A hierarchical method for verification of particle-in-cell/ Monte Carlo collision modelling on plasma discharges
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摘要: 当前,科学计算的验证主要针对基于确定性偏微分方程组的网格离散方法。放电等离子体的粒子云网格PIC方法作为一种粒子-网格耦合的仿真手段,其验证方法具有显著不同的特点:第一,PIC仿真除了在时间和空间上进行离散,还需要对粒子数权重进行离散;第二,离散粒子的相空间分布函数是否适合作为验证研究的观测量;第三,粒子-网格耦合过程中的电场插值和电荷分配会影响PIC仿真的全局收敛精度。另外,当PIC方法与蒙特卡罗(MC)方法耦合时,离散误差和随机误差通常叠加在一起,理查德森外推需要结合系综平均进行。提出了一种分层级验证的方法。首先对单粒子轨道、电磁场求解、二体粒子碰撞进行收敛精度阶测试;然后采用空间电荷限制流、气体的傅里叶流动等具有精确解的经典物理模型分别对集成PIC、MC模块进行离散误差评估;最后采用放电物理过程对程序功能进行基准校验。Abstract: The verification of scientific computing currently places a strong emphasis on grid discretization methods for systems of deterministic partial differential equations. However, verifying particle-in-cell (PIC) simulations, which employ a particle-mesh method to model discharging plasmas, presents distinctive challenges. Firstly, PIC simulations require discretization not only in time and space but also in macro-particle weights. Secondly, challenges arise regarding the utilization of the discretized particle phase space distribution function for verification purposes. Thirdly, the interpolation of electric fields and charge distribution can significantly impact the overall accuracy of PIC convergence.When PIC methods are integrated with Monte Carlo (MC) methods, discretization and stochastic errors often combine, necessitating Richardson extrapolation in conjunction with ensemble averaging.To tackle these challenges, this paper introduces a hierarchical verification approach. It commences with order-of-accuracy tests for individual particle trajectories, electromagnetic field solvers, and binary-particle collisions. Discretization errors for integrated PIC and MC modules are then evaluated using classical physical models that possess exact solutions, such as space-charge-limited current and Fourier flow in gases. Finally, a code-to-code comparison is performed with benchmark examples of simplified discharge simulations.
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Key words:
- PIC method /
- verification techniques /
- order of accuracy test /
- discharge simulation
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表 1 粒子模拟验证的第二层级功能验证
Table 1. Second-level functional verification of particle simulation validation
function typical case particle push particle collision field solvers gather and scatter particle boundaries DSMC Fourier heat flow × × O O reflective boundary MCC charged particle transport × × O O O PIC space charge limited current × O × × absorptive boundary 表 2 带电粒子在等离子体中的输运
Table 2. Transport of charged particles in a plasma
physical problem typical case collision type transport of electrons in weakly
ionized plasmasMaxwell gas model electron-heavy particle elastic collisions model/hard sphere gas electron-heavy particle elastic + excitation Lucas-Salee model electron-heavy particle elastic + excitation + ionization Ness-Robson model electron-heavy particle elastic + excitation + ionization + attachment ion transport in weakly
ionized plasmaion velocity distribution in an
alternating electric fieldelastic collisions between ions and heavy particles transport in fully ionized plasma Spitzer conductivity Coulomb collisions between electrons and ions -
[1] Mehta U B. Credible computational fluid dynamics simulations[J]. AIAA Journal, 1998, 36(5): 665-667. doi: 10.2514/2.431 [2] Oberkampf W L, Trucano T G. Verification and validation benchmarks[J]. Nuclear Engineering and Design, 2008, 238(3): 716-743. doi: 10.1016/j.nucengdes.2007.02.032 [3] Turner M M. Verification of particle-in-cell simulations with Monte Carlo collisions[J]. Plasma Sources Science and Technology, 2016, 25: 054007. doi: 10.1088/0963-0252/25/5/054007 [4] Riva F, Beadle C F, Ricci P. A methodology for the rigorous verification of Particle-in-Cell simulations[J]. Physics of Plasmas, 2017, 24: 055703. doi: 10.1063/1.4977917 [5] Fierro A, Barnat E, Hopkins M, et al. Challenges and opportunities in verification and validation of low temperature plasma simulations and experiments[J]. The European Physical Journal D, 2021, 75: 151. doi: 10.1140/epjd/s10053-021-00088-6 [6] Tranquilli P, Ricketson L, Chacón L. A deterministic verification strategy for electrostatic particle-in-cell algorithms in arbitrary spatial dimensions using the method of manufactured solutions[J]. Journal of Computational Physics, 2022, 448: 110751. doi: 10.1016/j.jcp.2021.110751 [7] DeChant C, Icenhour C, Keniley S, et al. Verification methods for drift-diffusion reaction models for plasma simulations[J]. Plasma Sources Science and Technology, 2023, 32: 044006. doi: 10.1088/1361-6595/acce65 [8] O’Connor S, Crawford Z D, Verboncoeur J P, et al. A set of benchmark tests for validation of 3-D particle in cell methods[J]. IEEE Transactions on Plasma Science, 2021, 49(5): 1724-1731. doi: 10.1109/TPS.2021.3072353 [9] Xiao Jianyuan, Qin Hong, Liu Jian, et al. Explicit high-order non-canonical symplectic particle-in-cell algorithms for