Hydraulic control valves, positioned at the terminus of pipe networks, are critical for regulatingflow and pressure, thereby ensuring the operational safety and efficiency of pipeline systems. However,conventional val...Hydraulic control valves, positioned at the terminus of pipe networks, are critical for regulatingflow and pressure, thereby ensuring the operational safety and efficiency of pipeline systems. However,conventional valve designs often struggle to maintain effective regulation across a wide range of systempressures. To address this limitation, this study introduces a novel Pilot hydraulic valves specificallyengineered for enhanced dynamic performance and precise regulation under variable pressure conditions.Building upon prior experimental findings, the proposed design integrates a high-fidelity simulationframework and a surrogate model-based optimization strategy. The study begins by formulating acomprehensive mathematical model of the pipeline system using electro-hydraulic simulation techniques,capturing the dynamic behavior of both the pilot valve and the broader urban water distribution network. Acoupled simulation platform is then developed, leveraging both one-dimensional (1D) and three-dimensional(3D) software tools to accurately analyze the valve’s transient response and operational characteristics. Toachieve optimal valve performance, a multi-objective optimization approach is proposed. This approachemploys a Levy-based Improved Tuna-InspiredWake-Up Optimization Algorithm (L-TIWOA) to refine aBackpropagation (BP) neural network, thereby constructing a highly accurate surrogate model. Compared tothe conventional BP neural network, the improved model demonstrates significantly reduced mean absoluteerror (MAE) and mean squared error (MSE), underscoring its superior predictive capability. The surrogatemodel serves as the objective function within an Improved Multi-Objective Mother Lode OptimizationAlgorithm (IMOMLOA), which is then used to fine-tune the key design parameters of the control valve.Validation through experimental testing reveals that the optimized valve achieves a maximum flow deviationof just 1.11 t/h, corresponding to a control accuracy of 3.7%, at a target flow rate of 30 t/h. Moreover,substantial improvements in dynamic response are observed, confirming the effectiveness of the proposeddesign and optimization strategy.展开更多
基金Gansu Provincial Department of Education(Industrial Support Plan Project:202CYZC-048).
文摘Hydraulic control valves, positioned at the terminus of pipe networks, are critical for regulatingflow and pressure, thereby ensuring the operational safety and efficiency of pipeline systems. However,conventional valve designs often struggle to maintain effective regulation across a wide range of systempressures. To address this limitation, this study introduces a novel Pilot hydraulic valves specificallyengineered for enhanced dynamic performance and precise regulation under variable pressure conditions.Building upon prior experimental findings, the proposed design integrates a high-fidelity simulationframework and a surrogate model-based optimization strategy. The study begins by formulating acomprehensive mathematical model of the pipeline system using electro-hydraulic simulation techniques,capturing the dynamic behavior of both the pilot valve and the broader urban water distribution network. Acoupled simulation platform is then developed, leveraging both one-dimensional (1D) and three-dimensional(3D) software tools to accurately analyze the valve’s transient response and operational characteristics. Toachieve optimal valve performance, a multi-objective optimization approach is proposed. This approachemploys a Levy-based Improved Tuna-InspiredWake-Up Optimization Algorithm (L-TIWOA) to refine aBackpropagation (BP) neural network, thereby constructing a highly accurate surrogate model. Compared tothe conventional BP neural network, the improved model demonstrates significantly reduced mean absoluteerror (MAE) and mean squared error (MSE), underscoring its superior predictive capability. The surrogatemodel serves as the objective function within an Improved Multi-Objective Mother Lode OptimizationAlgorithm (IMOMLOA), which is then used to fine-tune the key design parameters of the control valve.Validation through experimental testing reveals that the optimized valve achieves a maximum flow deviationof just 1.11 t/h, corresponding to a control accuracy of 3.7%, at a target flow rate of 30 t/h. Moreover,substantial improvements in dynamic response are observed, confirming the effectiveness of the proposeddesign and optimization strategy.
