基于HBA-SVR混合模型的斜式轴流泵变角性能预测

doi:10.3969/j.issn.1000-1158.2025.02.06

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Abstract To address the difficulty and cost associated with obtaining performance curves for different blade angles， a performance prediction method for slanted axial-flow pump based on the hybrid HBA-SVR model is proposed. The standard bat algorithm is enhanced with directional acceleration and variation strategies to optimize the SVR. The model is trained by using the operating data of the 30° slanted axial-flow pump， and applied to the variable angle performance prediction of the slanted axial-flow pump. The average relative errors for head and efficiency are reduced to 1.49% and 0.41% respectively， with convergence times of 15.47 s and 18.78 s. When compared to the results of standard bat optimization support vector regression prediction， the convergence times are reduced by 122.11% and 103.62% respectively. Moreover， compared to PSO， GA， and BA optimized SVR， the head prediction errors are reduced by 29.53%， 70.46%， and 131.54% respectively， and the efficiency prediction errors are reduced by 7.31%， 9.75%， and 19.51% respectively. The results indicate that the proposed model effectively predicts the variable angle performance of slanted axial-flow pump.

Key words： flow measurement slanted axial-flow pump support vector regression bat algorithm blade placement angle variable angle performance prediction

Received: 18 September 2023 Published: 04 March 2025

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TB937

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	Articles by authors
	ZHENG Haisheng
	ZHOU Peijian
	XIAO Gang
	MOU Jiegang
	XIANG Chun
	QIAN Heng

Cite this article:

ZHENG Haisheng,ZHOU Peijian,XIAO Gang, et al. Variable Angle Performance Prediction of a Slanted Axial-flow Pump Based on HBA-SVR Model[J]. Acta Metrologica Sinica, 2025, 46(2): 190-197.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2025.02.06 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2025/V46/I2/190

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