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A Method to Measure and Model Acoustic Emissions of Multicopters

There is a growing interest for commercial applications of Unmanned Aerial Vehicles, but important foundations for an assessment, among others about noise, are missing. This contribution specifically focuses on a method to measure and model the sound radiation of multicopters. The emission predictio...

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Detalles Bibliográficos
Autores principales: Wunderli, Jean Marc, Meister, Jonas, Boolakee, Oliver, Heutschi, Kurt
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819535/
https://www.ncbi.nlm.nih.gov/pubmed/36612420
http://dx.doi.org/10.3390/ijerph20010096
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author Wunderli, Jean Marc
Meister, Jonas
Boolakee, Oliver
Heutschi, Kurt
author_facet Wunderli, Jean Marc
Meister, Jonas
Boolakee, Oliver
Heutschi, Kurt
author_sort Wunderli, Jean Marc
collection PubMed
description There is a growing interest for commercial applications of Unmanned Aerial Vehicles, but important foundations for an assessment, among others about noise, are missing. This contribution specifically focuses on a method to measure and model the sound radiation of multicopters. The emission prediction is hereby based on measurements using a multiple regression approach. An important finding is that the directivity pattern is widely independent of the rotational speed of the rotors and of the flight procedure. Consequently, the directivity pattern can be determined for a stationary hover flight, which considerably simplifies the measurement procedure. In addition to a rotational speed-dependent sound emission model for hover flight, a multicopter-specific correction term is required to account for forward flight. The validity of this approach is demonstrated based on the field measurements of three different multicopter models.
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spelling pubmed-98195352023-01-07 A Method to Measure and Model Acoustic Emissions of Multicopters Wunderli, Jean Marc Meister, Jonas Boolakee, Oliver Heutschi, Kurt Int J Environ Res Public Health Article There is a growing interest for commercial applications of Unmanned Aerial Vehicles, but important foundations for an assessment, among others about noise, are missing. This contribution specifically focuses on a method to measure and model the sound radiation of multicopters. The emission prediction is hereby based on measurements using a multiple regression approach. An important finding is that the directivity pattern is widely independent of the rotational speed of the rotors and of the flight procedure. Consequently, the directivity pattern can be determined for a stationary hover flight, which considerably simplifies the measurement procedure. In addition to a rotational speed-dependent sound emission model for hover flight, a multicopter-specific correction term is required to account for forward flight. The validity of this approach is demonstrated based on the field measurements of three different multicopter models. MDPI 2022-12-21 /pmc/articles/PMC9819535/ /pubmed/36612420 http://dx.doi.org/10.3390/ijerph20010096 Text en © 2022 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
Wunderli, Jean Marc
Meister, Jonas
Boolakee, Oliver
Heutschi, Kurt
A Method to Measure and Model Acoustic Emissions of Multicopters
title A Method to Measure and Model Acoustic Emissions of Multicopters
title_full A Method to Measure and Model Acoustic Emissions of Multicopters
title_fullStr A Method to Measure and Model Acoustic Emissions of Multicopters
title_full_unstemmed A Method to Measure and Model Acoustic Emissions of Multicopters
title_short A Method to Measure and Model Acoustic Emissions of Multicopters
title_sort method to measure and model acoustic emissions of multicopters
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819535/
https://www.ncbi.nlm.nih.gov/pubmed/36612420
http://dx.doi.org/10.3390/ijerph20010096
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