Calculation of flow capacity of arc gate based on stable water conveyance state identification
Control gates play a crucial role in managing water levels and flow rates in water transfer canal systems.Arc gates are particularly favored due to their hydraulic efficiency and lightweight construction.Accurate flow rate calculation through these gates is essential for hydraulic simulation models and water management decisions.However,traditional empirical formulas face challenges due to the complex nature of arc gates,leading to the proposal of dimensionless analysis-based approaches.Combined with emerging technologies like artificial intelligence,these approaches improve adaptability and flow calculation accuracy.Yet,challenges persist,such as the need for representative data for parameter calibration and the impact of factors like equipment failures and dispatch instruction operations on monitoring data accuracy.In digital twin basin construction,accurately characterizing gate flow characteristics is crucial for effective water management.Therefore,identifying stable water delivery states and obtaining representative hydrological data are essential steps for analyzing gate flow coefficients and ensuring accurate flow rate calculations,ultimately supporting real-time monitoring and decision-making in water transfer projects.A stable water conveyance state identification method was introduced to accurately characterize stable water delivery states and select representative data for gate parameter calibration in digital twin basin construction.Leveraging dimensionless analysis,it contrasts flow rate calculation accuracy between monitoring and stable state data,validating the method's effectiveness.The aim is to provide scientific basis and technical support for precise gate flow capacity depiction and real-time gate state synchronization in water transfer projects.The methodology involves deriving discharge formulas,stable state identification,and dimensionless analysis.Threshold values for discharge coefficient change and cumulative change are determined by selecting stable state data from historical monitoring data.The dimensionless analysis method establishes a mathematical model for gate flow calculation.Additionally,the dimensionless analysis method establishes a mathematical model for gate flow calculation.Evaluation criteria,including R2,ERMS,EMA,EMAP,and ENS,assess method accuracy and performance.This comprehensive approach ensures reliable gate parameter calibration and enhances the robustness of water management decisions in open channel water transfer systems.The study examines three control gates from different South-to-North Water Transfers Project segments:Jinshui River Control Gate,Qi River Control Gate,and Qili River Control Gate.Using one year data from July 2022 to July 2023,at 2-hour intervals,stable state identification involved normality testing of comprehensive flow coefficient changes,revealing a bell-shaped distribution for three gates.Thresholds,based on a 95％confidence interval and a 4-hour cumulative change duration,identified stable water conveyance states.Specific thresholds were set for change values and cumulative changes at each gate,ensuring reliable data for water transfer management decisions.Stable state data showed greater representatives,utilizing stable state data identified through dimensionless analysis,the determination coefficients of the comprehensive flow coefficients for the Jinshui River Control Gate,Qi River Control Gate,and Qili Control River Control Gate were all improved compared to original monitoring data.Additionally,the root mean square error(ERMS)significantly decreased,with reductions of 43％,47％,and 29％,respectively.Moreover,the accuracy of flow rate calculations using stable state data surpassed that of original monitoring data,reducing the average relative errors for the Jinshui River Control Gate,Qi River Control Gate,and Qili River Control Gate from 7.26％,3.35％,and 3.80％to 6.55％,3.22％,and 2.19％,respectively.Significant insights emerge when comparing results derived from original monitoring data and stable state-identified data.First,parameter calibration utilizing stable state-identified data enhances the determination coefficient of the comprehensive flow coefficient for all three gates,leading to notable reductions in root mean square error(ERMS).Second,the precision of flow calculations improves when using stable state data,resulting in decreased average relative errors in flow for each gate.Third,the proposed stable water conveyance state identification method enables the extraction of representative datasets for different scheduling conditions,and offering robust support for high-precision water transfer scheduling simulations and canal hydraulic capacity analyses.In conclusion,this method demonstrates promising applicability and potential for widespread adoption in practice.
open channel water transfer projectdigital twinstable water conveyance statearc gatedischarge calculationdimensional analysis
万方数据
维普
