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Icing Condition Predictions Using FBGS

Icing is a hazard which is important for the aerospace industry and which has grown over the last few years. Developing sensors that can detect the existence not only of standard icing conditions with typically small droplet size, but also of Supercooled Large Droplet (SLD) conditions is one of the...

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Autores principales: González del Val, Miguel, Mora Nogués, Julio, García Gallego, Paloma, Frövel, Malte
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473373/
https://www.ncbi.nlm.nih.gov/pubmed/34577259
http://dx.doi.org/10.3390/s21186053
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author González del Val, Miguel
Mora Nogués, Julio
García Gallego, Paloma
Frövel, Malte
author_facet González del Val, Miguel
Mora Nogués, Julio
García Gallego, Paloma
Frövel, Malte
author_sort González del Val, Miguel
collection PubMed
description Icing is a hazard which is important for the aerospace industry and which has grown over the last few years. Developing sensors that can detect the existence not only of standard icing conditions with typically small droplet size, but also of Supercooled Large Droplet (SLD) conditions is one of the most important aims in order to minimize icing hazards in the near future. In the present paper a study of the Fiber Bragg Grating Sensors’ (FBGSs) performance as a flight icing detection system that predicts the conditions of an icing cloud is carried out. The test matrix was performed in the INTA Icing Wind Tunnel (IWT) with several icing conditions including SLD. Two optic fibers with 16 FBGS in total were integrated in the lower and upper surface of an airfoil to measure the temperature all over the chord. The results are compared with a Messinger heat and mass balance model and the measurements of the FBGS are used to predict the Liquid Water Content (LWC) and Ice Accretion Rate (IAR). Finally, the results are evaluated and a sensor assessment is made. A good correlation was observed between theoretical calculations and test results obtained with the FBGS in the IWT tests. FBGS proved to detect the beginning and end of ice accretion, LWC and IAR quickly and with good precision.
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spelling pubmed-84733732021-09-28 Icing Condition Predictions Using FBGS González del Val, Miguel Mora Nogués, Julio García Gallego, Paloma Frövel, Malte Sensors (Basel) Article Icing is a hazard which is important for the aerospace industry and which has grown over the last few years. Developing sensors that can detect the existence not only of standard icing conditions with typically small droplet size, but also of Supercooled Large Droplet (SLD) conditions is one of the most important aims in order to minimize icing hazards in the near future. In the present paper a study of the Fiber Bragg Grating Sensors’ (FBGSs) performance as a flight icing detection system that predicts the conditions of an icing cloud is carried out. The test matrix was performed in the INTA Icing Wind Tunnel (IWT) with several icing conditions including SLD. Two optic fibers with 16 FBGS in total were integrated in the lower and upper surface of an airfoil to measure the temperature all over the chord. The results are compared with a Messinger heat and mass balance model and the measurements of the FBGS are used to predict the Liquid Water Content (LWC) and Ice Accretion Rate (IAR). Finally, the results are evaluated and a sensor assessment is made. A good correlation was observed between theoretical calculations and test results obtained with the FBGS in the IWT tests. FBGS proved to detect the beginning and end of ice accretion, LWC and IAR quickly and with good precision. MDPI 2021-09-09 /pmc/articles/PMC8473373/ /pubmed/34577259 http://dx.doi.org/10.3390/s21186053 Text en © 2021 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
González del Val, Miguel
Mora Nogués, Julio
García Gallego, Paloma
Frövel, Malte
Icing Condition Predictions Using FBGS
title Icing Condition Predictions Using FBGS
title_full Icing Condition Predictions Using FBGS
title_fullStr Icing Condition Predictions Using FBGS
title_full_unstemmed Icing Condition Predictions Using FBGS
title_short Icing Condition Predictions Using FBGS
title_sort icing condition predictions using fbgs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473373/
https://www.ncbi.nlm.nih.gov/pubmed/34577259
http://dx.doi.org/10.3390/s21186053
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