Distributed curvature sensing using long period fiber grating and machine learning numerical analysis.

In this numerical study, we propose a fiber distributed curvature sensor based on the analysis of the spectral transmission of a long period fiber grating (LPG) with a neural network. A simulation of the optical transmissions of a proposed 6-cm LPG structure for different curvature profiles is first performed using EigenMode Expansion and a coupled-mode theory algorithm. Both fiber curvature profiles and their corresponding optical transmission spectra are then injected into a four dense layer neural network which, after training, leads to a 0.40% relative median estimation error in the bending profiles. This paper demonstrates the efficiency of neural network-based optical sensors to analyze non-uniform perturbations, while also revealing long-period gratings to be promising candidates for such systems.

Comprehensive Modeling of Multimode Fiber Sensors for Refractive Index Measurement and Experimental Validation.

We propose and develop a comprehensive model for estimating the refractive index (RI) response over three potential sensing zones in a multimode fiber. The model has been developed based on a combined ray optics, Gaussian beam, and wave optics analysis coupled to the consideration of the injected interrogating lightwave characteristics and validated experimentally through the realization of three sensors with different lengths of stripped cladding sections as the sensing region. The experimental results highly corroborate and validate the simulation output from the model for the three RI sensing zones. The sensors can be employed over a very wide dynamic RI range from 1.316 to over 1.608 at a wavelength of 1550 nm, with the best resolution of 2.2447 × 10-5 RI unit (RIU) obtained in Zone II for a 1-cm sensor length.