Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Ultra-sensitive quasi-distributed temperature sensor based on an apodized fiber Bragg grating

Not Accessible

Your library or personal account may give you access

Abstract

This work targets a remarkable quasi-distributed temperature sensor based on an apodized fiber Bragg grating. To achieve this, the mathematical formula for a proposed apodization function is carried out and tested. Then, an optimization parametric process required to achieve the remarkable accuracy that is based on coupled mode theory (CMT) is done. A detailed investigation for the side lobe analysis, which is a primary judgment factor, especially in quasi-distributed configuration, is investigated. A comparison between elite selection of apodization profiles (extracted from related literatures) and the proposed modified-Nuttal profile is carried out covering reflectivity peak, full width half maximum (FWHM), and side lobe analysis. The optimization process concludes that the proposed modified-Nuttal profile with a length (L) of 15 mm and refractive index modulation amplitude (Δn) of 1.4×104 is the optimum choice for single-stage and quasi-distributed temperature sensor networks. At previous values, the proposed profile achieves an acceptable reflectivity peak of 100.426dB, acceptable FWHM of 0.0808 nm, lowest side lobe maximum (SL max) of 7.037×1012dB, lowest side lobe average (SL avg) of 3.883×1012dB, and lowest side lobe suppression ratio (SLSR) of 1.875×1011dB. These optimized characteristics lead to an accurate single-stage sensor with a temperature sensitivity of 0.0136 nm/°C. For the quasi-distributed scenario, a noteworthy total isolation of 91 dB is achieved without temperature, and an isolation of 4.83 dB is achieved while applying temperature of 110°C for a five-stage temperature-sensing network. Further investigation is made proving that consistency in choosing the apodization profile in the quasi-distributed network is mandatory. If the consistency condition is violated, the proposed profile still survives with a casualty of side lobe level rise of 73.2070dB when adding uniform apodization and 46.4823dB when adding Gaussian apodization to the five-stage modified-Nuttall temperature-sensing network.

© 2018 Optical Society of America

Full Article  |  PDF Article
More Like This
Amendment performance of an apodized tilted fiber Bragg grating for a quasi-distributed-based sensor

Eman A. Elzahaby, Ishac Kandas, and Moustafa H. Aly
Appl. Opt. 56(19) 5480-5488 (2017)

Highly sensitive fiber Bragg grating-based pressure sensor using side-hole packaging

Suneetha Sebastian, S. Sridhar, P. Shiva Prasad, and S. Asokan
Appl. Opt. 58(1) 115-121 (2019)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (19)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (1)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (12)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.