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arXiv 2206.04440physics.plasm-ph
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基于神经网络的霍尔效应推力器定标研究

On Scaling of Hall-Effect Thrusters Using Neural Nets

Yegor V. Plyashkov, Andrey A. Shagayda, Dmitrii A. Kravchenko, Fedor D. Ratnikov, Alexander S. Lovtsov

摘要:

霍尔效应推力器(HHHT)广泛用于现代近地航天器的推进,并且对于未来的深空任务至关重要。障碍物的建模方法正在迅速发展。然而,这种方法还不够精确,不能可靠地预测新设计的推进器的参数,主要是由于HET等离子体模拟的巨大计算成本。另一种方法是使用基于可用实验数据的缩放技术。本文提出了一种使用神经网络和其他现代机器学习方法来扩展HET的方法。新的缩放模型建立了从广泛的数据库中的信息HET参数从已发表的论文中收集。新的缩放模型的预测是有效的数据库所涵盖的操作参数域。在设计过程中,该模型可以帮助HET开发人员评估新设计的推力器的性能。在实验研究阶段,该模型可以用来比较所研究的推力器所获得的特性与其他开发人员所获得的水平。与国家的最先进的HET缩放模型的比较。

英文摘要:

Hall-effect thrusters (HETs) are widely used for modern near-earth spacecraft propulsion and are vital for future deep-space missions. Methods of modeling HETs are developing rapidly. However, such methods are not yet precise enough and cannot reliably predict the parameters of a newly designed thruster, mostly due to the enormous computational cost of a HET plasma simulation. Another approach is to use scaling techniques based on available experimental data. This paper proposes an approach for scaling HETs using neural networks and other modern machine learning methods. The new scaling model was built with information from an extensive database of HET parameters collected from published papers. Predictions of the new scaling model are valid for the operating parameters domain covered by the database. During the design, this model can help HET developers estimate the performance of a newly-designed thruster. At the stage of experimental research, the model can be used to compare the achieved characteristics of the studied thruster with the level obtained by other developers. A comparison with the state-of-the-art HET scaling model is also presented.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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