Optical plasma boundary reconstruction based on least squares for EASTTokamak

Hao LUO, Zheng-ping LUO, Chao XU, Wei JIANG

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Front. Inform. Technol. Electron. Eng ›› 2018, Vol. 19 ›› Issue (9) : 1124-1134. DOI: 10.1631/FITEE.1700041
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Optical plasma boundary reconstruction based on least squares for EASTTokamak

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Abstract

Reconstructing the shape and position of plasma is an important issue in Tokamaks. Equilibrium and fitting (EFIT) code is generally used for plasma boundary reconstruction in some Tokamaks. However, this magnetic method still has some inevitable disadvantages. In this paper, we present an optical plasma boundary reconstruction algorithm. This method uses EFIT reconstruction results as the standard to create the optimally optical reconstruction. Traditional edge detection methods cannot extract a clear plasma boundary for reconstruction. Based on global contrast, we propose an edge detection algorithm to extract the plasma boundary in the image plane. Illumination in this method is robust. The extracted boundary and the boundary reconstructed by EFIT are fitted by same-order polynomials and the transformation matrix exists. To acquire this matrix without camera calibration, the extracted plasma boundary is transformed from the image plane to the Tokamak poloidal plane by a mathematical model, which is optimally resolved by using least squares to minimize the error between the optically reconstructed result and the EFIT result. Once the transform matrix is acquired, we can optically reconstruct the plasma boundary with only an arbitrary image captured. The error between the method and EFIT is presented and the experimental results of different polynomial orders are discussed.

Keywords

Optical boundary reconstruction / Boundary detection / Global contrast / Least square / EAST Tokamak

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Hao LUO, Zheng-ping LUO, Chao XU, Wei JIANG. Optical plasma boundary reconstruction based on least squares for EASTTokamak. Front. Inform. Technol. Electron. Eng, 2018, 19(9): 1124‒1134 https://doi.org/10.1631/FITEE.1700041

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2018 Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature
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