Mr. Adam Kong Profile

Adam Kong

at US Naval Academy

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Proceedings Article | 27 March 2019 Presentation + Paper

Discerning localized thermal impulses using an embedded distributed optical fiber sensor network

Brian Jenkins, Peter Joyce, Adam Kong, Charles Nelson

Proceedings Volume 10970, 109701W (2019) https://doi.org/10.1117/12.2514386

KEYWORDS: Sensors, Composites, Temperature metrology, Fiber optics sensors, Optical fibers, Semiconductor lasers, Beam splitters, Directed energy weapons, Sensor networks, Carbon

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An embedded distributed optical fiber sensor (DOFS) based on Rayleigh scattering is used to measure the localized thermal response of a carbon fiber/epoxy composite to directed energy. The thermal impulse from a laser strike is detected while the composite is subjected to cyclic mechanical strain. The ultimate goal is rapid detection of directed energy on the surface of the composite. Prior research demonstrated the use of distributed optical fiber sensors embedded in carbon fiber/epoxy composite structures to rapidly detect temperature changes approaching 1000℃ during a high energy laser strike. However, swept wavelength interferometry used to interrogate a DOFS uses shifts in frequency which can be caused by both changes in temperature and strain. Hence, strain in the composite resulting from mechanical loading is also detected by the embedded sensor and can interfere with rapid detection and measurement of a localized thermal response. Initial tests have demonstrated the simultaneous response of the DOFS to both temperature and strain. A sensing network has been designed to mitigate the response of the sensor to mechanically applied strains, and a simple signal processing technique has been used to cancel the response of the sensor to bending strain while enhancing the thermal response when directed energy is incident on the composite surface. Additional testing indicates that the network and processing technique can also be used to isolate localized impacts on the composite surface from bending strain.

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