A novel method to capture and analyze flow in a gross pollutant trap using image-based vector visualization

Abstract

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.