面向托卡马克破裂预测的实时数据采集系统设计

张沛龙; 叶伟杰; 郑玮; 丁永华; 王俪晔; 杨玉林; 张沛龙; 叶伟杰; 郑玮; 丁永华; 王俪晔; 杨玉林

doi:10.16516/j.ceec.2024.3.11

[1]

王龙. 磁约束等离子体实验物理 [M]. 北京: 科学出版社, 2018.

WANG L. Experimental physics of magnetic confinement plasmas [M]. Beijing: Science Press, 2018.

[2]

YOKOYAMA T, MIYOSHI Y, HIWATARI R, et al. Prediction of high-beta disruptions in JT-60U based on sparse modeling using exhaustive search [J]. Fusion engineering and design, 2019, 140: 67-80. DOI: 10.1016/j.fusengdes.2019.01.128.

[3]

YUAN J S, ZUO G Z, ZHAO S B, et al. Design of a shattered pellet injector and preliminary bench tests of Ne pellet formation for EAST disruption mitigation [J]. Fusion engineering and design, 2023, 191: 113567. DOI: 10.1016/j.fusengdes.2023.113567.

[4]

LANDMAN I S, PESTCHANYI S E, IGITKHANOV Y, et al. Two-dimensional modeling of disruption mitigation by gas injection [J]. Fusion engineering and design, 2011, 86(9/11): 1616-1619. DOI: 10.1016/j.fusengdes.2010.12.017.

[5]

李峰. 用于托卡马克等离子体破裂缓解的电磁弹丸注入系统研制 [D]. 武汉: 华中科技大学, 2023.

LI F. Development of electromagnetic pellet-injection system for tokamak plasma disruption mitigation [D]. Wuhan: Huazhong University of Science and Technology, 2023.

[6]

王雪玲. J-TEXT等离子体破裂预测及逃逸电子束控制的研究 [D]. 武汉: 华中科技大学, 2018.

WANG X L. Research of the plasma disruption prediction and runaway electron beams control on J-TEXT [D]. Wuhan: Huazhong University of Science and Technology, 2018.

[7]

QIU H B, HU Z Z, WU S F, et al. Initial analytical theory of plasma disruption and experimental evidence [J]. Scientific reports, 2023, 13(1): 9551. DOI: 10.1038/s41598-023-36504-7.

[8]

吴其其. 基于深度学习异常检测的等离子体破裂预测 [D]. 武汉: 华中科技大学, 2021. DOI: 10.27157/d.cnki.ghzku.2021.001055.

WU Q Q. Plasma disruption prediction based on deep learning and anomaly detection [D]. Wuhan: Huazhong University of Science and Technology, 2021. DOI: 10.27157/d.cnki.ghzku.2021.001055.

[9]

RATTÁ G A, VEGA J, MURARI A, et al. An advanced disruption predictor for JET tested in a simulated real-time environment [J]. Nuclear fusion, 2010, 50(2): 025005. DOI: 10.1088/0029-5515/50/2/025005.

[10]

ALEDDA R, CANNAS B, FANNI A, et al. Multivariate statistical models for disruption prediction at ASDEX upgrade [J]. Fusion engineering and design, 2013, 88(6/8): 1297-1301. DOI: 10.1016/j.fusengdes.2013.01.103.

[11]

REA C, MONTES K J, ERICKSON K G, et al. A real-time machine learning-based disruption predictor in DIII-D [J]. Nuclear fusion, 2019, 59(9): 096016. DOI: 10.1088/1741-4326/ab28bf.

[12]

WANG N C, LIANG Y, DING Y H, et al. Advances in physics and applications of 3D magnetic perturbations on the J-TEXT tokamak [J]. Nuclear fusion, 2022, 62(4): 042016. DOI: 10.1088/1741-4326/ac3aff.

[13]

胡斐然. J-TEXT实时破裂预测与避免系统设计与实现 [D]. 武汉: 华中科技大学, 2018.

