水力发电学报2026，Vol.45Issue(2)：31-45,15.DOI:10.11660/slfdxb.20260203
大型闸室泄洪流固耦合场预测重构研究及其数值分析
Study on predictive reconstruction and numerical simulations of fluid-structure interaction fields in large-scale sluice chambers
摘要
Abstract
To examine the interaction mechanism between flood discharge and a sluice chamber,a novel method is developed coupling fluid-structure interaction(FSI)Finite Element analysis with a Back Propagation Neural Network,based on stress-strain characteristics,and applied to the Datengxia water control hub project.This method facilitates the development of a digital twin based on numerical simulation data.We construct a finite element model of COMSOL for flood discharge and sluice chamber structure,and simulate five flood discharge scenarios of 23400 m3/s,30600 m3/s,39000 m3/s,42300 m3/s,and 66200 m3/s.Then,we examine the FSI process of the sluice chamber and its corresponding load patterns.A total of 1250 monitoring points are arranged throughout the sluice chamber and the flow domain.The time-sequence data for four hydraulic parameters are extracted at a 15-second interval-flow velocity(u),pressure(p),turbulence intensity(I),and vorticity(ω).And,stress and displacement data are simultaneously collected from the sluice chamber,so that training datasets for the BP Neural Network(BPNN)can be constructed.Finally,we develop a BPNN model for predictions of the sluice chamber's stress and displacement,using spatial coordinates and hydraulic parameters as inputs,and train and validate it.Results show a high predictive accuracy of this FSI collaborative BPNN method-the coefficient of determination(R2)reaches up to 0.975 for stress and 0.987 for displacement.Specifically,96.0%of the stress predictions have an error below 10%with the maximum absolute error of 0.097 MPa;99.1%of the predicted displacements have an error below 10%with the maximum absolute error of 0.395 mm,or significantly below the allowable deformation threshold of 0.45 mm for chamber joints.This study verifies the feasibility of our new method,the reliability of BPNN in predicting stress and displacement in the sluice chamber,and the advantage of methodology.关键词
泄洪闸室/流-固耦合有限元/BP神经网络/模拟预测/流场重构Key words
sluice chambers/fluid-structure interaction finite element analysis/BP neural network/simulation-based prediction/flow field reconstruction分类
建筑与水利引用本文复制引用
龚成勇,翁伟涛,王银莹,陈诗明,郭新玉..大型闸室泄洪流固耦合场预测重构研究及其数值分析[J].水力发电学报,2026,45(2):31-45,15.基金项目
科技部创新方法工作专项(2020IM030400) （2020IM030400）
甘肃省水利科学试验研究与技术推广计划项目(24GSLK019 （）
24GSLK024) （）
