Dynamic monitoring of leaking oil diffusion in porous media: An improved method assisting buried oil pipeline condition assessment DOI

Yuanbo Yin,

Xiangning Hu,

Wen Yang

et al.

Tunnelling and Underground Space Technology, Journal Year: 2023, Volume and Issue: 140, P. 105289 - 105289

Published: June 30, 2023

Language: Английский

Detecting Excitations of Pipes, Ropes, and Bars Using Piezo Sensors and Collecting Information Remotely DOI Creative Commons
M. Cirillo, Э. Реали,

Giuseppe Soda

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(5), P. 1444 - 1444

Published: Feb. 27, 2025

An investigation of a non-invasive method to detect defects and localize excitations in metallic structures is presented. It shown how signals generated by very sensitive piezo sensor assemblies, secured the elements, can allow for space localization analyzed structures. The origin are acoustic modes light percussive whose strength order tenths newton that provide signal amplitudes few hundred millivolts. Tests detection scheme performed on steel ropes, iron pipes, bars with lengths range 1–6 m output shaped form clean pulse. when adequately shaped, feed input an RF transmitter, which turn transfers information remote receiver readout allows remotely analyzing collected elements. Considering voltage amplitude (of 300 mV) sensors as result excitations, low power required transmitting data, cost sensing assembly, it conceivable our devices could even tens kilometers away setting up array controlling real time status pipe networks.

Language: Английский

Citations

0
Failure Pressure Prediction of Corroded High-Strength Steel Pipe Elbow Subjected to Combined Loadings Using Artificial Neural Network DOI Creative Commons
Suria Devi Vijaya Kumar,

Saravanan Karuppanan,

Veeradasan Perumal

et al.

Mathematics, Journal Year: 2023, Volume and Issue: 11(7), P. 1615 - 1615

Published: March 27, 2023

There is no reliable failure pressure assessment method for pipe elbows, specifically those subjected to internal and axial compressive stress, other than time-consuming numerical methods, which are impractical in time-critical situations. This paper proposes a set of empirical equations, based on Artificial Neural Networks, the prediction elbows combined loadings. The neural network was trained with data generated using Finite Element Method. A parametric analysis then carried out study behaviour corroded high-strength steel It found that defect depth, length, spacing (longitudinal), stress greatly influenced elbow, especially defects located at intrados, reductions ranging from 12.56–78.3%. On contrary, effects circumferential were insignificant, maximum 6.78% reduction elbow. enables loadings equations. However, its application limited single, longitudinally interacting, circumferentially interacting specified range parameters mentioned this study.

Language: Английский

Citations

7
Examining the Impact of Producer Gas in Specific Regions of Pipeline Materials of Producer Gas Systems DOI

C. M. Vivek,

P. K. Srividhya

Journal of Materials Engineering and Performance, Journal Year: 2024, Volume and Issue: unknown

Published: March 12, 2024

Language: Английский

Citations

2
Machine Learning-Based Outlier Detection for Pipeline In-line Inspection Data DOI Creative Commons
Muhammad Nihal Hussain, Tieling Zhang

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 254, P. 110553 - 110553

Published: Oct. 12, 2024

Language: Английский

Citations

2
Dynamic monitoring of leaking oil diffusion in porous media: An improved method assisting buried oil pipeline condition assessment DOI

Yuanbo Yin,

Xiangning Hu,

Wen Yang

et al.

Tunnelling and Underground Space Technology, Journal Year: 2023, Volume and Issue: 140, P. 105289 - 105289

Published: June 30, 2023

Language: Английский

Citations

4