"In the present research two techniques are applied for leak detection in pipelines. The first method is a hardware-based technique which uses ultrasonic wave's emission for pipeline inspection. Ultrasonic waves are propagated in the pipe walls and reflected signal from leakage will be used for pipe analysis. Several Pipes with various dimensions and characteristics are modeled by finite element method using ANSYS. Second order longitudinal modes of ultrasonic waves are emitted in their walls. For this purpose, excited frequency is calculated such that it excites the second order longitude mode. In order to investigate the behavior of emitted wave in contact with leakage, four sensors are used in outer surface of pipe. Waves are reflected when encountering leakage and the leak location is recognized knowing the wave emission speed and flight time of backscattered signals. Wavelet transform is used for processing these signals and recognizing leak location. This method is tested on several pipe models and it presents satisfactory results for short pipes. The second approach is a software-based method which works based on the transient model of the pipeline. In this method the outputs from simulated pipeline are compared to those measured from flow meters and if their difference goes beyond a threshold value, leak is detected. For leak localization a gradient pressure technique is applied which needs pressure slope measurements at inlet and outlet of the pipeline. Several cases with leak at various positions are studied. This method works well with high accuracy for long pipelines."--Abstract, page iii.
Zawodniok, Maciej Jan, 1975-
Zheng, Y. Rosa
Electrical and Computer Engineering
M.S. in Computer Engineering
Missouri University of Science and Technology
ix, 51 pages
© 2015 Marcia Golmohamadi, All rights reserved.
Thesis - Open Access
Library of Congress Subject Headings
Pipelines -- Computer simulation
Electronic OCLC #
Link to Catalog Recordhttp://laurel.lso.missouri.edu/record=b10848575~S5
Golmohamadi, Marcia, "Pipeline leak detection" (2015). Masters Theses. 7397.