Parameter optimization of rectification sill in the forebay of pumping station using BPNN-GA algorithm

YU Yonghai; YAN Haodi; YE Changliang

doi:10.11975/j.issn.1002-6819.202303168

Transactions of the Chinese Society of Agricultural Engineering > 2023 > 39(14): 106-113. > DOI: 10.11975/j.issn.1002-6819.202303168

YU Yonghai, YAN Haodi, YE Changliang. Parameter optimization of rectification sill in the forebay of pumping station using BPNN-GA algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(14): 106-113. DOI: 10.11975/j.issn.1002-6819.202303168

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Parameter optimization of rectification sill in the forebay of pumping station using BPNN-GA algorithm

1.
College of Agricultural Science And Engineering , Hohai University, Nanjing 210098, China
2.
College of Energy And Electrical Engineering, Hohai University, Nanjing 210098, China

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Received Date: March 23, 2023
Revised Date: June 21, 2023
Available Online: August 10, 2023

Graphical Abstract

Abstract

Abstract

In the field of hydraulic engineering, the design of pumping stations played an essential role in ensuring the efficient and reliable operation of water supply systems. However, due to various factors such as asymmetric pump configurations and non-straight alignment of the forebay and inlet pool center lines, the flow patterns in the forebay could be poor, which could lead to problems such as cavitation and reduced pump efficiency. In order to improve the unfavorable flow patterns within the pump station's inlet structure and enhance the efficiency of the pump operation, this study focuses on optimizing the structural design parameters of the forebay's sill using computational fluid dynamics and the BPNN-GA (back propagation neural network-genetic algorithm) model. To facilitate the genetic algorithm's fitness calculation, a comprehensive evaluation index F for the flow patterns in the forebay is proposed based on the uniformity of axial velocity distribution and velocity-weighted average angle. By taking the comprehensive evaluation index F as the objective parameter, the BPNN is optimized using the genetic algorithm, leading to the determination of the optimal sill design parameters, which are then compared and analyzed against the numerically simulated structures from the orthogonal experimental design.The research findings indicate that, under the operation of pumps No.1, No. 2, and No.4, compared to the optimal design from the orthogonal experimental design, the uniformity of axial velocity distribution of No.1 pump inlet flow increased by 16.58 percentage points, and the velocity-weighted average angle increased by 4.66°. For No.2 pump, the uniformity of axial velocity distribution increased by 0.49 percentage points, while the velocity-weighted average angle decreased by 2.81°. As for No.4 pump, the uniformity of axial velocity distribution improved by 8 percentage points, and the velocity-weighted average angle increased by 7.81°, resulting in a comprehensive evaluation index F of 1.16, indicating a significant improvement in the flow patterns of the forebay.The optimization of the pump station's forebay sill parameters using the BPNN-GA algorithm overcomes the drawback of traditional methods being trapped in local optima. It enables the identification of the optimal sill design parameters for the uniformity of axial velocity distribution and velocity-weighted average angle within the required design range, providing reference for the application of computational intelligence in optimizing hydraulic design in pump stations.
- pumping station,
- numerical simulation,
- forebay,
- rectification sill,
- BP neural network,
- genetic algorithm

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References (25)

References

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