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Chin. Phys. B, 2024, Vol. 33(4): 045201    DOI: 10.1088/1674-1056/ad23d6
PHYSICS OF GASES, PLASMAS, AND ELECTRIC DISCHARGES Prev   Next  

Magnetic diagnostics layout design for CFETR plasma equilibrium reconstruction

Qingze Yu(于庆泽)1,2, Yao Huang(黄耀)1,†, Zhengping Luo(罗正平)1, Yuehang Wang(汪悦航)1, Zijie Liu(刘自结)3, Wangyi Rui(芮望颐)1,2, Kai Wu(吴凯)1, Bingjia Xiao(肖炳甲)1,2, and Jiangang Li(李建刚)1,2
1 Institute of Plasma Physics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei 230031, China;
2 University of Science and Technology of China, Hefei 230026, China;
3 College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Abstract  Plasma equilibrium reconstruction provides essential information for tokamak operation and physical analysis. An extensive and reliable set of magnetic diagnostics is required to obtain accurate plasma equilibrium. This study designs and optimizes the magnetic diagnostics layout for the reconstruction of the equilibrium of the plasma according to the scientific objectives, engineering design parameters, and limitations of the Chinese Fusion Engineering Test Reactor (CFETR). Based on the CFETR discharge simulation, magnetic measurement data are employed to reconstruct consistent plasma equilibrium parameters, and magnetic diagnostics' number and position are optimized by truncated Singular value decomposition, verifying the redundancy reliability of the magnetic diagnostics layout design. This provides a design solution for the layout of the magnetic diagnostics system required to control the plasma equilibrium of CFETR, and the developed design and optimization method can provide effective support to design magnetic diagnostics systems for future magnetic confinement fusion devices.
Keywords:  plasma equilibrium reconstruction      EFIT code      flux loops and magnetic probes      optimization  
Received:  23 November 2023      Revised:  18 January 2024      Accepted manuscript online:  30 January 2024
PACS:  52.55.Fa (Tokamaks, spherical tokamaks)  
  28.52.-s (Fusion reactors)  
  52.70.Ds (Electric and magnetic measurements)  
  52.55.-s (Magnetic confinement and equilibrium)  
Fund: Project supported by the National MCF Energy Research and Development Program of China (Grant Nos. 2022YFE03010002, 2018YFE0302100, and 2018YFE0301105) and the National Natural Science Foundation of China (Grant Nos. 11875291, 11805236, 11905256, and 12075285).
Corresponding Authors:  Yao Huang     E-mail:  yaohuang@ipp.ac.cn

Cite this article: 

Qingze Yu(于庆泽), Yao Huang(黄耀), Zhengping Luo(罗正平), Yuehang Wang(汪悦航), Zijie Liu(刘自结), Wangyi Rui(芮望颐), Kai Wu(吴凯), Bingjia Xiao(肖炳甲), and Jiangang Li(李建刚) Magnetic diagnostics layout design for CFETR plasma equilibrium reconstruction 2024 Chin. Phys. B 33 045201

