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Volume 36, Issue 6
June 2024
Research Article| June 17 2024
Xianwei Liu (刘显为)
;
Xianwei Liu (刘显为)
(Conceptualization, Methodology, Writing – original draft)
1
School of Power and Energy, Northwestern Polytechnical University
, Xi'an 710129,
China
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Shijie Zhong (仲世杰)
;
Shijie Zhong (仲世杰)
(Writing – original draft)
1
School of Power and Energy, Northwestern Polytechnical University
, Xi'an 710129,
China
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Xuebo Zheng (郑学波)
;
Xuebo Zheng (郑学波)
(Writing – review & editing)
2
School of Energy and Electrical Engineering, Chang'an University
, Xi'an 710149,
China
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Jiangfeng Fu (符江锋)
Jiangfeng Fu (符江锋) a)
(Funding acquisition, Writing – review & editing)
1
School of Power and Energy, Northwestern Polytechnical University
, Xi'an 710129,
China
3
Advanced Power Research Institute of Northwestern Polytechnical University in Sichuan Tianfu New Area
, Chengdu 610213,
China
a)Author to whom correspondence should be addressed: fjf@nwpu.edu.cn. URL: https://teacher.nwpu.edu.cn/fujiangfeng
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Author & Article Information
a)Author to whom correspondence should be addressed: fjf@nwpu.edu.cn. URL: https://teacher.nwpu.edu.cn/fujiangfeng
Physics of Fluids 36, 067120 (2024)
Article history
Received:
March 28 2024
Accepted:
May 31 2024
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Citation
Xianwei Liu, Shijie Zhong, Xuebo Zheng, Jiangfeng Fu; Research on uncertainties in fuel centrifugal pump based on prediction and reconstruction of internal flow field. Physics of Fluids 1 June 2024; 36 (6): 067120. https://doi.org/10.1063/5.0211010
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Geometric machining errors in the blade profile and variable operating conditions in the extreme operating environment are primary factors leading to the uncertainties in pump performance. This paper presents an analysis of uncertainties of fuel centrifugal pumps by modeling the geometry uncertainty in blade machining based on the Karhunen–Loève (KL) expansion and using a polynomial chaos expansion (PCE) model. First, the geometric uncertainty in the blade machining is described by the KL expansion in three sections and a stochastic simulation of the blade geometry is performed. Then, a PCE surrogate model is trained based on the least angle regression method and validated by the bootstrap method to quantify the uncertainties of performance indices. Finally, the influence mechanism and relative importance of each input uncertainty parameter are investigated using a quasi-Monte Carlo simulation method. The results show that the KL expansion of the blade profile uses the random vector perturbation superposition of three stream surface, achieving the dimensional reduction in the blade machining error. The PCE surrogate model, trained with a dataset of 3 × 106 sample points, exhibits excellent fit, and the R-squared and adjusted R-squared for head coefficient and efficiency are both above 80%. The variance of parameter control points of the reconstructed flow field is less than 0.002. The uncertainties in both operating conditions and parameters have an influence on the distribution of the global flow field, while the influence of the uncertainty in machining error on the global flow field mainly concentrates on the power-generating positions of the blade.
Topics
Fuels, Data science, Machining, Computer simulation, Aerodynamics, Flow rate measurement, Monte Carlo methods, Statistical analysis, Stochastic processes
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