Optimal volume selection of air vessels in long-distance water supply systems

Shi, Liming; Zhang, Jian; Yu, Xiaodong; Wang, Xingtao; Chen, Xu-yun; Zhang, Zhe-xin

doi:10.2166/aqua.2021.079

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…∀ o air = 5.127D −0.333 H −0.111 s a −0.33 ∀ cc (24) ∀ f air = D 2.667 H −0.4 s v 1.2 L 0.5 f −0.05 (25) where ∀ cc is the compression chamber volume (m 3 ), ∀ o air is the initial gas volume (m 3 ), ∀ f air is the expanded air volume (m 3 ), v is the steady state velocity (m/s), D is the pipeline diameter (m), L is the pipeline length (m), H s is the static head (m), and f is the Darcy-Weisbach friction coefficient. The compression chamber volume is an influential predictive input for the initial air volume Genetic Programming model only, as shown in Equation (23).…”

Section: Developed Genetic Programming Modelsmentioning

confidence: 99%

“…The developed formulae are easy to apply and have been developed based on thousands of generated numerical pump failure cases. Based on the Krylov-Mitropolsky method, an analytical mode for the water level oscillations in the surge vessel was developed [23]. They used the developed model for optimal volume selection of the air vessel and used two practical cases for testing.…”

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning Coupled with Stochastic Numerical Analyses for Sizing Hybrid Surge Vessels on Low-Head Pumping Mains

Sattar,

Ghazal,

Elhakeem

et al. 2023

Water

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In surge protection, low-head profiles are deemed a challenge in pump failure events since they are prone to severe negative pressure surges that require an uneconomical surge vessel volume. A hybrid surge vessel with a dipping tube can provide required protection with reasonable economic volume. This work presents novel analyses for the hybrid surge vessel and develops a simple model for its optimum sizing using a stochastic numerical approach coupled with machine learning. Practical ranges for correct sizing of vessel components, such as ventilation tube, inlet/outlet air valves, and compression chamber, are presented for optimal protection and performance. The water hammer equations are iteratively solved using the hybrid surge vessel’s revised boundary conditions within the method of characteristics numerical framework to generate 2000 cases representing real pump failures on low-head pipelines. Genetic programming is utilized to develop simple relations for prediction of the hybrid vessel initial and expanded air volumes in addition to the compression chamber volume. Moreover, the developed model presented a classification index for low-head pipelines on which the hybrid vessel would be most economical. The developed model yielded good prediction error statistics. The developed model proves to be more accurate and easier to use than the classical design charts for the low-head pumping mains. The model clearly showed the relation between various hydraulic and pipe parameters, with pipe diameter and static head as the most influencing parameters on compression chamber volume and expanded air volume. The developed model, together with the classification indices, can be used for preliminary surge protection sizing for low-head pipelines.

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Section: Developed Genetic Programming Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Coupled with Stochastic Numerical Analyses for Sizing Hybrid Surge Vessels on Low-Head Pumping Mains

Sattar,

Ghazal,

Elhakeem

et al. 2023

Water

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Air vessel sizing approach for pipeline protection using artificial neural networks

Tawfik

2023

J. Eng. Appl. Sci.

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Water hammer is the unsteady flow in conduits due to sudden change of velocities in pipelines and poses it to danger. Sensitivity analysis is performed to show the effect of pump and pipeline parameters on the maximum and minimum head just downstream the pump after pump power failure. A new approach to find the required gas volume in a hydropneumatic tank (air vessel) to protect the pipeline using artificial neural networks (ANNs) is introduced. About 760 runs were generated using Bentley Hammer v8i. For each run, the maximum and minimum head just downstream the pump were calculated for a pump power failure. Two MATLAB codes are written to use networks for finding the best design that guarantees the pressure in the pipeline is within the allowable range. The results showed that pump inertia and wave celerity have a very small effect on the maximum and minimum heads.

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Negative pressure protection of water supply systems with multi-undulating terrain by one-way surge tanks

Zhao,

Yu,

Tang

et al. 2023

AQUA — Water Infrastructure, Ecosystems and Society

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The protection of negative pressures generated by the hydraulic transient process in the water supply system is crucial to safe and stable operation. In this study, a mathematical model of a pipeline hydraulic transient system with multi-undulating terrain was established based on the method of characteristics (MOC). The generation and development of water hammer negative pressures were analyzed, and double one-way surge tank protection schemes were proposed. It proved that the first surge tank should be located at the initial negative pressure point, and the second surge tank should be located at the second-highest point rather than the highest point. Additionally, compared with the theoretical minimum height, there was an optimization margin for the total surge tank height, which was reduced by 21% in this study. Meanwhile, the applicability of protection schemes and the influence of the one-way surge tank number on the total height were analyzed. The total height reduced with the increase of one-way surge tank number and tended to a minimum value. By comprehensively considering the engineering investment and negative pressure protection effect, the optimal surge tank number could be determined. This research represents an advance in negative pressure protection in multi-undulating terrain and provides support for further engineering studies.

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Optimal volume selection of air vessels in long-distance water supply systems

Cited by 5 publications

References 17 publications

Application of Machine Learning Coupled with Stochastic Numerical Analyses for Sizing Hybrid Surge Vessels on Low-Head Pumping Mains

Application of Machine Learning Coupled with Stochastic Numerical Analyses for Sizing Hybrid Surge Vessels on Low-Head Pumping Mains

Air vessel sizing approach for pipeline protection using artificial neural networks

Negative pressure protection of water supply systems with multi-undulating terrain by one-way surge tanks

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