[1] Song Y T, Wu S T, Li J G, Wan B N, Wan Y X, Fu P, Ye M Y, Zheng J X, Lu K, Gao X G, Liu S M, Liu X F, Lei M Z, Peng X B and Chen Y 2014 IEEE Trans. Plasma Sci. 42 503
[2] Zhuang G, Li G Q, Li J, Wan Y X, Liu Y, Wang X L, Song Y T, Chan V, Yang Q W, Wan B N, Duan X R, Fu P and Xiao B J 2019 Nucl. Fusion 59 112010
[3] Lao L L, John H St, Stambaugh R D, Kellman A G and Pfeiffer W 1985 Nucl. Fusion 25 1611
[4] Lao L L, John H E St, Peng Q, Ferron J R, Strait E J, Taylor T S, Meyer W H, Zhang C and You K I 2005 Fusion Sci. Technol. 48 968
[5] Strait E J, Fredrickson E D, Moret J M and Takechi M 2008 Fusion Sci. Technol. 53 304
[6] Peruzzo S, Albanese R, Artaserse G, Coccorese V, Gerasimov S, Lam N, Maviglia F, Pearson I, Prior P, Quercia A, Zabeo L and JET-EFDA Contributors 2009 Fusion Eng. Des. 84 1495
[7] Kim H S, Bak J G and Hahn S H 2017 Fusion Eng. Des. 123 641
[8] Strait E J 2006 Rev. Sci. Instrum. 77 023502
[9] Shen B, Luo J R, Wan B N and Wang H Z 2003 Plasma Sci. Technol. 5 1785
[10] Testa D, Toussaint M, Chavan R, Guterl J, Lister J B, Moret J M, Perez A, Sanchez F, Schaller B, Tonetti G, Encheva A, Vayakis G, Walker C, Fournier Y, Maeder T, Le-Luyer A, Moreau P, Chitarin G, Alessi E, Delogu R S, Gallo A, Marconato N, Peruzzo S, Preindl M, Carfantan H, Hodgson E, Romero J, Vila R, Brichard B and Vermeeren L 2010 IEEE Trans. Plasma Sci. 38 284
[11] Biel W, Albanese R, Ambrosino R, Ariola M, Berkel M V, Bolshakova I, Brunner K J, Cavazzana R, Cecconello M, Conroy S, Dinklage A, Duran I, Dux R, Eade T, Entler S, Ericsson G, Fable E, Farina D, Figini L, Finotti C, Franke T, Giacomelli L, Giannone L, Gonzalez W, Hjalmarsson A, Hron M, Janky F, Kallenbach A, Kogoj J, König R, Kudlacek O, Luis R, Malaquias A, Marchuk O, Marchiori G, Mattei M, Maviglia F, Masi D G, Mazon D, Meister H, Meyer K, Micheletti D, Nowak S, Piron C, Pironti A, Rispoli N, Rohde V, Sergienko G, Shawish E S, Siccinio M, Silva A, Silva F, Sozzi C, Tardocchi M, Tokar M, Treutterer W and Zohm H 2019 Fusion Eng. Des. 146 465
[12] Pironti A, Albanese R, Ambrosino G and Ariola M 52$nd IEEE Conference on Decision and Control (CDC), December 10——13, 2013, Florence, Italy, pp. 4200——4205
[13] Yu Q Z, Huang Y, Luo Z P, Wang Y H, Liu Z J, Rui W Y, Wu K, Chen D L, Shen B, Xiao B J and Li J G 2023 Plasma Phys. Control. Fusion 65 055013
[14] Formisano A, Martone R and Trevisan F 2001 COMPEL-The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 20 523
[15] Tommasi D G, Neto A C, Pironti A and Sterle C 2015 IEEE Conference on Control and Applications (CCA), September 21——23, 2015, Sydney, Australia, pp. 1296——1302
[16] Chlechowitz E, Talmadge J N, Hanson J D, Anderson F S B and Anderson D T 2015 Nucl. Fusion 55 113012
[17] King J D, Strait E J, Boivin R L, Taussig D, Watkins M G, Hanson J M, Logan N C, Paz-Soldan C, Pace D C, Shiraki D, Lanctot M J, Haye L R J, Lao L L, Battaglia D J, Sontag A C, Haskey S R and Bak J G 2014 Rev. Sci. Instrum. 85 083503
[18] Neilson G H, Basile A, Cohen A, Cometa F, Looz D M A, Fair R, Gattuso A, Jariwala A, Muscatello C, Pablant N, Paraiso G, Shirey S, Smith M, Zolfaghari A and Team U I D 2022 IEEE Trans. Plasma Sci. 50 4144
[19] Liu L, Wang M, Mao S F, Guo Y, Luo Z P, Jian X, Liu X F, Zu C, Chan V and Ye M Y 2017 Fusion Eng. Des. 123 137
[20] Liu C Y, Wu B, Qian J P, Li G, Hou Y, Wei W, Chen M X, Lei M Z and Guo Y 2020 Chin. Phys. B 29 025202
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