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Simulation of effect of neoclassical convection on tungsten impurity core accumulation in tokamak

LUO Yuchen SANG Chaofeng WANG Yilin WU Yihan ZHOU Qingrui LI Jiaxian XUE Miao XUE Lei ZHENG Guoyao DU Hailong WANG Dezhen

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Simulation of effect of neoclassical convection on tungsten impurity core accumulation in tokamak

LUO Yuchen, SANG Chaofeng, WANG Yilin, WU Yihan, ZHOU Qingrui, LI Jiaxian, XUE Miao, XUE Lei, ZHENG Guoyao, DU Hailong, WANG Dezhen
cstr: 32037.14.aps.74.20250384
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  • Controlling of tungsten (W) impurity core accumulation is of great significance for the steady-state operation of tokamaks. This work mainly investigates the effect of neoclassical transport on the core accumulation of W impurities by using STRAHL code. The study focuses on the HL-3 device, which will use tungsten divertor and conduct research under argon gas injection discharge conditions. In the simulation, the edge and core background plasma parameters are obtained by SOLPS-ITER and OMFIT simulations, respectively. The distribution of tungsten impurities in the boundary region is simulated using the IMPEDGE code. The edge anomalous transport coefficient in STRAHL is adjusted accordingly, and the simulation results are compared with those from the IMPEDGE to ensure consistency in impurity distribution between the core and edge. In the core region, a numerical scan is performed to adjust the simulation results so that the energy radiation matches the setting values, thereby determining the specific turbulence convection velocity. By setting the coefficients for both the core region and the boundary region, a complete distribution of W impurities from boundary to the core is obtained. To account for the neoclassical transport effects, the neoclassical transport coefficients are calculated using the subroutine NEOART and applied to the impurity transport simulation, and the simulation region is set from $ \rho =0 $ to 0.9. On this basis, the transport of W impurities with and without neoclassical convection is simulated. The simulation results show that without neoclassical convection, anomalous transport dominates the impurity transport, which is inward and enhances impurity accumulation in the core, and the core impurity density reaches $ 1.1\times {10}^{16}\;{{\mathrm{m}}}^{-3} $. After introducing neoclassical convection which is outward, it can offset the inward anomalous convection and significantly reduces the W impurity density in the core, thereby significantly reducing the core tungsten impurity density to $ 4.0\times {10}^{15}\;{{\mathrm{m}}}^{-3} $. In addition, the neoclassical convection in the region of $ \rho$ = 0.72–0.90 plays a more important role in reducing the core impurity density. Further analysis of the components of neoclassical convection shows that the Pfirsche-Schlüter (PS) component dominates the neoclassical convection term, which is mainly driven by the ion temperature gradient term. Therefore, experimentally, plasma heating can be used to enhance the temperature gradient and suppress impurity core accumulation.
      Corresponding author: SANG Chaofeng, sang@dlut.edu.cn
    • Funds: Project supported by the National Magnetic Confinement Fusion Energy Program (Grant No. 2024YFE03160001), the National Natural Science Foundation of China (Grant Nos. 12235002, 12261131496), the Science & Technology Talents of Dalian, China (Grant No. 2022RJ11), and the Revitalization Talents Program of Liaoning Province, China (Grant No. XLYC2203182).
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  • 图 1  模拟方法及程序耦合流程图

    Figure 1.  Simulation method and program coupling flowchart.

    图 2  $ \rho $= 0—1.01区域参数的径向剖面 (a)电子密度ne; (b)电子温度 Te; 和(c)离子温度Ti; 其中红线代表芯部$ \rho $= 0—0.98区域参数(OMFIT提供), 蓝线代表边缘$ \rho $= 0.98—1.01区域参数(SOLPS-ITER模拟), 虚线代表$ \rho $= 0.98位置

    Figure 2.  Radial profiles in the region $ \rho $= 0–1.01: (a) Electron density ne; (b) electron temperature Te; (c) ion temperature Ti. The red line represents the core $ \rho $= 0–0.98 region parameter (provided by OMFIT), the blue line represents the edge $ \rho $= 0.98–1.01 region parameters (provided by SOLPS-ITER), and the dashed line represents the $ \rho $= 0.98 location.

