Numerical investigation of three-dimensional effects of hydrodynamic cavitation in a Venturi tube

Abstract

Hydrodynamic Cavitation (HC) is a highly turbulent, unsteady, multi-phase flow that has been useful in many processing applications like wastewater treatment and process intensification and hence needs to be studied in detail. The aim of this study is to investigate the mechanisms driving HC inside a Venturi tube using numerical simulations. The numerical simulations are conducted in the form of both two-dimensional (2D) and three-dimensional (3D) simulations using the Detached Eddy Simulation (DES) model database to simulate the cavitation–turbulence interplay, and the results are validated against high-fidelity experimental data. Initial 2D calculation results show that though URANS models are able to show unsteady cavitation, they are unable to reproduce the correct cavity morphology while the DES models reproduce the cavity morphology accurately. After extending to 3D simulations and the resulting vorticity budget analysis highlight the cavitation–vortex interactions and show the domination of velocity gradients and the growth and shrinking of the fluid element terms over the baroclinic torque for vortex production. Finally, localized scale comparisons are conducted to evaluate the model’s ability to simulate the cavitation–turbulence interaction. It is observed that the 3D DES simulations are able to predict accurately the cavitation–turbulence interaction on a localized scale for turbulence properties like Reynolds shear stress and Turbulent Kinetic Energy (TKE), emphasizing the 3D effects of turbulence and their influence on the cavitating flow. However, significant discrepancies continue to exist between the numerical simulations and experiments, near the throat where the numerical simulations predict a thinner cavity. Therefore, this study offers new insights on simulating HC and highlights the bottleneck between turbulence model development and accurate simulations of HC to provide a reference for improving modeling accuracy.