Using Neural Networks for Determining Velocity Vectors of Air Flow Visualized by Helium Bubbles

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dc.contributor.authorŠťastný, Jiřícs
dc.contributor.authorRichter, Jancs
dc.contributor.authorŠťastný, Petrcs
dc.coverage.issue4cs
dc.coverage.volume62cs
dc.date.accessioned2021-10-01T10:53:41Z
dc.date.available2021-10-01T10:53:41Z
dc.date.issued2014-10-01cs
dc.description.abstractOne of the important characteristics of air flow is the velocity of flow. To determine the speed, in addition to other methods, we also use the helium bubbles seeding visualization method, when bubbles are injected into the air stream so that the air flow is obvious to the eye. If a video of such flow is taken, it is possible to determine velocity vectors in a pair of consecutive frames of this video footage, derived from the change in position of individual bubbles. This article describes a method of monitoring the bubbles in consecutive video frames. During this process, helium bubbles are detected in the first image of the pair, either by a firmly defined procedure, or with the use of a neural network. For detected bubbles, the velocity vectors are determined according to the way in which they move, therefore, according to their location in the following frame. Another neural network then determines the velocity vector at any point of image, which will be implemented in the construction of vector maps for the first image. A vector map is used for comprehensive evaluation of air flow and thus, plays an important role in the development of ventilation and air conditioning systems.en
dc.description.abstractOne of the important characteristics of air flow is the velocity of flow. To determine the speed, in addition to other methods, we also use the helium bubbles seeding visualization method, when bubbles are injected into the air stream so that the air flow is obvious to the eye. If a video of such flow is taken, it is possible to determine velocity vectors in a pair of consecutive frames of this video footage, derived from the change in position of individual bubbles. This article describes a method of monitoring the bubbles in consecutive video frames. During this process, helium bubbles are detected in the first image of the pair, either by a firmly defined procedure, or with the use of a neural network. For detected bubbles, the velocity vectors are determined according to the way in which they move, therefore, according to their location in the following frame. Another neural network then determines the velocity vector at any point of image, which will be implemented in the construction of vector maps for the first image. A vector map is used for comprehensive evaluation of air flow and thus, plays an important role in the development of ventilation and air conditioning systems.cs
dc.formattextcs
dc.format.extent757-768cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationActa Universitatis Agriculturae et Silviculturae Mendelianae Brunensis. 2014, vol. 62, issue 4, p. 757-768.en
dc.identifier.doi10.11118/actaun201462040757cs
dc.identifier.issn1211-8516cs
dc.identifier.other110144cs
dc.identifier.urihttp://hdl.handle.net/11012/201726
dc.language.isoencs
dc.publisherMendel University in Brnocs
dc.relation.ispartofActa Universitatis Agriculturae et Silviculturae Mendelianae Brunensiscs
dc.relation.urihttp://acta.mendelu.cz/artkey/acu-201404-0016_using-neural-networks-for-determining-velocity-vectors-of-air-flow-visualized-by-helium-bubbles.phpcs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1211-8516/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectHelium bubbles seedingen
dc.subjectair jet velocityen
dc.subjectMultilayer Perceptron Neural Networken
dc.subjectrecognitionen
dc.subjectvector approximationen
dc.titleUsing Neural Networks for Determining Velocity Vectors of Air Flow Visualized by Helium Bubblesen
dc.title.alternativePuužití neuronových sítí pro určení vektoru rychlosti vzduchového prouducs
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-110144en
sync.item.dbtypeVAVen
sync.item.insts2021.10.01 12:53:41en
sync.item.modts2021.10.01 12:15:12en
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav automatizace a informatikycs
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