HU F R. The design and implementation of real-time disruption prediction and avoidance system on J-TEXT [D]. Wuhan: Huazhong University of Science and Technology, 2018.

[14]

唐畴尧. 基于机器学习的J-TEXT实时等离子体破裂预测及非磁位移测量技术研究 [D]. 武汉: 华中科技大学, 2022. DOI: 10.27157/d.cnki.ghzku.2022.001349.

TANG C Y. Technology research on J-TEXT real-time plasma disruption prediction and non-magnetic displacement measurement based on machine learning [D]. Wuhan: Huazhong University of Science and Technology, 2022. DOI: 10.27157/d.cnki.ghzku.2022.001349.

[15]

KATES-HARBECK J, SVYATKOVSKIY A, TANG W. Predicting disruptive instabilities in controlled fusion plasmas through deep learning [J]. Nature, 2019, 568(7753): 526-531. DOI: 10.1038/s41586-019-1116-4.

[16]

ZHU J X, REA C, MONTES K, et al. Hybrid deep-learning architecture for general disruption prediction across multiple tokamaks [J]. Nuclear fusion, 2021, 61(2): 026007. DOI: 10.1088/1741-4326/abc664.

[17]

TANG W, DONG G, BARR J, et al. Implementation of AI/DEEP learning disruption predictor into a plasma control system [J]. Contributions to plasma physics, 2023, 63(5/6): e202200095. DOI: 10.1002/ctpp.202200095.

[18]

BELLIZIO T, DE TOMMASI G, RISOLI N, et al. A MARTe based simulator for the JET vertical stabilization system [J]. Fusion engineering and design, 2011, 86(6/8): 1026-1029. DOI: 10.1016/j.fusengdes.2011.02.076.

[19]

ZHANG M, ZHENG G Z, ZHENG W, et al. JRTF: a flexible software framework for real-time control in magnetic confinement nuclear fusion experiments [J]. IEEE transactions on nuclear science, 2016, 63(2): 1070-1075. DOI: 10.1109/TNS.2016.2518709.

[20]

LIU G M, MAKIJARVI P, PONS N. The ITER CODAC network design [J]. Fusion engineering and design, 2018, 130: 6-10. DOI: 10.1016/j.fusengdes.2018.02.072.

[21]

LU T C, YUAN Q P, ZHANG R R, et al. Research on transparent access method for multiple types of data acquisition device in EAST PCS [J]. Fusion engineering and design, 2022, 179: 113135. DOI: 10.1016/j.fusengdes.2022.113135.

[22]

KADZIELA M, JABLONSKI B, PEREK P, et al. Evaluation of the ITER real-time framework for data acquisition and processing from pulsed gigasample digitizers [J]. Journal of fusion energy, 2020, 39(5): 261-269. DOI: 10.1007/s10894-020-00264-3.

[23]

BONCAGNI L, CENTIOLI C, IANNONE F, et al. Synchronous databus network in ITER: open source real-time network for the next nuclear fusion experiment [J]. Fusion engineering and design, 2008, 83(2/3): 504-510. DOI: 10.1016/j.fusengdes.2007.10.007.

[24]

XIAO B J, HUMPHREYS D A, WALKER M L, et al. EAST plasma control system [J]. Fusion engineering and design, 2008, 83(2/3): 181-187. DOI: 10.1016/j.fusengdes.2007.12.028.

[25]

KWON G, LEE W, BERTRAND B, et al. Development of real-time network translator between ITER synchronous data bus network and reflective memory [J]. Fusion engineering and design, 2017, 123: 955-959. DOI: 10.1016/j.fusengdes.2017.03.024.

[26]

ZHENG W, LIU Q, ZHANG M, et al. J-TEXT distributed data storage and management system [J]. Fusion engineering and design, 2018, 129: 207-213. DOI: 10.1016/j.fusengdes.2018.02.058.

