Distributed Fiber-Optic Sensing with Low Bending Loss based on Thin-Core Fiber
This paper presents a thin-core high-numerical aperture (TC-HNA) optical fiber with low macrobending loss for Rayleigh backscattering-based (RBS-based) temperature and strain measurements. The standard single-mode optical fiber (SMF), commonly used in RBS-based sensing, has low macrobending loss-resistance, making the fiber less suited for sensing applications that require sharp bending of the fiber under test. The TC-HNA fiber, on the other hand, offers high macrobending loss-resistance. Experiments were designed and conducted to demonstrate the reliability of a TC-HNA fiber for RBS-based temperature and strain measurements. The macrobending losses in a standard SMF and a TC-HNA fiber were compared in many experiments conducted with different bending radii ranging from 12.5 mm down to 0.9 mm. The RBS signal for a standard SMF, with a single turn of a 5 mm bending radius, degraded severely, exhibiting a 75% drop (6 dB) in signal power. Notably, the RBS signal for a similar test configuration of a TC-HNA fiber did not exhibit any observable loss. We demonstrated that a TC-HNA fiber could be used for RBS-based measurements with a single turn of ~1 mm bending radius. Moreover, an experiment was conducted to demonstrate the spatial thermal mapping capability of the TC-HNA fiber configured with multiple tight bends with radii in the range ~2–3 mm. The high macrobending loss-resistance of the TC-HNA fiber could extend the range of applications for RBS-based measurements to compact structures, such as batteries, robotic fingers, and printed circuit boards, where sharp bending of the test optical fiber is unavoidable.
M. Roman et al., "Distributed Fiber-Optic Sensing with Low Bending Loss based on Thin-Core Fiber," IEEE Sensors Journal, vol. 21, no. 6, pp. 7672-7680, Institute of Electrical and Electronics Engineers (IEEE), Jan 2021.
The definitive version is available at https://doi.org/10.1109/JSEN.2021.3050702
Materials Science and Engineering
Electrical and Computer Engineering
Peaslee Steel Manufacturing Research Center
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11 Jan 2021