Citation: | Yue GE, Tianchao XU, Chijie XIAO, Zhibin GUO, Xiaogang WANG, Renchuan HE, Xiaoyi YANG, Zuyu ZHANG, Ruixin YUAN. Inward particle transport driven by biased endplate in a cylindrical magnetized plasma[J]. Plasma Science and Technology, 2024, 26(3): 034017. DOI: 10.1088/2058-6272/ad1676 |
The inward particle transport is associated with the formation of peaked density profiles, which contributes to improve the fusion rate and the realization of steady-state discharge. The active control of inward particle transport is considered as one of the most critical issues of magnetic confinement fusion. Recently, it is realized preliminarily by adding a biased endplate in the Peking University Plasma Test (PPT) device. The results reveal that the inward particle flux increases with the bias voltage of the endplate. It is also found that the profile of radial electric field (Er) shear is flattened by the increased bias voltage. Radial velocity fluctuations affect the inward particle more than density fluctuations, and the frequency of the dominant mode driving inward particle flux increases with the biased voltage applied to the endplate. The experimental results in the PPT device provide a method to actively control the inward particle flux using a biased endplate and enrich the understanding of the relationship between {\boldsymbol{E}}_r\times{{\boldsymbol{B}}} shear and turbulence transport.
[1] |
Zhao N et al 2016 Phys. Plasmas 23 062309 doi: 10.1063/1.4953601
|
[2] |
Shats M G and Rudakov D L 1997 Phys. Rev. Lett. 79 2690 doi: 10.1103/PhysRevLett.79.2690
|
[3] |
Shats M G et al 2000 Phys. Rev. Lett. 84 6042 doi: 10.1103/PhysRevLett.84.6042
|
[4] |
Ohkuni K et al 2001 Phys. Plasmas 8 4035 doi: 10.1063/1.1387267
|
[5] |
Boedo J A et al 2002 Nucl. Fusion 42 117 doi: 10.1088/0029-5515/42/2/301
|
[6] |
Xu Y et al 2006 Phys. Rev. Lett. 97 165003 doi: 10.1103/PhysRevLett.97.165003
|
[7] |
Kong D F et al 2017 Nucl. Fusion 57 014005 doi: 10.1088/0029-5515/57/1/014005
|
[8] |
Burrell K H et al 2009 Phys. Rev. Lett. 102 155003 doi: 10.1103/PhysRevLett.102.155003
|
[9] |
Cui L et al 2015 Phys. Plasmas 22 050704 doi: 10.1063/1.4921671
|
[10] |
Liu H et al 2023 Plasma Phys. Control. Fusion 65 055017 doi: 10.1088/1361-6587/acc209
|
[11] |
Ke R et al 2022 Nucl. Fusion 62 076014 doi: 10.1088/1741-4326/ac5fe9
|
[12] |
Garofalo A M et al 2011 Nucl. Fusion 51 083018 doi: 10.1088/0029-5515/51/8/083018
|
[13] |
Burrell K H et al 2016 Phys. Plasmas 23 056103 doi: 10.1063/1.4943521
|
[14] |
Ding S et al 2020 Nucl. Fusion 60 034001 doi: 10.1088/1741-4326/ab66db
|
[15] |
Sun Y et al 2014 Plasma Phys. Control. Fusion 56 015001 doi: 10.1088/0741-3335/56/1/015001
|
[16] |
Van Oost G et al 2003 Plasma Phys. Control. Fusion 45 621 doi: 10.1088/0741-3335/45/5/308
|
[17] |
Kuznetsov Y K et al 2012 Nucl. Fusion 52 063004 doi: 10.1088/0029-5515/52/6/063004
|
[18] |
Yoshinuma M et al 1999 Fusion Technol. 35 278 doi: 10.13182/FST99-A11963867
|
[19] |
Tsushima A and Sato N 1991 J. Phys. Soc. Japan 60 2665 doi: 10.1143/JPSJ.60.2665
|
[20] |
Thakur S C et al 2014 Plasma Sources Sci. Technol. 23 044006 doi: 10.1088/0963-0252/23/4/044006
|
[21] |
Xiao C J et al 2016 Rev. Sci. Instrum. 87 11D610 doi: 10.1063/1.4961282
|
[22] |
Yan Z et al 2008 Phys. Plasmas 15 092309 doi: 10.1063/1.2985836
|
[23] |
Beall J M, Kim Y C and Powers E J 1982 J. Appl. Phys. 53 3933 doi: 10.1063/1.331279
|
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1. | Wang, D., Xu, L., Gao, W. et al. Application of Semi-Supervised Learning Model to Coal Sample Classification. Applied Sciences (Switzerland), 2024, 14(4): 1606. DOI:10.3390/app14041606 | |
2. | Sun, Y., Liu, L., Xiao, L. Application of Wavelet Threshold Denoising in LIBS Spectral Denoising. 2024. DOI:10.1109/EIT63098.2024.10762555 | |
3. | Wu, L., Kim, S.K. Evaluating the economic and climate adaptation benefits of land conservation strategies in urban coastal regions of the U.S. and China. Climate Risk Management, 2024. DOI:10.1016/j.crm.2024.100632 | |
4. | Dong, M., Cai, J., Liu, H. et al. A review of laser-induced breakdown spectroscopy and spontaneous emission techniques in monitoring thermal conversion of fuels. Spectrochimica Acta - Part B Atomic Spectroscopy, 2023. DOI:10.1016/j.sab.2023.106807 | |
5. | Yang, L., Xiang, Y., Li, Y. et al. Identification and classification of recyclable waste using laser-induced breakdown spectroscopy technology. AIP Advances, 2023, 13(7): 075024. DOI:10.1063/5.0149329 | |