    图 3  (a) $ \rho $= 0.98—1.01区域的杂质分布, 其中蓝线为IMPEDGE模拟分布, 蓝点为STRAHL模拟分布; (b) $ \rho $= 0.98—1.01区域湍流扩散系数$ {D}^{{\mathrm{A}}{\mathrm{N}}{\mathrm{O}}} $与对流速度$ {v}^{{\mathrm{A}}{\mathrm{N}}{\mathrm{O}}} $分布; (c) $ \rho = $0—0.98区域湍流扩散系数$ {D}^{{\mathrm{A}}{\mathrm{N}}{\mathrm{O}}} $与对流速度$ {v}^{{\mathrm{A}}{\mathrm{N}}{\mathrm{O}}} $分布

    Figure 3.  (a) Impurity distribution in the region $ \rho $= 0.98–1.01, the blue line represents the IMPEDGE simulation distribution, and the blue dots represent the STRAHL simulation distribution; (b) anomalous diffusion coefficient $ {D}^{{\mathrm{A}}{\mathrm{N}}{\mathrm{O}}} $and convective velocity $ {v}^{{\mathrm{A}}{\mathrm{N}}{\mathrm{O}}} $distributions in the region $ \rho $= 0.98–1.01; (c) anomalous diffusion coefficient $ {D}^{{\mathrm{A}}{\mathrm{N}}{\mathrm{O}}} $and convective velocity $ {v}^{{\mathrm{A}}{\mathrm{N}}{\mathrm{O}}} $ distributions in the region in the region $ \rho = $0–0.98.

    图 4  区域$ \rho =0—0.9 $ (a) $ {v}^{{\mathrm{N}}{\mathrm{E}}{\mathrm{O}}} $, $ {v}^{{\mathrm{A}}{\mathrm{N}}{\mathrm{O}}} $和$ {v}^{{\mathrm{T}}{\mathrm{O}}{\mathrm{T}}{\mathrm{A}}{\mathrm{L}}} $径向分布; (b) 3种情况下$ {v}^{{\mathrm{N}}{\mathrm{E}}{\mathrm{O}}} $分布设置; (c) 区域$ \rho =0—1.01 $三种情况下模拟得到的钨杂质密度$ {n}_{{\mathrm{W}}}^{{\mathrm{t}}{\mathrm{o}}{\mathrm{t}}} $径向分布, 其中杂质边缘分布均与IMPEDGE一致, 虚线分别表示$ \rho $= 0.72和$ \rho $= 0.98所在位置

    Figure 4.  (a) The radial distributions of $ {v}^{{\mathrm{N}}{\mathrm{E}}{\mathrm{O}}} $, $ {v}^{{\mathrm{A}}{\mathrm{N}}{\mathrm{O}}} $, and $ {v}^{{\mathrm{T}}{\mathrm{O}}{\mathrm{T}}{\mathrm{A}}{\mathrm{L}}} $; (b) the setup of $ {v}^{{\mathrm{N}}{\mathrm{E}}{\mathrm{O}}} $ distribution for three different cases in the region $ \rho$ = 0–0.9; (c) radial distribution of tungsten impurity density $ {n}_{{\mathrm{W}}}^{{\mathrm{t}}{\mathrm{o}}{\mathrm{t}}} $ of different cases in the region $ \rho$ = 0–1.01, the $ {n}_{{\mathrm{W}}}^{{\mathrm{t}}{\mathrm{o}}{\mathrm{t}}} $ distributions in the edge region are consistent with those from IMPEDGE. The dashed lines represent the $ \rho =0.72 $ and $ \rho =0.98 $ location.

