Sound pressure level spectrum analysis by combination of 4D PTV and ANFIS method around automotive side-view mirror models

Abstract This paper proposes a data augmentation method based on artificial intelligence (AI) to obtain sound level spectrum as predicting the spatial and temporal data of time-resolved three-dimensional Particle Tracking Velocimetry (4D PTV) data. A 4D PTV has used to measure flow characteristics o...

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Autores principales:	Dong Kim, Arman Safdari, Kyung Chun Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f45c6c49ac2149cf8ce581169a6626ab
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spelling	oai:doaj.org-article:f45c6c49ac2149cf8ce581169a6626ab2021-12-02T14:49:25ZSound pressure level spectrum analysis by combination of 4D PTV and ANFIS method around automotive side-view mirror models10.1038/s41598-021-90734-12045-2322https://doaj.org/article/f45c6c49ac2149cf8ce581169a6626ab2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90734-1https://doaj.org/toc/2045-2322Abstract This paper proposes a data augmentation method based on artificial intelligence (AI) to obtain sound level spectrum as predicting the spatial and temporal data of time-resolved three-dimensional Particle Tracking Velocimetry (4D PTV) data. A 4D PTV has used to measure flow characteristics of three side mirror models adopting the Shake-The-Box (STB) algorithm with four high-speed cameras on a robotic arm for measuring industrial scale. Helium filled soap bubbles are used as tracers in the wind tunnel experiment to characterize flow structures around automobile side mirror models. Full volumetric velocity fields and evolution of vortex structures are obtained and analyzed. Instantaneous pressure fields are deduced by solving a Poisson equation based on the 4D PTV data. To predict spatial and temporal data of velocity field, artificial intelligence (AI)-based data prediction method has applied. Adaptive Neural Fuzzy Inference System (ANFIS) based machine learning algorithm works well to find 4D missing data behind the automobile side mirror model. Using the ANFIS model, power spectrum of velocity fluctuations and sound level spectrum of pressure fluctuations are successfully obtained to assess flow and noise characteristics of three different side mirror models.Dong KimArman SafdariKyung Chun KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dong Kim Arman Safdari Kyung Chun Kim Sound pressure level spectrum analysis by combination of 4D PTV and ANFIS method around automotive side-view mirror models
description	Abstract This paper proposes a data augmentation method based on artificial intelligence (AI) to obtain sound level spectrum as predicting the spatial and temporal data of time-resolved three-dimensional Particle Tracking Velocimetry (4D PTV) data. A 4D PTV has used to measure flow characteristics of three side mirror models adopting the Shake-The-Box (STB) algorithm with four high-speed cameras on a robotic arm for measuring industrial scale. Helium filled soap bubbles are used as tracers in the wind tunnel experiment to characterize flow structures around automobile side mirror models. Full volumetric velocity fields and evolution of vortex structures are obtained and analyzed. Instantaneous pressure fields are deduced by solving a Poisson equation based on the 4D PTV data. To predict spatial and temporal data of velocity field, artificial intelligence (AI)-based data prediction method has applied. Adaptive Neural Fuzzy Inference System (ANFIS) based machine learning algorithm works well to find 4D missing data behind the automobile side mirror model. Using the ANFIS model, power spectrum of velocity fluctuations and sound level spectrum of pressure fluctuations are successfully obtained to assess flow and noise characteristics of three different side mirror models.
format	article
author	Dong Kim Arman Safdari Kyung Chun Kim
author_facet	Dong Kim Arman Safdari Kyung Chun Kim
author_sort	Dong Kim
title	Sound pressure level spectrum analysis by combination of 4D PTV and ANFIS method around automotive side-view mirror models
title_short	Sound pressure level spectrum analysis by combination of 4D PTV and ANFIS method around automotive side-view mirror models
title_full	Sound pressure level spectrum analysis by combination of 4D PTV and ANFIS method around automotive side-view mirror models
title_fullStr	Sound pressure level spectrum analysis by combination of 4D PTV and ANFIS method around automotive side-view mirror models
title_full_unstemmed	Sound pressure level spectrum analysis by combination of 4D PTV and ANFIS method around automotive side-view mirror models
title_sort	sound pressure level spectrum analysis by combination of 4d ptv and anfis method around automotive side-view mirror models
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f45c6c49ac2149cf8ce581169a6626ab
work_keys_str_mv	AT dongkim soundpressurelevelspectrumanalysisbycombinationof4dptvandanfismethodaroundautomotivesideviewmirrormodels AT armansafdari soundpressurelevelspectrumanalysisbycombinationof4dptvandanfismethodaroundautomotivesideviewmirrormodels AT kyungchunkim soundpressurelevelspectrumanalysisbycombinationof4dptvandanfismethodaroundautomotivesideviewmirrormodels
_version_	1718389486590623744

Sound pressure level spectrum analysis by combination of 4D PTV and ANFIS method around automotive side-view mirror models

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