experimantal Results
    4.1 Experimental Devices
        4.1.1 Plasma Assembly for Non-linear Turbulence Analysis (PANTA)
        4.1.2. Vacuum system
        4.1.3 Plasma source
        4.1.4 Multi-Channel Spectroscopic Measurement System for Tomography
    4.2 Application of Developed Algorithms on Experiments
        4.2.1 Background
        4.2.2 Reconstructed Emission Profiles
CHAPTER 5: Summary
REFERENCE
ACKNOWLEDGEMENT
ACADEMIC ACHIEVEMENT



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