Jehangir T. Madhani

An experimental and theoretical investigation of flow in a gross pollutant trap.

Flow through a gross pollutant trap (GPT) with fully blocked screens is investigated experimentally and theoretically using computational fluid dynamics (CFD). Due to the wide range of possible flow regimes, an experimental approach is developed which uses a downstream weir arrangement to control the nature of the flow and the variation in free surface height. To determine the overall flow structure, measurements are taken at a fixed depth throughout the trap with an Acoustic Doppler Velocimeter (ADV), including velocity profile data across three cross sections of the GPT suitable for more detailed comparison with simulations. Observations of the near-wall flow features at the free surface are also taken, due to their likely importance for understanding litter capture and retention in the GPT. Complementary CFD modelling (using Fluent 6.3) is performed using a two-dimensional k-epsilon turbulence model along with either standard wall law boundary conditions or enhanced near-wall modelling approaches. Comparison with experiments suggest that neither CFD modelling approach could be considered as clearly superior to the other, despite the significant difference in near-wall mesh refinement and modelling that is involved. The experimental approach taken here is found useful to control the flow regime in the GPT and further experiments are recommended to study a greater range of flow conditions.

Visualising experimental flow fields through a stormwater gross pollutant trap

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 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 facilitated 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.Graphical abstract