针对黏弹性管道水锤压力精准计算问题,基于有机玻璃管水锤压力试验数据进行分析,并结合黏弹性管材的蠕变特性,对经典黏弹性理论水锤压力计算公式进行修正,构建适用于黏弹性管道的水锤压力计算公式。基于修正后的水锤压力计算公式,利用反向传播(BP)神经网络的非线性拟合功能,预测了同类型管材的水锤压力。研究结果表明:黏弹性管道水锤压力与阀门关闭时间呈反向变化,关阀时间越短,产生的水锤压力值越大;修正后的黏弹性水锤压力公式计算值与试验值的最大误差仅为3.3%,显著优于传统黏弹性理论模型,神经网络预测结果与拟合公式计算结果误差为1%~4%;提出的数值模拟方法可为黏弹性管道工程水锤风险评估提供可靠的参考依据。

[Objective] The water hammer pressure in pipeline systems is a key factor that endangers the safe and stable operation of the system. Especially for the viscoelastic pipes, their time-dependent mechanical behavior makes the analysis and calculation of water hammer pressure more complicated. This study aims to 1) explore the direct water hammer pressure response characteristics of viscoelastic pipes under rapid valve closing by combining experiment and theory, 2) modify the existing theoretical calculation model, and introduce an intelligent algorithm to predict the pressure, and 3) finally to establish a more accurate and practical calculation method for water hammer pressure in viscoelastic pipes. [Methods] The classical Joukowsky formula for direct water hammer pressure was modified and the creep compliance function of pipeline was introduced to derive the direct water hammer pressure calculation formula suitable for viscoelastic pipelines. The formula was then modified by using experimental data. Based on the modified pressure rise formula, the nonlinear fitting function of a backpropagation (BP) neural network was used to predict the direct water hammer pressure rise value of the same type of pipe with the valve closing time, initial flow rate and pipeline material parameters as input features and the peak value of water hammer pressure as the output target, and an intelligent prediction model of direct water hammer pressure in viscoelastic pipeline was established. [Results] In viscoelastic pipes, the shorter the valve closing time, the greater the water hammer pressure value. The delayed strain of viscoelastic material was introduced into the viscoelastic calculation formula. The mechanical response of the pipe wall included two parts: instantaneous elastic strain and delayed strain, resulting in a higher peak value of water hammer pressure, which made the calculated value of the viscoelastic water hammer pressure formula higher than that of the classical water hammer pressure formula. The calculation results showed that the classical elastic theory was no longer suitable for the calculation of direct water hammer pressure in viscoelastic pipelines. The comparison showed that the maximum error between the calculated value and the measured value of the modified formula was small under all test conditions, indicating that the proposed modified formula could better characterize the direct water hammer pressure in viscoelastic pipeline. In the BP neural network prediction model, the prediction results were compared with the calculation results of the modified viscoelastic pipe direct water hammer pressure calculation formula, and it was found that the error between the two was kept in the range of 1% to 4%. [Conclusion] In the water hammer test of PMMA pipeline, the deviation between the calculation results of classical viscoelastic water hammer pressure rise formula and the measured values is large, with a maximum deviation of 15.1%. Based on the viscoelastic theory derivation and experimental data fitting, a modified direct water hammer pressure calculation formula is proposed, which effectively characterizes the influence of pipe viscoelasticity and valve operation rate on the pressure peak. Verification shows that the maximum error of the calculation is only 3.3%, which is significantly better than the classical elastic theory. Furthermore, a hybrid prediction method combining theoretical model and BP neural network is constructed in this study. The theoretical formula is embedded in the network structure, and the prediction error of pressure peak is controlled within 0.1 m under various flow velocity conditions, showing good accuracy, reliability, and adaptability to working conditions. This provides a feasible reference method for pressure prediction and water hammer risk assessment in engineering practice.