期刊
ENERGIES
卷 15, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/en15093309
关键词
centrifugal pump; performance relationship; support vector regression; particle swarm; performance prediction
资金
- Welfare Technology Applied Research Project of Zhejiang Province [LGG21E090003]
- Postdoctoral Science Foundation of Zhejiang Province [ZJ2021105]
- Fundamental Research Funds for the Provincial Universities of Zhejiang [2021YW72]
This study proposes a multi-condition performance prediction method for centrifugal pumps by introducing performance constraints, which effectively predicts the performance of centrifugal pumps under different operating conditions and is compared with experimental results. The support vector regression model performs better in predicting performance curves.
It is of great significance to predict the energy performance of centrifugal pumps for the improvement of the pump design. However, the complex internal flow always affects the performance prediction of centrifugal pumps, particularly under low-flow operating conditions. Relying on the data-fitting method, a multi-condition performance prediction method for centrifugal pumps is proposed, where the performance relationship is incorporated into the particle swarm optimization algorithm, and the prediction model is optimized by automatically meeting the performance constraints. Compared with the experimental results, the performance under multiple operating conditions is well predicted by introducing performance constraints with the mean absolute relative error (MARE) for the head, power and efficiency of 0.85%, 1.53%,1.15%, respectively. By comparing the extreme gradient boosting and support vector regression models, the support vector regression is more suitable for the prediction of performance curves. Finally, by introducing performance constraints, the proposed model demonstrates a dramatic decrease in the head, power and efficiency of MARE by 98.64%, 82.06%, and 85.33%, respectively, when compared with the BP neural network.
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