    图 5  区域$ \rho =0—0.9 $ (a)新经典对流速度的3个分量; (b)新经典对流PS分量$ {v}_{{\mathrm{P}}{\mathrm{S}}}^{{\mathrm{N}}{\mathrm{E}}{\mathrm{O}}} $及离子温度梯度项$ {v}_{{\mathrm{P}}{\mathrm{S}}\_{\mathrm{T}}}^{{\mathrm{N}}{\mathrm{E}}{\mathrm{O}}} $和密度梯度项$ {v}_{{\mathrm{P}}{\mathrm{S}}\_{\mathrm{n}}}^{{\mathrm{N}}{\mathrm{E}}{\mathrm{O}}} $, 虚线位置为$ \rho =0.55 $

    Figure 5.  (a) Three components of the neoclassical convective velocity; (b) the neoclassical convective PS component $ {v}_{{\mathrm{P}}{\mathrm{S}}}^{{\mathrm{N}}{\mathrm{E}}{\mathrm{O}}} $ along with its temperature gradient term $ {v}_{{\mathrm{P}}{\mathrm{S}}\_{\mathrm{T}}}^{{\mathrm{N}}{\mathrm{E}}{\mathrm{O}}} $ and density gradient term $ {v}_{{\mathrm{P}}{\mathrm{S}}\_{\mathrm{n}}}^{{\mathrm{N}}{\mathrm{E}}{\mathrm{O}}} $ in region $ \rho =0$–0.9, the dashed line represents the $ \rho =0.55 $ location.

    图 6  区域$ \rho =0—0.9 $杂质碰撞率$ {\nu }_{{\mathrm{I}}}^{{\mathrm{*}}} $分布, 虚线位置为$ \rho =0.55 $

    Figure 6.  Impurity collision rate $ {\nu }_{{\mathrm{I}}}^{{\mathrm{*}}} $ distribution in the region $ \rho$ = 0–0.9 , the dashed line represents the $ \rho =0.55 $ location.

    表 1  不同$ {v}^{{\mathrm{N}}{\mathrm{E}}{\mathrm{O}}} $使用区域情况下的$ \rho =0 $和$ \rho =0.72 $处钨密度, $ \rho =0 $处钨浓度, $ \rho =0 $处钨杂质引起的总辐射损失密度

    Table 1.  In different $ {v}^{{\mathrm{N}}{\mathrm{E}}{\mathrm{O}}} $ applied region cases, the tungsten density at $ \rho =0 $ and $ \rho =0.72 $, the tungsten concentration at $ \rho =0 $ and total power radiation loss density by tungsten at $ \rho =0 $.

    使用区域 W/O NEO With NEO
    $ \rho $= 0.72—0.90
    With NEO
    $ \rho $= 0—0.9
    $ {n}_{{\mathrm{w}}}^{\rho =0.72} $(1015 m–3) $ 6.00 $ $ 3.80 $ $ 3.80 $
    $ {n}_{{\mathrm{w}}}^{\rho =0} $(1016 m–3) $ 1.10 $ $ 0.58 $ $ 0.40 $
    $ {C}_{{\mathrm{w}}}^{\rho =0} $(10–5) 7.70 4.20 2.50
    $ {P}_{{\mathrm{r}}{\mathrm{a}}{\mathrm{d}}}^{\rho =0} $(MW·m–3) 0.26 0.15 0.11
    DownLoad: CSV
  • [1]

    Neu R, Dux R, Geier A, Kallenbach A, Pugno R, Rohde V, Bolshukhin D, Fuchs J C, Gehre O, Gruber O, Hobirk J, Kaufmann M, Krieger K, Laux M, Maggi C, Murmann H, Neuhauser J, Ryter F, Sips A C C, Stäbler A, Stober J, Suttrop W, Zohm H 2002 Plasma Phys. Control. Fusion 44 811Google Scholar

    [2]

    Sang C F, Zhou Q R, Xu G S, Wang L, Wang Y L, Zhao X L, Zhang C, Ding R, Jia G Z, Yao D M, Liu X J, Si H, Wang D Z, the EAST Team 2021 Nucl. Fusion 61 066004Google Scholar

    [3]

    Liu B, Dai S Y, Yang X D, Chan V S, Ding R, Zhang H M, Feng Y, Wang D Z 2022 Nucl. Fusion 62 126040Google Scholar

