Water Science and Engineering (Jun 2024)

Significance of including lid thickness and particle shape factor in numerical modeling for prediction of particle trap efficiency of invert trap

  • Salman Beg,
  • Deo Raj Kaushal

Journal volume & issue
Vol. 17, no. 2
pp. 166 – 176

Abstract

Read online

Sediment accumulation on the bed of open sewers and drains reduces hydraulic efficiency and can cause localized flooding. Slotted invert traps installed underneath the bed of open sewers and drains can eliminate sediment build-up by catching sediment load. Previous three-dimensional (3D) computational studies have examined the particle trapping performance of invert traps of different shapes and depths under varied sediment and flow conditions, considering particles as spheres. For two-dimensional and 3D numerical modeling, researchers assumed the lid geometry to be a thin line and a plane, respectively. In this 3D numerical study, the particle trapping efficiency of a slotted irregular hexagonal invert trap fitted at the flume bottom was examined by incorporating the particle shape factor of non-spherical sewage solid particles and the thicknesses of upstream and downstream lids over the trap in the discrete phase model of the ANSYS Fluent 2020 R1 software. The volume of fluid (VOF) and the realizable k–ε turbulence models were used to predict the velocity field. The two-dimensional particle image velocimetry (PIV) was used to measure the velocity field inside the invert trap. The results showed that the thicknesses of upstream and downstream lids affected the velocity field and turbulent kinetic energy at all flow depths. The joint impact of the particle shape factor and lid thickness on the trap efficiency was significant. When both the lid thickness and particle shape factor were considered in the numerical modeling, trap efficiencies were underestimated, with relative errors of −8.66% to −0.65% in comparison to the experimental values of Mohsin and Kaushal (2017). They were also lower than the values predicted by Mohsin and Kaushal (2017), which showed an overall overestimation with errors of −2.3% to 17.4%.

Keywords