Visualising experimental flow fields through a stormwater gross pollutant trap

Madhani, Jehangir T., Young, Joseph A., & Brown, Richard J. (2014) Visualising experimental flow fields through a stormwater gross pollutant trap. Journal of Visualization, 17(1), pp. 17-26.

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Abstract

Abstract An experimental dataset representing a typical flow field in a stormwater gross pollutant trap (GPT) was visualised. A technique was developed to apply the image-based flow visualisation (IBFV) algorithm to the raw dataset. Particle image velocimetry (PIV) software was previously used to capture the flow field data by tracking neutrally buoyant particles with a high speed camera. 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 stormwater pollutant capture and retention behaviour 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 the possible flow paths of pollutants entering the GPT. The investigated flow paths were compared with the behaviour of pollutants monitored during experiments.

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1 citations in Scopus
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ID Code: 63708
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: GPT, Gross pollutant trap, LIC, line integral convolution, IBFV, Image based flow visualisation, PIV, Particle image velocimetry, CFD, Computational fluid dynamics
DOI: 10.1007/s12650-013-0188-8
ISSN: 1875-8975
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > NUMERICAL AND COMPUTATIONAL MATHEMATICS (010300)
Australian and New Zealand Standard Research Classification > PHYSICAL SCIENCES (020000) > CLASSICAL PHYSICS (020300) > Fluid Physics (020303)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > INTERDISCIPLINARY ENGINEERING (091500) > Computational Fluid Dynamics (091501)
Divisions: Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > QUT Faculties and Divisions > Division of Technology, Information and Learning Support
Current > Research Centres > High Performance Computing and Research Support
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 Springer
Deposited On: 27 Oct 2013 22:21
Last Modified: 26 May 2015 20:30

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