Vlasov-Maxwell systems[J]. Physics of Plasmas, 2015, 22: 112504. doi: 10.1063/1.4935904 [10] Smith J R, Orban C, Rahman N, et al. A particle-in-cell code comparison for ion acceleration: EPOCH, LSP, and WarpX[J]. Physics of Plasmas, 2021, 28: 074505. doi: 10.1063/5.0053109 [11] Bagheri B, Teunissen J, Ebert U, et al. Comparison of six simulation codes for positive streamers in air[J]. Plasma Sources Science and Technology, 2018, 27: 095002. doi: 10.1088/1361-6595/aad768 [12] Bravenec R V, Chen Y, Candy J, et al. A verification of the gyrokinetic microstability codes GEM, GYRO, and GS2[J]. Physics of Plasmas, 2013, 20: 104506. doi: 10.1063/1.4826511 [13] Turner M M, Derzsi A, Donkó Z, et al. Simulation benchmarks for low-pressure plasmas: Capacitive discharges[J]. Physics of Plasmas, 2013, 20: 013507. doi: 10.1063/1.4775084 [14] Charoy T, Boeuf J P, Bourdon A, et al. 2D axial-azimuthal particle-in-cell benchmark for low-temperature partially magnetized plasmas[J]. Plasma Sources Science and Technology, 2019, 28: 105010. doi: 10.1088/1361-6595/ab46c5 [15] Radtke G A, Martin N, Moore C H, et al. Robust verification of stochastic simulation codes[J]. Journal of Computational Physics, 2022, 451: 110855. doi: 10.1016/j.jcp.2021.110855 [16] Taccogna F. Monte Carlo collision method for low temperature plasma simulation[J]. Journal of Plasma Physics, 2014, 81: 305810102. [17] Timko H, Crozier P S, Hopkins M M, et al. Why perform code-to-code comparisons: A vacuum arc discharge simulation case study[J]. Contributions to Plasma Physics, 2012, 52(4): 295-308. doi: 10.1002/ctpp.201100051 [18] Carlsson J, Khrabrov A, Kaganovich I, et al. Validation and benchmarking of two particle-in-cell codes for a glow discharge[J]. Plasma Sources Science and Technology, 2016, 26: 014003. doi: 10.1088/0963-0252/26/1/014003 [19] Gonzalez-Herrero D, Micera A, Boella E, et al. ECsim-CYL: Energy Conserving Semi-Implicit particle in cell simulation in axially symmetric cylindrical coordinates[J]. Computer Physics Communications, 2019, 236: 153-163. doi: 10.1016/j.cpc.2018.10.026 [20] Krook M, Wu T T. Formation of Maxwellian tails[J]. Physical Review Letters, 1976, 36(19): 1107-1109. doi: 10.1103/PhysRevLett.36.1107 [21] Bobylev A V. Proceedings of the USSR Academy of Sciences, 1975, 225: 1041. [22] Krook M, Wu T T. Exact solution of Boltzmann equations for multicomponent systems[J]. Physical Review Letters, 1977, 38(18): 991-993. doi: 10.1103/PhysRevLett.38.991 [23] Myong R S, Karchani A, Ejtehadi O. A review and perspective on a convergence analysis of the direct simulation Monte Carlo and solution verification[J]. Physics of Fluids, 2019, 31: 066101. doi: 10.1063/1.5093746 [24] Rader D J, Gallis M A, Torczynski J R, et al. Direct simulation Monte Carlo convergence behavior of the hard-sphere-gas thermal conductivity for Fourier heat flow[J]. Physics of Fluids, 2006, 18: 077102. doi: 10.1063/1.2213640 [25] Chapman S, Cowling T G. The mathematical theory of non-uniform gases[M]. Cambridge: Cambridge University Press, 1935. [26] Reid I D. An investigation of the accuracy of numerical solutions of Boltzmann’s equation for electron swarms in gases with large inelastic cross sections[J]. Australian Journal of Physics, 1979, 32(3): 231-254. doi: 10.1071/PH790231 [27] Lucas J, Saelee H T. A comparison of a Monte Carlo simulation and the Boltzmann solution for electron swarm motion in gases[J]. Journal of Physics D:Applied Physics, 1975, 8(6): 640-650. doi: 10.1088/0022-3727/8/6/007 [28] Ness K F, Robson R E. Velocity distribution function and transport coefficients of electron swarms in gases. II. Moment equations and applications[J]. Physical Review A, 1986, 34(3): 2185-2209. doi: 10.1103/PhysRevA.34.2185 [29] Kumar K. Swarms in periodically time dependent electric fields[J]. Australian Journal of Physics, 1995, 48(3): 365-376. doi: 10.1071/PH950365 [30] Spitzer L Jr, Härm R. Transport phenomena in a completely ionized gas[J]. Physical Review, 1953, 89(5): 977-981. doi: 10.1103/PhysRev.89.977 [31] Kovalev V F, Bychenkov V Y. Analytic solutions to the vlasov equations for expanding plasmas[J]. Physical Review Letters, 2003, 90: 185004. doi: 10.1103/PhysRevLett.90.185004 [32] Kilian P, Muñoz P A, Cedric Schreiner, et al. Plasma waves as a benchmark problem[J]. Journal of Plasma Physics, 2017, 83: 707830101. doi: 10.1017/S0022377817000149 [33] Lau Y Y. Simple theory for the two-dimensional Child-Langmuir law[J]. Physical Review Letters, 2001, 87: 278301. doi: 10.1103/PhysRevLett.87.278301 [34] Zhu Y S, Zhang P, Valfells A, et al. Novel scaling laws for the Langmuir-Blodgett solutions in cylindrical and spherical diodes[J]. Physical Review Letters, 2013, 110: 265007. doi: 10.1103/PhysRevLett.110.265007 [35] Caflisch R E, Rosin M S. Beyond the Child-Langmuir limit[J]. Physical Review E, 2012, 85: 056408. doi: 10.1103/PhysRevE.85.056408 [36] Rokhlenko A. Child-Langmuir flow with periodically varying anode voltage[J]. Physics of Plasmas, 2015, 22: 022126. doi: 10.1063/1.4913351