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Advances in Fiber Optic Sensors for Energy Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: 25 May 2024 | Viewed by 1581

Special Issue Editors


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Guest Editor
Research & Innovation Center, National Energy Technology Laboratory, 3610 Collins Ferry Road, Morgantown, WV 26505, USA
Interests: fibre-optic sensors; Rayleigh scattering; distributed sensors; high-speed optical techniques; light interferometry; temperature sensors; vibration measurement; Fabry–Perot interferometers; gas sensors; laser cavity resonators

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Guest Editor
Research & Innovation Center, Leidos/National Energy Technology Laboratory, 626 Cochrans Mill Road, Pittsburgh, PA 15236, USA
Interests: distributed fiber-optic sensors; interferometric fiber sensors; nonlinear fiber optics; fiber sensors for energy infrastructure monitoring; chemical sensing

Special Issue Information

Dear Colleagues,

Fiber optic sensors have been exploited for the last several decades, and there have been significant advances in energy-monitoring applications. Fiber optic sensors represent a rapidly growing research area, where challenges concerning increased sensitivity, selectivity, resolution, harsh environment, and cost reduction capability need to be thoroughly addressed.

This Special Issue aims to highlight the advancements and explore new findings that expand the possibilities of fiber-optic sensors usage in energy applications. Both original research papers and review articles describing the current state-of-the-art in this research field are welcome. This Special Issue brings out the immense diversity in every perspective of the evolution of fiber-optic sensor science and technologies.

The list of topics includes, but is not limited to;

  • Specialty fibers and passive/active fiber systems for sensing applications.
  • Distributed fiber-optic-sensors-based Rayleigh, Brillouin, and Raman scattering.
  • Physical, chemical, acoustics, and electromagnetic fiber sensors.
  • FBG, SMS, fiber ring, Fabry–Pérot, and other novel fiber sensing structures.
  • Fiber sensors with big data, AI/machine learning methods, and sensor data processing.
  • High-temperature, radiation, leak detection in harsh environment energy applications.
  • Advanced sensitive materials to fabricate optical fiber sensors.
  • Fabrication, modeling, and multiparameter sensing fiber devices.
  • Fiber sensors in energy industry practices.

Dr. Michael P. Buric
Dr. Nageswara R. Lalam
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

15 pages, 7043 KiB  
Article
Robust Vector BOTDA Signal Processing with Probabilistic Machine Learning
by Abhishek Venketeswaran, Nageswara Lalam, Ping Lu, Sandeep R. Bukka, Michael P. Buric and Ruishu Wright
Sensors 2023, 23(13), 6064; https://doi.org/10.3390/s23136064 - 30 Jun 2023
Viewed by 966
Abstract
This paper presents a novel probabilistic machine learning (PML) framework to estimate the Brillouin frequency shift (BFS) from both Brillouin gain and phase spectra of a vector Brillouin optical time-domain analysis (VBOTDA). The PML framework is used to predict the Brillouin frequency shift [...] Read more.
This paper presents a novel probabilistic machine learning (PML) framework to estimate the Brillouin frequency shift (BFS) from both Brillouin gain and phase spectra of a vector Brillouin optical time-domain analysis (VBOTDA). The PML framework is used to predict the Brillouin frequency shift (BFS) along the fiber and to assess its predictive uncertainty. We compare the predictions obtained from the proposed PML model with a conventional curve fitting method and evaluate the BFS uncertainty and data processing time for both methods. The proposed method is demonstrated using two BOTDA systems: (i) a BOTDA system with a 10 km sensing fiber and (ii) a vector BOTDA with a 25 km sensing fiber. The PML framework provides a pathway to enhance the VBOTDA system performance. Full article
(This article belongs to the Special Issue Advances in Fiber Optic Sensors for Energy Applications)
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