A novel method to capture and analyze flow in a gross pollutant trap using image-based vector visualization
Madhani, Jehangir T., Young, Joseph A., Kelson, Neil A., & Brown, Richard J. (2009) A novel method to capture and analyze flow in a gross pollutant trap using image-based vector visualization. Water, Air, & Soil Pollution : Focus, 9(5-6), pp. 357-369.
A novel method is developed to capture and analyse several experimental flow regimes through a gross pollutant trap (GPT) with fully and partially blocked screens. Typical flow conditions and screen blockages are based on findings from field investigations that show a high content of organic matter in urban areas. Fluid motion of neutral buoyant particles is tracked using a high-speed camera and particle image velocimetery (PIV) software. The recorded fluid motion is visualized through an image based, line integral convolution (LIC) algorithm, generally suitable for large computational fluid dynamics (CFD) datasets. The LIC method, a dense representation of streamlines, is found to be superior to the point-based flow visualization (e.g., hedgehog or arrow plots) in highlighting main flow features that are important for understanding litter capture and retention in the GPT. Detailed comparisons are made between the flow regimes, and the results are compared with CFD data previously obtained for fully blocked screens. The LIC technique is a useful tool in identifying flow structures in the GPT and areas that are subjected to abnormalities difficult to detect by conventional methods. The method is found to be useful both in the laboratory and in the field, with little preparation and cost. The enhancements and pitfalls of the LIC technique along with the experimentally captured flow field are presented and discussed.
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Journal Article|
|Keywords:||Line integral convolution (LIC), Gross pollutant trap (GPT), Litter, Flow visualizations, HPC, High Performance Computing|
|Subjects:||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)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ENVIRONMENTAL ENGINEERING (090700)
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > NUMERICAL AND COMPUTATIONAL MATHEMATICS (010300) > Numerical and Computational Mathematics not elsewhere classified (010399)
|Divisions:||Current > QUT Faculties and Divisions > Division of Technology, Information and Learning Support|
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Current > Research Centres > High Performance Computing and Research Support
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2009 Springer|
|Copyright Statement:||The original publication is available at www.springerlink.com|
|Deposited On:||20 May 2009 12:36|
|Last Modified:||05 Feb 2013 13:09|
Repository Staff Only: item control page