Technical Papers
Mar 19, 2021

Field Measurements and Numerical Modeling of Hydraulic Transients in HDPE Pipeline with PRV Interaction

Publication: Journal of Hydraulic Engineering
Volume 147, Issue 6

Abstract

Pressure reducing valves (PRVs) and high-density polyethylene (HDPE) pipes are commonly used in urban water supply systems (UWSS). To study the joint effect of PRV and viscoelasticity on transient wave propagation, extensive experiments have been conducted in a field-scale reservoir-PRV-viscoelastic pipeline system covering a wide range of internal pressure heads (10to60m) and air temperatures (12°C–33°C). In addition, a one-dimensional method of characteristics (MOC) based model that incorporates a PRV model and the generalized Kelvin-Voigt (K-V) model for pipe viscoelasticity is developed and validated against field data for the first time. The simulated transient pressures are in good agreement with field measurements. The K-V parameters exhibit a clustered distribution and the mean value for each element can provide a satisfactory simulation. The PRV in hydraulic transients can be interpreted as a quasi-dead-end with a self-adjusting opening, and causes additional positive pressure wave reflections that are gradually damped due to viscoelasticity. The dynamic changes in PRV opening, head loss, pressure wave reflection, and transmission induced by an incident pressure surge are predicted. Due to the joint action of PRV and pipeline viscoelasticity, the pressure oscillations in an intact pipe settle to a value that is considerably higher than the initial PRV set pressure. In a leaking pipe, the downstream pressure reverts to the original set pressure within a few wave cycles. Leaks can be detected by wave reflections in the time domain signals. The existence of leaks is also found to be associated with the the amplification and damping of the frequency response function (FRF) of the system at certain resonance peaks. This study provides new insights into wave propagation in HDPE pipeline with PRV interaction and an original data set for validation of leakage detection methods.

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Data Availability Statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

This work was supported by a grant on smart urban water supply systems (SUWSS) from the Hong Kong Research Grants Council under the Theme-Based Research Scheme (T21-602/15-R). The support of the Water Supplies Department in the set up and the advice of Prof. Bruno Brunone and Prof. Pedro Lee in the initial design of the Beacon Hill experimental facility are gratefully acknowledged. The assistance of Dr. Bin-Ping Wu of Tianjin University and Mr. Chak-Hong Tong of Hong Kong University of Science and Technology in the field experiments is appreciated. We also thank the anonymous reviewers for the thorough and constructive reviews that helped improve this manuscript.

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Go to Journal of Hydraulic Engineering
Journal of Hydraulic Engineering
Volume 147Issue 6June 2021

History

Received: Feb 29, 2020
Accepted: Nov 12, 2020
Published online: Mar 19, 2021
Published in print: Jun 1, 2021
Discussion open until: Aug 19, 2021

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Hao-Chen Yan [email protected]
Formerly, M.Phil. Student, Dept. of Civil and Environmental Engineering, Hong Kong Univ. of Science and Technology, Clear Water Bay, Kowloon 999077, Hong Kong SAR. Email: [email protected]
Man-Yue Lam [email protected]
Formerly, Research Associate, Dept. of Civil and Environmental Engineering, Hong Kong Univ. of Science and Technology, Clear Water Bay, Kowloon 999077, Hong Kong SAR; presently, Research Associate, School of Engineering, Cardiff Univ., The Parade, Cardiff, Wales CF24 3AA, UK. Email: [email protected]
Chair Professor, Dept. of Civil and Environmental Engineering, Hong Kong Univ. of Science and Technology, Clear Water Bay, Kowloon 999077, Hong Kong SAR (corresponding author). ORCID: https://orcid.org/0000-0002-6305-2866. Email: [email protected]

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  • Fast computation of inverse transient analysis for pipeline condition assessment via surrogate modeling with sparse sampling strategy, Mechanical Systems and Signal Processing, 10.1016/j.ymssp.2021.107995, 162, (107995), (2022).

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