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  • 51.Cnf-17
EngiTek Congresses with Published Papers
EngiTek 2020, Irbid
2
Papers Published at EngiTek 2020
All 2 Papers
IDAuthors and TitlePages
51.Cnf-17 Mr. Ameer Mubaslat
Mr. Ahmad Al Haj
Prof. Saud Khashan
Simulation assisted leak detection in pressurized systems using machine learning
1-6
51.Cnf-1062 Dr. Aqeed M. Chyad
Dr. Osama Abudayyeh
Dr. Maha Alkasisbeh
A Nonlinear Regression-Based Machine Learning Model for Predicting Concrete Bridge Deck Condition
7-11
51.Cnf-17 Paper View Page
Title Simulation assisted leak detection in pressurized systems using machine learning
Authors Mr. Ameer Mubaslat, Jordan University of Science and Technology, Irbid, Jordan
Mr. Ahmad Al Haj, Jordan University of Science and Technology, Irbid, Jordan
Prof. Saud Khashan, Jordan University of Science and Technology, Irbid, Jordan
Abstract In this paper, we utilize Computational Fluid Dynamics (CFD) generated data to train a Recurrent Neural Network (RNN) for detecting leaks in pressurized fluid distribution systems. The obtained results support the validity of implementing Machine Learning techniques in approximating active leak locations in a single pipe setup. This paper also discusses the validity of implementing these techniques for implementation on a fluid distribution network.

Results obtained utilizing the RNN model show an adaptive behavior with the system’s consistent response to different configurations of pipes and boundary conditions. The predictions for the leak localities are more accurate and more economically feasible than those obtained with currently used methods.

Track FMHT: Fluid Mechanics and Heat Transfer
Conference 1st International Conference on Mechanical Engineering and Technologies (MechaniTek 2020)
Congress International Congress on Engineering Technologies (EngiTek 2020), 16-18 June 2020 (Remotely), Irbid, Jordan
Pages 1-6
Topics Experimental Fluid Flow and Heat Transfer
Molecular Dynamic Simulations
ISSN 2227-331X
DOI
BibTeX @inproceedings{17EngiTek2020,
title={Simulation assisted leak detection in pressurized systems using machine learning},
author={Ameer Mubaslat, and Ahmad Al Haj, and Saud Khashan},
booktitle={International Congress on Engineering Technologies (EngiTek 2020)},
year={2020},
pages={1-6},
doi={}},
organization={Mosharaka for Research and Studies} }
Paper Views 699 Paper Views Rank 80/525
Paper Downloads 207 Paper Downloads Rank 109/525
EngiTek 2020 Visits: 19103||MechaniTek 2020 Visits: 7745||FMHT Track Visits: 2005