    [4]

    Pitts R A, Bonnin X, Escourbiac F, Frerichs H, Gunn J P, Hirai T, Kukushkin A S, Kaveeva E, Miller M A, Moulton D, Rozhansky V, Senichenkov I, Sytova E, Schmitz O, Stangeby P C, De Temmerman G, Veselova I, Wiesen S 2019 Nucl. Mater. Energy 20 100696Google Scholar

    [5]

    Gruber O, Sips A C C, Dux R, Eich T, Fuchs J C, Herrmann A, Kallenbach A, Maggi C F, Neu R, Pütterich T, Schweinzer J, Stober J 2009 Nucl. Fusion 49 115014Google Scholar

    [6]

    Sun Z C, Lian Z W, Qiao W N, Yu J G, Han W J, Fu Q W, Zhu K G 2017 Chin. Phys. Lett. 34 030205Google Scholar

    [7]

    张启凡, 乐文成, 张羽昊, 葛忠昕, 邝志强, 萧声扬, 王璐 2024 物理学报 73 185201Google Scholar

    Zhang Q F, Le W C, Zhang Y H, Ge Z X, Kuang Z Q, Xiao S Y, Wang L 2024 Acta Phys. Sin. 73 185201Google Scholar

    [8]

    Angioni C 2021 Plasma Phys. Control. Fusion 63 073001Google Scholar

    [9]

    Guirlet R, Giroud C, Parisot T, Puiatti M E, Bourdelle C, Carraro L, Dubuit N, Garbet X, Thomas P R 2006 Plasma Phys. Control. Fusion 48 B63Google Scholar

    [10]

    Donnel P 2018 Ph. D. Dissertation (Aix-Marseille: Aix Marseille Université

    [11]

    Shi S Y, Chen J L, Bourdelle C, Jian X, Odstrčil T, Garofalo A M, Cheng Y X, Chao Y, Zhang L, Duan Y M, Wu M Q, Ding F, Li Y Y, Huang J, Qian J P, Gao X, Wan Y X 2022 Nucl. Fusion 62 066031Google Scholar

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    Shi S, Chen J, Jian X, Odstrčil T, Clarrisse B, Wu M, Wu M, Duan Y, Chao Y, Zhang L, Cheng Y, Qian J, Garofalo A M, Gong X, Gao X, Wan Y 2022 Nucl. Fusion 62 066040Google Scholar

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    Pütterich T, Dux R, Neu R, Bernert M, Beurskens M N A, Bobkov V, Brezinsek S, Challis C, Coenen J W, Coffey I, Czarnecka A, Giroud C, Jacquet P, Joffrin E, Kallenbach A, Lehnen M, Lerche E, de la Luna E, Marsen S, Matthews G, Mayoral M L, McDermott R M, Meigs A, Mlynar J, Sertoli M, van Rooij G 2013 Plasma Phys. Control. Fusion 55 124036Google Scholar

    [14]

    Lee H, Lee H, Han Y S, Song J, Belli E A, Choe W, Kang J, Lee J, Candy J, Lee J 2022 Phys. Plasmas 29 022504Google Scholar

    [15]

    赵伟宽, 张凌, 程云鑫, 周呈熙, 张文敏, 段艳敏, 胡爱兰, 王守信, 张丰玲, 李政伟, 曹一鸣, 刘海庆 2024 物理学报 73 035201Google Scholar

    Zhao W K, Zhang L, Cheng Y X, Zhou C X, Zhang W M, Duan Y M, Hu A L, Wang S X, Zhang F L, Li Z W, Cao Y M, Liu H Q 2024 Acta Phys. Sin. 73 035201Google Scholar

    [16]

    Mochinaga S, Kasuya N, Fukuyama A, Yagi M 2024 Nucl. Fusion 64 066002Google Scholar

    [17]

    Lim K, Garbet X, Sarazin Y, Gravier E, Lesur M, Lo-Cascio G, Rouyer T 2023 Phys. Plasmas 30 082501Google Scholar

