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  • EngiTek 2020
  • 51.Cnf-17
EngiTek Congresses with Published Papers
EngiTek 2020, Irbid
Papers Published at EngiTek 2020
All 10 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
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
47.Cnf-1071 Dr. Mohammad M. Banat
Mr. Ahmed Al-Shwmi
Detailed Simplified Implementation of Filter Bank Multicarrier Modulation Using Sub-Channel Prototype Filters
47.Cnf-1097 Dr. Mohammad M. Banat
Mrs. Haifaa Elayyan
Academic Rewards Coin (ARC) Concept: Mining Academic Contribution Into Bitcoin
47.Cnf-1098 Mr. Chris Matrakidis
Dr. Dimitris Uzunidis
Alexandros Stavdas
Prof. Gerasimos Pagiatakis
Analytical modeling of dispersion penalty for NRZ transmission
47.Cnf-1099 Dr. Dimitris Uzunidis
Mr. Chris Matrakidis
Alexandros Stavdas
Prof. Gerasimos Pagiatakis
On the Attainable Transparent Length of Multi-Band Optical Systems Employing Rare – Earth Doped Fiber Amplifiers
47.Cnf-1101 Mr. Georgios D. Kolezas
Dr. Grigorios Zouros
Prof. Gerasimos Pagiatakis
Complex Magnetic and Electric Dipolar Resonances of Subwavelength GaAs Prolate Spheroids
51.Cnf-1104 Dr. Rafat Al-Waked
Underground Car Park - Mechanical Ventilation
47.Cnf-1105 Dr. Vladimir Vinnikov
Dr. Ekaterina Pshehotskaya
The partial disclosure of gpsOne file format for assisted GPS service
47.Cnf-1107 Mr. Ugur Ozbek
Dr. Derya Yiltas-Kaplan
Mr. Ahmet Zengin
Ms. Sema Ölmez
Traffic Analysis of a Software Defined Network
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, Irbid, Jordan
Pages 1-6
Topics Experimental Fluid Flow and Heat Transfer
Molecular Dynamic Simulations
ISSN 2227-331X
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)},
organization={Mosharaka for Research and Studies} }
Paper Views 130 Paper Views Rank 3/524
Paper Downloads 48 Paper Downloads Rank 7/524
EngiTek 2020 Visits: 8812||MechaniTek 2020 Visits: 4368||FMHT Track Visits: 1042