Impeller geometry optimization using a machine learning-based algorithm with physics embedded dynamic sampling method for a high-speed miniature pump

This paper proposes an optimization approach leveraging machine learning, integrated with physics-informed dynamic sampling, to improve the hydraulic efficiency of highspeed pumps utilized in aerospace. These pumps are difficult to optimize due to their sensitivity to various impeller geometrical pa...

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Vydané v:	Journal of mechanical science and technology Ročník 39; číslo 7; s. 4043 - 4065
Hlavní autori:	Song, Xueyi, Zheng, Kexin, Luo, Xianwu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Seoul Korean Society of Mechanical Engineers 01.07.2025 Springer Nature B.V 대한기계학회
Predmet:	Algorithms Control Dynamical Systems Efficiency Engineering Flow separation High speed Hydraulics Impellers Industrial and Production Engineering Machine learning Mechanical Engineering Optimization Original Article Parameter sensitivity Physics Pumps Sampling methods Suction Uniform flow Vibration Vorticity 기계공학 Machine learning-based algorithm Physics embedded dynamic sampling method High-speed miniature pump Optimization
ISSN:	1738-494X, 1976-3824
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