    [18]

    Zheng G Y, Cai L Z, Duan X R, Xu X Q, Ryutov D D, Cai L J, Liu X, Li J X, Pan Y D 2016 Nucl. Fusion 56 126013Google Scholar

    [19]

    Cao C Z, Huang X M, Hu Y, Xie Y F, Zhou J, Qiao T, Gao J M, Cai L Z, Cao Z, HL-2A and HL-3 team 2025 Nucl. Mater. Energy 42 101852Google Scholar

    [20]

    Han J Y, He Y X, Zhao D Y, Cai L Z, Wang Y Q, Qian W, Huang W Y, Lu Y, Cai L J, Zhong W L 2025 Nucl. Mater. Energy 42 101861Google Scholar

    [21]

    Zhang X L, He Z Y H, Cheng Z F, Yan W, Dong Y B, Liu Y, Deng W, Fu B Z, Shi Z B, Zhang Y P, Shi Y J 2024 Fusion Eng. Des. 208 114674Google Scholar

    [22]

    Zhou Q R, Zhang Y J, Sang C F, Li J X, Zheng G Y, Wang Y L, Wu Y H, Wang D Z 2024 Plasma Sci. Technol. 26 104003Google Scholar

    [23]

    Zhang Y J, Sang C F, Li J X, Zheng G Y, Senichenkov I Y, Rozhansky V A, Zhang C, Wang Y L, Zhao X L, Wang D Z 2022 Nucl. Fusion 62 106006Google Scholar

    [24]

    Dux R 2006 STRAHL User Manual Report

    [25]

    Wu Y H, Zhou Q R, Sang C F, Zhang Y J, Wang Y L, Wang D Z 2022 Nucl. Mater. Energy 33 101297Google Scholar

    [26]

    Zhou Y, Zheng G Y, Du H L, Li J X, Xue L 2022 Fusion Eng. Des. 182 113222Google Scholar

    [27]

    Jirakova K, Kovanda O, Adamek J, Komm M, Seidl J 2019 J. Instrum. 14 C11020Google Scholar

    [28]

    Wang J F, Wu B, Wang J, Hu C D 2013 J. Fusion Energy 33 20Google Scholar

    [29]

    Candy J, Holland C, Waltz R E, Fahey M R, Belli E 2009 Phys. Plasmas 16 060704Google Scholar

    [30]

    Logan N C, Grierson B A, Haskey S R, Smith S P, Meneghini O, Eldon D 2018 Fusion Sci. Technol. 74 125Google Scholar

    [31]

    Dux R, Loarte A, Angioni C, Coster D, Fable E, Kallenbach A 2017 Nucl. Mater. Energy 12 28Google Scholar

    [32]

    Dubuit N, Garbet X, Parisot T, Guirlet R, Bourdelle C 2007 Phys. Plasmas 14 042301Google Scholar

    [33]

    Dux R 2004 Ph. D. Dissertation (Garching: Max Planck Institut für Plasmaphysik

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    Wang Y L, Sang C F, Zhao X L, Wu Y H, Zhou Q R, Zhang Y J, Wang D Z 2023 Nucl. Fusion 63 096024Google Scholar

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    杜海龙, 桑超峰, 王亮, 孙继忠, 刘少承, 汪惠乾, 张凌, 郭后扬, 王德真 2013 物理学报 62 245206Google Scholar

    Du H L, Sang C F, Wang L, Sun J Z, Liu S C, Wang H Q, Zhang L, Guo H Y, Wang D Z 2013 Acta Phys. Sin. 62 245206Google Scholar

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    Wang Y L, Sang C F, Zhang C, Zhao X L, Zhang Y J, Jia G Z, Senichenkov I Y, Wang L, Zhou Q R, Wang D Z 2021 Plasma Phys. Control. Fusion 63 085002Google Scholar

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Publishing process
  • Received Date:  25 March 2025
  • Accepted Date:  19 April 2025
  • Available Online:  29 April 2025
  • Published Online:  05 July 2025
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