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Article

A Novel Energy Performance Prediction Approach towards Parametric Modeling of a Centrifugal Pump in the Design Process

by
Lingbo Nan
1,2,
Yumeng Wang
1,2,
Diyi Chen
1,2,*,
Weining Huang
3,
Zuchao Zhu
4 and
Fusheng Liu
5
1
Institute of Water Resources and Hydropower Research, Northwest A&F University, Yangling, Xianyang 712100, China
2
Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling, Xianyang 712100, China
3
Kaiquan Group Co., Ltd., Wenzhou 325000, China
4
Key Laboratory of Fluid Transmission Technology of Zhejiang Province, Zhejiang Sci-Tech University, Hangzhou 310000, China
5
Hanjiang-to-Weihe River Valley Water Diversion Project Construction Co., Ltd., Xi’an 710024, China
*
Author to whom correspondence should be addressed.
Water 2023, 15(10), 1951; https://doi.org/10.3390/w15101951
Submission received: 11 April 2023 / Revised: 17 May 2023 / Accepted: 19 May 2023 / Published: 21 May 2023
(This article belongs to the Section Hydraulics and Hydrodynamics)
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Abstract

Traditional centrifugal pump performance prediction (CPPP) employs the semi-theoretical and semi-empirical approaches; however, it can lead to many prediction errors. Considering the superiority of deep learning when applied to nonlinear systems, in this paper, a method combining hydraulic loss and convolutional neural network (HLCNN) is applied to CPPP. Head and efficiency were selected as two variables for demonstrating the energy performance of the centrifugal pump in order to reflect the prediction ability of the proposed model. The evaluation results indicate that the predicted head and efficiency are accurate, compared with the experimental results. Furthermore, the HLCNN prediction model was compared against machine learning methods and the computational fluid dynamic method. The proposed HLCNN model obtained a better AREmean, root mean square error, sum of squares due to error, and mean absolute error for centrifugal pump energy performance. The research revealed that the HLCNN model achieves accurate energy performance prediction in the design of centrifugal pumps, reducing the development time and costs.
Keywords: centrifugal pump; energy performance; hydraulic loss; convolution neural network centrifugal pump; energy performance; hydraulic loss; convolution neural network

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MDPI and ACS Style

Nan, L.; Wang, Y.; Chen, D.; Huang, W.; Zhu, Z.; Liu, F. A Novel Energy Performance Prediction Approach towards Parametric Modeling of a Centrifugal Pump in the Design Process. Water 2023, 15, 1951. https://doi.org/10.3390/w15101951

AMA Style

Nan L, Wang Y, Chen D, Huang W, Zhu Z, Liu F. A Novel Energy Performance Prediction Approach towards Parametric Modeling of a Centrifugal Pump in the Design Process. Water. 2023; 15(10):1951. https://doi.org/10.3390/w15101951

Chicago/Turabian Style

Nan, Lingbo, Yumeng Wang, Diyi Chen, Weining Huang, Zuchao Zhu, and Fusheng Liu. 2023. "A Novel Energy Performance Prediction Approach towards Parametric Modeling of a Centrifugal Pump in the Design Process" Water 15, no. 10: 1951. https://doi.org/10.3390/w15101951

APA Style

Nan, L., Wang, Y., Chen, D., Huang, W., Zhu, Z., & Liu, F. (2023). A Novel Energy Performance Prediction Approach towards Parametric Modeling of a Centrifugal Pump in the Design Process. Water, 15(10), 1951. https://doi.org/10.3390/w15101951

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