Numerical study of air-entraining and submerged vortices in a pump sump

Numerical detection of harmful vortices in pump sumps, such as an air-entraining vortex (AEV) and a submerged vortex (SMV), is crucially important to develop the drain pump machinery. We performed numerical simulations of the benchmark experiments of the pump sump conducted by Matsui et al. (2006 and 2016) using the OpenFOAM and compared the simulation results with the experimental data considering the effects of turbulence model, grid density and detection method of the vortices. We studied the threshold of the gas-liquid volume fraction of the VOF method and the second invariant of velocity gradient tensor to identify AEV and SMV. The methods proposed in the present paper were found to be very effective for the detection of the vortices, and the simulation results by RANS with the SST k-omega model successfully reproduced the experimental data. LES with the Smagorinsky model, however, was sensitive to the grid system and difficult to reproduce the experimental data even for the finest grid system having 3.7 million cells in the present study