Reduced model of plasma evolution in hydrogen discharge capillary plasmas,Physical Review E

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Reduced model of plasma evolution in hydrogen discharge capillary plasmas
Physical Review E ( IF 2.2 ) Pub Date : 2021-07-16 , DOI: 10.1103/physreve.104.015211
G J Boyle ₁ , M Thévenet ₁ , J Chappell ₂ , J M Garland ₁ , G Loisch ₁ , J Osterhoff ₁ , R D'Arcy ₁

Affiliation

A model describing the evolution of the average plasma temperature inside a discharge capillary device including Ohmic heating, heat loss to the capillary wall, and ionization and recombination effects is developed. Key to this approach is an analytic quasistatic description of the radial temperature variation which, under local thermal equilibrium conditions, allows the radial behavior of both the plasma temperature and the electron density to be specified directly from the average temperature evolution. In this way, the standard set of coupled partial differential equations for magnetohydrodynamic (MHD) simulations is replaced by a single ordinary differential equation, with a corresponding gain in simplicity and computational efficiency. The on-axis plasma temperature and electron density calculations are benchmarked against existing one-dimensional MHD simulations for hydrogen plasmas under a range of discharge conditions and initial gas pressures, and good agreement is demonstrated. The success of this simple model indicates that it can serve as a quick and easy tool for evaluating the plasma conditions in discharge capillary devices, particularly for computationally expensive applications such as simulating long-term plasma evolution, performing detailed input parameter scans, or for optimization using machine-learning techniques.

中文翻译：

氢放电毛细管等离子体中等离子体演化的简化模型

开发了描述放电毛细管装置内平均等离子体温度演变的模型，包括欧姆加热、毛细管壁的热损失以及电离和复合效应。这种方法的关键是对径向温度变化的分析准静态描述，在局部热平衡条件下，允许直接从平均温度演化中指定等离子体温度和电子密度的径向行为。通过这种方式，用于磁流体动力学 (MHD) 模拟的标准耦合偏微分方程组被单个常微分方程所取代，从而在简单性和计算效率方面获得了相应的收益。在一系列放电条件和初始气压下，轴上等离子体温度和电子密度计算以现有的氢等离子体一维 MHD 模拟为基准，并证明了良好的一致性。这个简单模型的成功表明它可以作为评估放电毛细管装置中等离子体条件的快速简便工具，特别是对于计算成本高昂的应用，例如模拟长期等离子体演化、执行详细的输入参数扫描或优化使用机器学习技术。

更新日期：2021-07-16

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