[27]

ZHENG W, XUE F M, CHEN Z Y, et al. Disruption prediction for future tokamaks using parameter-based transfer learning [J]. Communications physics, 2023, 6(1): 181. DOI: 10.1038/s42005-023-01296-9.

[28]

GUO D J, HU Q M, LI D, et al. Upgrade of the Mirnov probe arrays on the J-TEXT tokamak [J]. Review of scientific instruments, 2017, 88(12): 123502. DOI: 10.1063/1.4996360.

[29]

GUO D J, HU Q M, LI D, et al. Development of the saddle loop sensors on the J-TEXT tokamak [J]. AIP Advances, 2017, 7(10): 105002. DOI: 10.1063/1.4993480.

[30]

DING Y H, ZHUANG G, ZHANG X Q, et al. Soft X-ray imaging diagnostic system on the J-TEXT tokamak [J]. Nuclear instruments and methods in physics research section A: accelerators, spectrometers, detectors and associated equipment, 2009, 606(3): 743-748. DOI: 10.1016/j.nima.2009.05.012.

[31]

ZHANG X L, CHENG Z F, LIN X D, et al. Spectral diagnostic system for light impurity transport study in J-TEXT tokamak [J]. Fusion engineering and design, 2019, 147: 111241. DOI: 10.1016/j.fusengdes.2019.111241.

[32]

ZHANG X L, CHENG Z F, HOU S Y, et al. Upgrade of absolute extreme ultraviolet diagnostic on J-TEXT [J]. Review of scientific instruments, 2014, 85(11): 11E420. DOI: 10.1063/1.4891159.

[33]

ZHENG W, HU F R, ZHANG M, et al. Profile aided real-time plasma electron density feedback control based on FPGA on J-TEXT [J]. IEEE transactions on nuclear science, 2018, 65(2): 771-776. DOI: 10.1109/TNS.2017.2782788.

[34]

YANG Z Y, XIA F, SONG X M, et al. Real-time disruption prediction in the plasma control system of HL-2A based on deep learning [J]. Fusion engineering and design, 2022, 182: 113223. DOI: 10.1016/j.fusengdes.2022.113223.

