Application of Machine Learning Coupled with Stochastic Numerical Analyses for Sizing Hybrid Surge Vessels on Low-Head Pumping Mains
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- @Article{sattar:2023:Water,
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author = "Ahmed M. A. Sattar and Abedalkareem Nedal Ghazal and
Mohamed Elhakeem and Amgad S. Elansary and
Bahram Gharabaghi",
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title = "Application of Machine Learning Coupled with
Stochastic Numerical Analyses for Sizing Hybrid Surge
Vessels on {Low-Head} Pumping Mains",
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journal = "Water",
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year = "2023",
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volume = "15",
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number = "19",
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pages = "Article No. 3525",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2073-4441",
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URL = "https://www.mdpi.com/2073-4441/15/19/3525",
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DOI = "doi:10.3390/w15193525",
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abstract = "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 used 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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notes = "also known as \cite{w15193525}",
- }
Genetic Programming entries for
Ahmed M Abdel Sattar
Abedalkareem Nedal Ghazal
Mohamed Elhakeem
Amgad S Elansary
Bahram Gharabaghi
Citations