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20 April 2016 Corrosion monitoring along infrastructures using distributed fiber optic sensing
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Pipeline Inspection Gauges (PIGs) are used for internal corrosion inspection of oil pipelines every 3-5 years. However, between inspection intervals, rapid corrosion may occur, potentially resulting in major accidents. The motivation behind this research project was to develop a safe distributed corrosion sensor placed inside oil pipelines continuously monitoring corrosion. The intrinsically safe nature of light provided motivation for researching fiber optic sensors as a solution. The sensing fiber's cladding features polymer plastic that is chemically sensitive to hydrocarbons within crude oil mixtures. A layer of metal, used in the oil pipeline's construction, is deposited on the polymer cladding, which upon corrosion, exposes the cladding to surrounding hydrocarbons. The hydrocarbon's interaction with the cladding locally increases the cladding's refractive index in the radial direction. Light intensity of a traveling pulse is reduced due to local reduction in the modal capacity which is interrogated by Optical Time Domain Reflectometery. Backscattered light is captured in real-time while using time delay to resolve location, allowing real-time spatial monitoring of environmental internal corrosion within pipelines spanning large distances. Step index theoretical solutions were used to calculate the power loss due changes in the intensity profile. The power loss is translated into an attenuation coefficient characterizing the expected OTDR trace which was verified against similar experimental results from the literature. A laboratory scale experiment is being developed to assess the validity of the model and the practicality of the solution.
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Khalil B. Alhandawi, Nader Vahdati, Oleg Shiryayev, and Lydia Lawand "Corrosion monitoring along infrastructures using distributed fiber optic sensing", Proc. SPIE 9803, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2016, 980340 (20 April 2016);

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