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arXiv 2403.06809physics.plasm-ph
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等离子体空间推进中振荡现象的数据驱动稀疏建模

Data-driven sparse modeling of oscillations in plasma space propulsion

B. Bayón-Buján, M. Merino

AI总结:

本文提出基于稀疏回归和Pareto前沿分析的算法,用于获取等离子体空间推进系统中振荡现象的数据驱动模型,并在Hall效应推进器中验证了其有效性与物理可解释性。

摘要:

本文提出了一种基于稀疏回归(SINDy)和Pareto前沿分析的算法,用于获取等离子体空间推进系统中振荡现象的数据驱动模型。该算法能够纳入物理约束,利用数据自助法增强鲁棒性,并针对不同指标进行微调。文中讨论并比较了标准、弱形式和积分SINDy公式。该方案在Hall效应推进器呼吸模式振荡案例中进行了基准测试,使用了PIC/流体模拟数据。针对平均等离子体属性获得了不同复杂度的模型,并表明其具有明确的物理可解释性,且与文献中现有的0D模型相一致。最后,该算法还能识别具有定性不同等离子体动力学的物理子域,为更高级的建模方法提供了有价值的信息。

英文摘要:

An algorithm to obtain data-driven models of oscillatory phenomena in plasma space propulsion systems is presented, based on sparse regression (SINDy) and Pareto front analysis. The algorithm can incorporate physical constraints, use data bootstrapping for additional robustness, and fine-tuning to different metrics. Standard, weak and integral SINDy formulations are discussed and compared. The scheme is benchmarked in the case of breathing-mode oscillations in Hall effect thrusters, using PIC/fluid simulation data. Models of varying complexity are obtained for the average plasma properties, and shown to have a clear physical interpretability and agreement with existing 0D models in the literature. Lastly, the algorithm applied is also shown to enable the identification of physical subdomains with qualitatively different plasma dynamics, providing valuable information for more advanced modeling approaches.

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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