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A Novel Fiber-Optic Ice Sensor to Identify Ice Types Based on Total Reflection
To address the issues of not accurately identifying ice types and thickness in current fiber-optic ice sensors, in this paper, we design a novel fiber-optic ice sensor based on the reflected light intensity modulation method and total reflection principle. The performance of the fiber-optic ice sens...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10145984/ https://www.ncbi.nlm.nih.gov/pubmed/37112334 http://dx.doi.org/10.3390/s23083996 |
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author | Zhang, Chi Xiao, Chunhua Li, Shaorong Guo, Xiaowei Wang, Qi He, Yizhou Lv, Huiyan Yan, Hongkai Liu, Dongan |
author_facet | Zhang, Chi Xiao, Chunhua Li, Shaorong Guo, Xiaowei Wang, Qi He, Yizhou Lv, Huiyan Yan, Hongkai Liu, Dongan |
author_sort | Zhang, Chi |
collection | PubMed |
description | To address the issues of not accurately identifying ice types and thickness in current fiber-optic ice sensors, in this paper, we design a novel fiber-optic ice sensor based on the reflected light intensity modulation method and total reflection principle. The performance of the fiber-optic ice sensor was simulated by ray tracing. The low-temperature icing tests validated the performance of the fiber-optic ice sensor. It is shown that the ice sensor can detect different ice types and the thickness from 0.5 to 5 mm at temperatures of −5 °C, −20 °C, and −40 °C. The maximum measurement error is 0.283 mm. The proposed ice sensor provides promising applications in aircraft and wind turbine icing detection. |
format | Online Article Text |
id | pubmed-10145984 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101459842023-04-29 A Novel Fiber-Optic Ice Sensor to Identify Ice Types Based on Total Reflection Zhang, Chi Xiao, Chunhua Li, Shaorong Guo, Xiaowei Wang, Qi He, Yizhou Lv, Huiyan Yan, Hongkai Liu, Dongan Sensors (Basel) Article To address the issues of not accurately identifying ice types and thickness in current fiber-optic ice sensors, in this paper, we design a novel fiber-optic ice sensor based on the reflected light intensity modulation method and total reflection principle. The performance of the fiber-optic ice sensor was simulated by ray tracing. The low-temperature icing tests validated the performance of the fiber-optic ice sensor. It is shown that the ice sensor can detect different ice types and the thickness from 0.5 to 5 mm at temperatures of −5 °C, −20 °C, and −40 °C. The maximum measurement error is 0.283 mm. The proposed ice sensor provides promising applications in aircraft and wind turbine icing detection. MDPI 2023-04-14 /pmc/articles/PMC10145984/ /pubmed/37112334 http://dx.doi.org/10.3390/s23083996 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Chi Xiao, Chunhua Li, Shaorong Guo, Xiaowei Wang, Qi He, Yizhou Lv, Huiyan Yan, Hongkai Liu, Dongan A Novel Fiber-Optic Ice Sensor to Identify Ice Types Based on Total Reflection |
title | A Novel Fiber-Optic Ice Sensor to Identify Ice Types Based on Total Reflection |
title_full | A Novel Fiber-Optic Ice Sensor to Identify Ice Types Based on Total Reflection |
title_fullStr | A Novel Fiber-Optic Ice Sensor to Identify Ice Types Based on Total Reflection |
title_full_unstemmed | A Novel Fiber-Optic Ice Sensor to Identify Ice Types Based on Total Reflection |
title_short | A Novel Fiber-Optic Ice Sensor to Identify Ice Types Based on Total Reflection |
title_sort | novel fiber-optic ice sensor to identify ice types based on total reflection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10145984/ https://www.ncbi.nlm.nih.gov/pubmed/37112334 http://dx.doi.org/10.3390/s23083996 |
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