Optimizing baffle plates in the forebay of lateral inlet pump station using backpropagation neural network-genetic algorithm
Year: 2024
Type: article
Abstract: 【Objective】 The baffle plates in the forebay of lateral inlet pump stations are critical components to control water flow and optimize the performance of the pump station. We proposed a method to optimize their design. 【Method】 We used computational fluid dynamics (CFD) combined with the backpropagation neural network-genetic algorithm (BPNN-GA) to optimize the design parameters of the rectification baffle plates in the forebay. The optimization process used a comprehensive evaluation index, F, which calculated the fitness of the genetic algorithm based on the uniformity of the axial flow velocity distribution and the velocity-weighted average angle. The BPNN model was fine-tuned, with the comprehensive index F as the optimization objective, leading to the identification of optimal design parameters for the rectification baffle plates. 【Result】 The optimization results showed that the rectification baffle plates designed by the BPNN-GA algorithm significantly improved the flow pattern within the inlet channel. Notable improvements included increased uniformity in axial velocity distribution and a better velocity-weighted average angle. Additionally, the improved design also significantly reduced the vortex area within the forebay, with the comprehensive evaluation index F showing a 6.31 reduction. 【Conclusion】 The proposed BPNN-GA algorithm for optimizing the design parameters of the rectification baffle plates effectively ameliorated the undesirable flow conditions in the forebay. It provides a valuable method for improving the design of similar hydraulic structures.
Authors: YAN Haodi, YU Yonghai
Institution: Hohai University
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6923052675
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