零方程输运模型在霍尔效应推力器预测模型中的自洽实现

T. Marks, A. L. Ortega, I. Mikellides, B. Jorns
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引用次数: 4

摘要

研究了轴对称多流体霍尔推力器代码的性能,该代码包含一个自一致的、数据驱动的闭合模型,用于异常电子输运。利用喷气推进实验室的Hall2De模拟了H9磁屏蔽霍尔推进器的五种不同工作状态。为了捕获与封闭模型相关的固有不确定性,对每种情况运行O(100)个模拟,每个模拟使用从概率分布中随机抽样的系数集。这些模拟结果提供了推进器性能数量的概率预测,包括推力、放电电流、几个组件效率和中心线等离子体特性。发现该模型在所有条件下都能得到收敛解,具有较大的10 khz范围振荡,并且随着电压和流量的变化,性能趋势与实验相似。与相同放电电压和质量流量下的实验相比,该模型对推力的预测平均偏低15-25%,对放电电流的预测平均过高20%。这种性能差异是由于加速区霍尔参数较高,导致离子加速区持续时间较长,导致束流利用率、质量利用率和发散效率低于实验。该结果背后的物理过程将在未来数据驱动的建模工作中进行讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Self-consistent implementation of a zero-equation transport model into a predictive model for a Hall effect thruster
The performance of an axisymmetric multi-fluid Hall thruster code that incorporates a self-consistent, data-driven closure model for the anomalous electron transport is investigated. Five different operating conditions of the H9 magnetically shielded Hall thruster are simulated with the Jet Propulsion Laboratory’s Hall2De. In order to capture the inherent uncertainty associated with the closure model, O(100) simulations are run for each condition, each of using a coefficient set sampled randomly from a probability distribution. The results of these simulations provide probabilistic predictions of thruster performance quantities including thrust, and discharge current, as well as several component efficiencies and centerline plasma properties. The model is found to yield converged solutions at all conditions, with large 10 kHzrange oscillations and performance trends with voltage and flow rate similar to experiment. Themodel under-predicts the thrust by 15-25%and over-predicts the discharge current by 20% on average compared to experiments at the same discharge voltage and mass flow rate. This performance discrepancy is due to lower beam utilization, mass utilization, and divergence efficiency than experiment, resulting from high Hall parameters in the acceleration region, which lead to a protracted ion acceleration region. The physical processes underlying this result are discussed in the context of future data-driven modeling efforts.
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