弹道电子的出生势多群流体模型：击穿与阴极鞘层区域
Birth-Potential Multigroup Fluid Model for Ballistic Electrons: Breakdown and Cathode Sheath Regimes
- University of Arizona（亚利桑那大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对电子逃逸主导的放电，提出基于出生势的弹道电子多群流体模型，以少量群实现近动力学精度，准确重现帕邢曲线左支和阴极鞘层电流-电压特性。
AI中文摘要：
流体模型在电子逃逸占主导地位的放电中失效。在帕邢曲线左支和高压阴极鞘层中，电子群体分裂为热体部分和弹道束，单一电子流体无法表示。我们提出了一种多群流体模型，将弹道电子按出生势分组：即它们被释放时的静电势，并以其携带的前向动能进行偏移。由于出生势是弹道运动的不变量，电场永远不会使电子在群间移动，只有碰撞才会。每个群只需要一个连续性方程，因为其速度由局部势代数确定，群的数量仅由能量分辨率决定，与网格无关。基于局部场的逃逸准则将每个新释放的电子分配到体部分或弹道群，因此该模型即使在没有鞘层的情况下也适用。在氩气中与动力学求解器进行基准测试，它重现了帕邢曲线左支——局部场和局部能量模型遗漏或错置的部分——并在约化场超过10^4 Td时，阴极鞘层的电流-电压特性跟踪误差在百分之几以内，而局部能量模型高估电流达三十倍。由于根据场强，仅需10至30个群即可，该方法以增加这么多额外物种为代价，为多物种计算流体动力学求解器带来了接近动力学的精度，而无需增加速度空间维度或粒子噪声。
英文摘要：
Fluid models of discharges fail where electron runaway dominates. On the left branch of the Paschen curve and in high-voltage cathode sheaths, the electron population splits into a thermal bulk and a ballistic beam that no single electron fluid can represent. We present a multigroup fluid model that divides ballistic electrons into groups indexed by their birth potential: the electrostatic potential at which they were liberated, offset by the forward kinetic energy they carried. Because the birth potential is a constant of ballistic motion, the electric field never moves electrons between groups and only collisions do. Each group requires a single continuity equation because its velocity follows algebraically from the local potential, and the number of groups is set by the energy resolution alone independently of the mesh. A runaway criterion based on the local field assigns each newly liberated electron to the bulk or a ballistic group, so the model applies even where no sheath exists. Benchmarked against a kinetic solver in argon, it reproduces the left branch of the Paschen curve---which local-field and local-energy models miss or misplace---and tracks the current--voltage characteristic of a cathode sheath to within a few percent at reduced fields exceeding $10^4$ Td, where the local-energy model overpredicts the current up to thirtyfold. Since as few as 10 to 30 groups suffice, depending on the field, the approach brings near-kinetic accuracy to multi-species computational fluid dynamics solvers at the cost of that many additional species, without added velocity-space dimensions or particle noise.
