Image based flow visualisation of experimental flow fields inside a gross pollutant trap
Data(s) |
03/12/2012
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Resumo |
Typical flow fields in a stormwater gross pollutant trap (GPT) with blocked retaining screens were experimentally captured and visualised. Particle image velocimetry (PIV) software was used to capture the flow field data by tracking neutrally buoyant particles with a high speed camera. A technique was developed to apply the Image Based Flow Visualization (IBFV) algorithm to the experimental raw dataset generated by the PIV software. The dataset consisted of scattered 2D point velocity vectors and the IBFV visualisation facilitates flow feature characterisation within the GPT. The flow features played a pivotal role in understanding gross pollutant capture and retention within the GPT. It was found that the IBFV animations revealed otherwise unnoticed flow features and experimental artefacts. For example, a circular tracer marker in the IBFV program visually highlighted streamlines to investigate specific areas and identify the flow features within the GPT. |
Formato |
application/pdf |
Identificador | |
Publicador |
The Australasian Fluid Mechanics Society |
Relação |
http://eprints.qut.edu.au/55466/2/55466.pdf http://www.afms.org.au/conference/AFMC.htm Madhani, Jehangir, Young, Joseph A., & Brown, Richard J. (2012) Image based flow visualisation of experimental flow fields inside a gross pollutant trap. In 18th Australasian Fluid Mechanics Conference Proceedings, The Australasian Fluid Mechanics Society, University of Tasmania, Launceston, TAS, pp. 1-4. |
Direitos |
Copyright 2012 [please consult the author] |
Fonte |
Faculty of Science and Technology; High Performance Computing and Research Support |
Palavras-Chave | #010399 Numerical and Computational Mathematics not elsewhere classified #091501 Computational Fluid Dynamics #091504 Fluidisation and Fluid Mechanics #Image based flow visualisation #gross pollutant trap #GPT #flow field |
Tipo |
Conference Paper |