[1]	王龙. 磁约束等离子体实验物理 [M]. 北京: 科学出版社, 2018. WANG L. Experimental physics of magnetic confinement plasmas [M]. Beijing: Science Press, 2018.
[2]	YOKOYAMA T, MIYOSHI Y, HIWATARI R, et al. Prediction of high-beta disruptions in JT-60U based on sparse modeling using exhaustive search [J]. Fusion engineering and design, 2019, 140: 67-80. DOI: 10.1016/j.fusengdes.2019.01.128.
[3]	YUAN J S, ZUO G Z, ZHAO S B, et al. Design of a shattered pellet injector and preliminary bench tests of Ne pellet formation for EAST disruption mitigation [J]. Fusion engineering and design, 2023, 191: 113567. DOI: 10.1016/j.fusengdes.2023.113567.
[4]	LANDMAN I S, PESTCHANYI S E, IGITKHANOV Y, et al. Two-dimensional modeling of disruption mitigation by gas injection [J]. Fusion engineering and design, 2011, 86(9/11): 1616-1619. DOI: 10.1016/j.fusengdes.2010.12.017.
[5]	李峰. 用于托卡马克等离子体破裂缓解的电磁弹丸注入系统研制 [D]. 武汉: 华中科技大学, 2023. LI F. Development of electromagnetic pellet-injection system for tokamak plasma disruption mitigation [D]. Wuhan: Huazhong University of Science and Technology, 2023.
[6]	王雪玲. J-TEXT等离子体破裂预测及逃逸电子束控制的研究 [D]. 武汉: 华中科技大学, 2018. WANG X L. Research of the plasma disruption prediction and runaway electron beams control on J-TEXT [D]. Wuhan: Huazhong University of Science and Technology, 2018.
[7]	QIU H B, HU Z Z, WU S F, et al. Initial analytical theory of plasma disruption and experimental evidence [J]. Scientific reports, 2023, 13(1): 9551. DOI: 10.1038/s41598-023-36504-7.
[8]	吴其其. 基于深度学习异常检测的等离子体破裂预测 [D]. 武汉: 华中科技大学, 2021. DOI: 10.27157/d.cnki.ghzku.2021.001055. WU Q Q. Plasma disruption prediction based on deep learning and anomaly detection [D]. Wuhan: Huazhong University of Science and Technology, 2021. DOI: 10.27157/d.cnki.ghzku.2021.001055.
[9]	RATTÁ G A, VEGA J, MURARI A, et al. An advanced disruption predictor for JET tested in a simulated real-time environment [J]. Nuclear fusion, 2010, 50(2): 025005. DOI: 10.1088/0029-5515/50/2/025005.
[10]	ALEDDA R, CANNAS B, FANNI A, et al. Multivariate statistical models for disruption prediction at ASDEX upgrade [J]. Fusion engineering and design, 2013, 88(6/8): 1297-1301. DOI: 10.1016/j.fusengdes.2013.01.103.
[11]	REA C, MONTES K J, ERICKSON K G, et al. A real-time machine learning-based disruption predictor in DIII-D [J]. Nuclear fusion, 2019, 59(9): 096016. DOI: 10.1088/1741-4326/ab28bf.
[12]	WANG N C, LIANG Y, DING Y H, et al. Advances in physics and applications of 3D magnetic perturbations on the J-TEXT tokamak [J]. Nuclear fusion, 2022, 62(4): 042016. DOI: 10.1088/1741-4326/ac3aff.
[13]	胡斐然. J-TEXT实时破裂预测与避免系统设计与实现 [D]. 武汉: 华中科技大学, 2018. HU F R. The design and implementation of real-time disruption prediction and avoidance system on J-TEXT [D]. Wuhan: Huazhong University of Science and Technology, 2018.
[14]	唐畴尧. 基于机器学习的J-TEXT实时等离子体破裂预测及非磁位移测量技术研究 [D]. 武汉: 华中科技大学, 2022. DOI: 10.27157/d.cnki.ghzku.2022.001349. TANG C Y. Technology research on J-TEXT real-time plasma disruption prediction and non-magnetic displacement measurement based on machine learning [D]. Wuhan: Huazhong University of Science and Technology, 2022. DOI: 10.27157/d.cnki.ghzku.2022.001349.
[15]	KATES-HARBECK J, SVYATKOVSKIY A, TANG W. Predicting disruptive instabilities in controlled fusion plasmas through deep learning [J]. Nature, 2019, 568(7753): 526-531. DOI: 10.1038/s41586-019-1116-4.
[16]	ZHU J X, REA C, MONTES K, et al. Hybrid deep-learning architecture for general disruption prediction across multiple tokamaks [J]. Nuclear fusion, 2021, 61(2): 026007. DOI: 10.1088/1741-4326/abc664.
[17]	TANG W, DONG G, BARR J, et al. Implementation of AI/DEEP learning disruption predictor into a plasma control system [J]. Contributions to plasma physics, 2023, 63(5/6): e202200095. DOI: 10.1002/ctpp.202200095.
