Optimizing baffle plates in the forebay of lateral inlet pump station using backpropagation neural network-genetic algorithm
YAN Haodi
YU Yonghai
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.
Keywords：pump stationlateral inflowBP neural networkgenetic algorithmrectification baffle plate
Publication Date：2024-10-28
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：8( 76-83 )