Ring-core few-mode fiber and DPP-BOTDA-based distributed large-curvature sensing eligible for shape reconstruction

Area of Science:

• Optoelectronics
• Fiber Optics Sensing
• Materials Science

Background:

• Distributed fiber optic sensors are crucial for structural health monitoring.
• Existing sensors face limitations in curvature sensing range and accuracy.
• Ring-core few-mode fiber (RC-FMF) offers unique light propagation properties.

Purpose of the Study:

• To develop a distributed large-curvature sensor with enhanced performance.
• To investigate the application of RC-FMF with DPP-BOTDA for high-curvature detection.
• To enable precise measurement in challenging environments.

Main Methods:

• Utilized ring-core few-mode fiber (RC-FMF) integrated with a steel substrate.
• Employed differential pulse-pair Brillouin optical time-domain analysis (DPP-BOTDA) for signal processing.
• Applied the Frenet-Serret algorithm for reconstructing asymmetric shapes.

Main Results:

• Achieved a significantly larger curvature-sensing range compared to conventional sensors.
• Demonstrated excellent tolerance to bending-induced optical loss.
• Observed an increased Brillouin gain coefficient and longer sensing distances.
• Enabled continuous absolute curvature measurement.

Conclusions:

• The proposed RC-FMF based DPP-BOTDA sensor offers superior performance for large-curvature detection.
• This technology is suitable for end-tracking in soft robotics and structural health monitoring of civil infrastructures.
• The sensor provides a robust and accurate solution for demanding applications.