[18]	BELLIZIO T, DE TOMMASI G, RISOLI N, et al. A MARTe based simulator for the JET vertical stabilization system [J]. Fusion engineering and design, 2011, 86(6/8): 1026-1029. DOI: 10.1016/j.fusengdes.2011.02.076.
[19]	ZHANG M, ZHENG G Z, ZHENG W, et al. JRTF: a flexible software framework for real-time control in magnetic confinement nuclear fusion experiments [J]. IEEE transactions on nuclear science, 2016, 63(2): 1070-1075. DOI: 10.1109/TNS.2016.2518709.
[20]	LIU G M, MAKIJARVI P, PONS N. The ITER CODAC network design [J]. Fusion engineering and design, 2018, 130: 6-10. DOI: 10.1016/j.fusengdes.2018.02.072.
[21]	LU T C, YUAN Q P, ZHANG R R, et al. Research on transparent access method for multiple types of data acquisition device in EAST PCS [J]. Fusion engineering and design, 2022, 179: 113135. DOI: 10.1016/j.fusengdes.2022.113135.
[22]	KADZIELA M, JABLONSKI B, PEREK P, et al. Evaluation of the ITER real-time framework for data acquisition and processing from pulsed gigasample digitizers [J]. Journal of fusion energy, 2020, 39(5): 261-269. DOI: 10.1007/s10894-020-00264-3.
[23]	BONCAGNI L, CENTIOLI C, IANNONE F, et al. Synchronous databus network in ITER: open source real-time network for the next nuclear fusion experiment [J]. Fusion engineering and design, 2008, 83(2/3): 504-510. DOI: 10.1016/j.fusengdes.2007.10.007.
[24]	XIAO B J, HUMPHREYS D A, WALKER M L, et al. EAST plasma control system [J]. Fusion engineering and design, 2008, 83(2/3): 181-187. DOI: 10.1016/j.fusengdes.2007.12.028.
[25]	KWON G, LEE W, BERTRAND B, et al. Development of real-time network translator between ITER synchronous data bus network and reflective memory [J]. Fusion engineering and design, 2017, 123: 955-959. DOI: 10.1016/j.fusengdes.2017.03.024.
[26]	ZHENG W, LIU Q, ZHANG M, et al. J-TEXT distributed data storage and management system [J]. Fusion engineering and design, 2018, 129: 207-213. DOI: 10.1016/j.fusengdes.2018.02.058.
[27]	ZHENG W, XUE F M, CHEN Z Y, et al. Disruption prediction for future tokamaks using parameter-based transfer learning [J]. Communications physics, 2023, 6(1): 181. DOI: 10.1038/s42005-023-01296-9.
[28]	GUO D J, HU Q M, LI D, et al. Upgrade of the Mirnov probe arrays on the J-TEXT tokamak [J]. Review of scientific instruments, 2017, 88(12): 123502. DOI: 10.1063/1.4996360.
[29]	GUO D J, HU Q M, LI D, et al. Development of the saddle loop sensors on the J-TEXT tokamak [J]. AIP Advances, 2017, 7(10): 105002. DOI: 10.1063/1.4993480.
[30]	DING Y H, ZHUANG G, ZHANG X Q, et al. Soft X-ray imaging diagnostic system on the J-TEXT tokamak [J]. Nuclear instruments and methods in physics research section A: accelerators, spectrometers, detectors and associated equipment, 2009, 606(3): 743-748. DOI: 10.1016/j.nima.2009.05.012.
[31]	ZHANG X L, CHENG Z F, LIN X D, et al. Spectral diagnostic system for light impurity transport study in J-TEXT tokamak [J]. Fusion engineering and design, 2019, 147: 111241. DOI: 10.1016/j.fusengdes.2019.111241.
[32]	ZHANG X L, CHENG Z F, HOU S Y, et al. Upgrade of absolute extreme ultraviolet diagnostic on J-TEXT [J]. Review of scientific instruments, 2014, 85(11): 11E420. DOI: 10.1063/1.4891159.
[33]	ZHENG W, HU F R, ZHANG M, et al. Profile aided real-time plasma electron density feedback control based on FPGA on J-TEXT [J]. IEEE transactions on nuclear science, 2018, 65(2): 771-776. DOI: 10.1109/TNS.2017.2782788.
[34]	YANG Z Y, XIA F, SONG X M, et al. Real-time disruption prediction in the plasma control system of HL-2A based on deep learning [J]. Fusion engineering and design, 2022, 182: 113223. DOI: 10.1016/j.fusengdes.2022.113223.