Numerical investigation of particles’ transport, deposition and resuspension under unsteady conditions in constructed stormwater ponds

Abstract

Background: Total suspended solids (TSS) loads carried by stormwater runoff is a major pollutant source on receiving water bodies. Stormwater ponds are widely used for controlling TSS discharge. However, the trap efficiency is not satisfactory because it is affected by many complex factors, which are not fully understood. Therefore, there is a necessity to gain insight into the sediment process in stormwater ponds for optimization design of stormwater ponds. To address this issue, we propose a novel modeling framework based on discrete phase model (DPM), aiming to fully represent the sediment transport, settling, and resuspension at grain scale under time-dependent conditions. Results: In the newly proposed method, heterogeneous characteristics of sediments’ loads, varying flows and sediment loads, settling and resuspension effect at grain scale, time-dependent conditions, and turbulent effect are all well considered. The proposed models have been coded with C language and hooked in computational fluid dynamics software Fluent, and the methods were tested with a case of laboratory experimental setup. Different bed boundary conditions are tested and compared with the observation data for optimization parameters’ identification. The simulation results demonstrated that the physically based DPM with the newly developed method can well reproduce the evolution of sediment transport, settling and resuspension behaviors compared with the scale experiment. Conclusions: The newly proposed method can accurately predict the trap efficiency and temporal–spatial sediment distribution. The decomposition of bed load motion at grain scale is a necessary and valid way to represent the sedimentation process in shallow ponds. The developed model could be a tool to help us gain insight into the sediment transport phenomena at grain scale in shallow tanks since it can provide detailed information which the experiment cannot.