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水力发电学报

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基于广义Kelvin–Voigt模型的粘弹性管道流场外推重构方法

  

  • 发布日期:2026-08-06

A Flow Field Extrapolation and Reconstruction Method for Viscoelastic Pipes Based on the Generalized Kelvin–Voigt Model

  • Published:2026-08-06

摘要: 已建成的输水系统,尤其是地下埋设管道,其传感器布设位置通常难以调整,导致管道流场监测过程中不可避免地存在空间盲段。然而,粘弹性管道瞬态流计算通常依赖特定位置的历史压力信息,因而传统方法天然缺乏空间外推求解能力。针对上述问题,本文基于广义Kelvin–Voigt粘弹性本构模型与CTSS数值格式,推导了两种粘弹性管道瞬态流的外推求解公式,并提出了一种弹性–粘弹性混合预测–校正重构算法(EPVC)。根据搭配的求解公式不同,本研究将完整的求解过程划分为E-V方法和V方法。数值模拟与物理实验结果表明,E-V方法能够实现较高精度的瞬态流场外推重构,约70%的测试工况重构均方根误差(RMSE)低于2.0,且所有工况的最大RMSE不超过4.22,其平均RMSE仅为1.46,较V方法降低了46.02%,显著提升了重构精度。进一步地,本研究综合采用Pearson相关性分析、分组趋势分析及随机森林特征贡献度分析等方法,交叉验证了影响重构精度的关键工况参数,并在此基础上结合高精度工况的概率分布给出了本文方法的高精度适用区间。

Abstract: For existing water conveyance systems, especially underground pipelines, the locations of sensors are typically difficult to adjust, which inevitably results in spatial blind zones during flow field monitoring. However, transient flow computations in viscoelastic pipelines usually rely on historical pressure information at specific locations, so conventional methods inherently lack the ability for spatial extrapolation. To address this issue, this study derives two extrapolation formulas for transient flow in viscoelastic pipelines based on the generalized Kelvin–Voigt viscoelastic constitutive model and the CTSS numerical scheme, and proposes an elastic–viscoelastic hybrid prediction–correction reconstruction algorithm (EPVC). Depending on the associated solution formulas, this study categorizes the full computational procedure into the E–V method and the V method. Numerical simulations and physical experiments show that the E–V method can achieve high-accuracy extrapolated reconstruction of transient flow fields: approximately 70% of the test cases have a root mean square error (RMSE) below 2.0, the maximum RMSE across all cases does not exceed 4.22, and the mean RMSE is only 1.46—representing a 46.02% reduction compared with the V method, thereby significantly improving reconstruction accuracy. Furthermore, this study employs Pearson correlation analysis, grouped trend analysis, and random forest feature contribution analysis to cross-validate the key operating parameters affecting reconstruction accuracy, and, based on this, defines the high-accuracy applicability range of the proposed method according to the probability distribution of high-precision operating conditions.

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