6. | Xu, L., Shu, Q., Fu, H. et al. Estimation of Quercus Biomass in Shangri-La Based on GEDI Spaceborne Lidar Data. Forests, 2023, 14(5): 876. DOI:10.3390/f14050876 | |
7. | Brunnbauer, L., Gajarska, Z., Lohninger, H. et al. A critical review of recent trends in sample classification using Laser-Induced Breakdown Spectroscopy (LIBS). TrAC - Trends in Analytical Chemistry, 2023. DOI:10.1016/j.trac.2022.116859 | |
8. | Guan, C., Wu, T., Chen, J. et al. Detection of Carbon Content from Pulverized Coal Using LIBS Coupled with DSC-PLS Method. Chemosensors, 2022, 10(11): 490. DOI:10.3390/chemosensors10110490 | |
9. | Zhang, Q., Liu, Y. Review of In-situ Online LIBS Detection in the Atmospheric Environment. Atomic Spectroscopy, 2022, 43(2): 174-185. DOI:10.46770/AS.2021.609 | |
10. | Zhang, D., Zhang, H., Zhao, Y. et al. A brief review of new data analysis methods of laser-induced breakdown spectroscopy: machine learning. Applied Spectroscopy Reviews, 2022, 57(2): 89-111. DOI:10.1080/05704928.2020.1843175 | |
11. | Kim, S.K., Bennett, M.M., van Gevelt, T. et al. Urban agglomeration worsens spatial disparities in climate adaptation. Scientific Reports, 2021, 11(1): 8446. DOI:10.1038/s41598-021-87739-1 | |
12. | Song, Y., Song, W., Yu, X. et al. Improvement of sample discrimination using laser-induced breakdown spectroscopy with multiple-setting spectra. Analytica Chimica Acta, 2021. DOI:10.1016/j.aca.2021.339053 | |
13. | Liu, K., He, C., Zhu, C. et al. A review of laser-induced breakdown spectroscopy for coal analysis. TrAC - Trends in Analytical Chemistry, 2021. DOI:10.1016/j.trac.2021.116357 | |
14. | Teng, G., Wang, Q., Cui, X. et al. Predictive data clustering of laser-induced breakdown spectroscopy for brain tumor analysis. Biomedical Optics Express, 2021, 12(7): 4438-4451. DOI:10.1364/BOE.431356 | |
15. | Yang, Y., Zhang, L., Hao, X. et al. Classification of iron ore based on machine learning and laser induced breakdown spectroscopy | [机器学习结合激光诱导击穿光谱技术铁矿石分类方法]. Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering, 2021, 50(5): 20200490. DOI:10.3788/IRLA20200490 | |
16. | Wang, C., Wang, J., Wang, J. et al. Classification of 13 original rock samples by laser induced breakdown spectroscopy. Laser Physics, 2021, 31(3): 035601. DOI:10.1088/1555-6611/abdfc8 | |
17. | Jayaganthan, S., Babu, M.S., Vasa, N.J. et al. Classification of coal deposited epoxy micro-nanocomposites by adopting machine learning techniques to libs analysis. Journal of Physics Communications, 2021, 5(10): 105006. DOI:10.1088/2399-6528/ac2b5d | |
18. | Liu, X., Che, X., Li, K. et al. Geographical authenticity evaluation of Mentha haplocalyx by LIBS coupled with multivariate analyzes. Plasma Science and Technology, 2020, 22(7): 074006. DOI:10.1088/2058-6272/ab7eda | |
19. | Feng, Z., Zhang, D., Wang, B. et al. The classification of plants by laser-induced breakdown spectroscopy based on two chemometric methods. Plasma Science and Technology, 2020, 22(7): 074012. DOI:10.1088/2058-6272/ab84ed | |
20. | Dong, M., Wei, L., González, J.J. et al. Coal Discrimination Analysis Using Tandem Laser-Induced Breakdown Spectroscopy and Laser Ablation Inductively Coupled Plasma Time-of-Flight Mass Spectrometry. Analytical Chemistry, 2020, 92(10): 7003-7010. DOI:10.1021/acs.analchem.0c00188 | |
21. | Yang, Y., Hao, X., Zhang, L. et al. Application of scikit and keras libraries for the classification of iron ore data acquired by laser-induced breakdown spectroscopy (LIBS). Sensors (Switzerland), 2020, 20(5): 1393. DOI:10.3390/s20051393 | |
22. | Wang, Z., Hou, Z., Zhang, L. et al. Coal analysis. Laser-Induced Breakdown Spectroscopy, Second Edition, 2020. DOI:10.1016/B978-0-12-818829-3.00021-6 | |
23. | Xia, W., Zeng, J., Long, Z. et al. Study on fracture fault diagnosis online method of bogie of maglev train. 2019. DOI:10.1109/CAC48633.2019.8996297 | |
24. | Wang, Z., Wang, S., Kong, D. et al. Methane detection based on improved chicken algorithm optimization support vector machine. Applied Sciences (Switzerland), 2019, 9(9): 1761. DOI:10.3390/app9091761 | |
25. | Fu, Y., Hou, Z., Deguchi, Y. et al. From big to strong: Growth of the Asian laser-induced breakdown spectroscopy community. Plasma Science and Technology, 2019, 21(3): 030101. DOI:10.1088/2058-6272/aaf873 |