Computational fluid dynamics simulations of phase separation in dispersed oil-water pipe flows. (5th March 2023) - Chen, Jianhua; Anastasiou, Charitos; Cheng, Sibo; Basha, Nausheen Mehboob; Kahouadji, Lyes; Arcucci, Rossella; Angeli, Panagiota; Matar, Omar K.
Graphical abstract: Highlights: Investigate separation characteristics by CFD simulations in concert with experiments. Find the sensitivity of the blending treatment in the solver. Emphasise the impact of the dense packed layer (DPL) and phase inversion on phase separation. Reveal the influence of the turbulent dispersion force on the flow pattern. Identify the feasibility of modelling the continuous phase diameter to improve the prediction. Abstract: The separation of liquid–liquid dispersions in horizontal pipes is common in many industrial sectors. It remains challenging, however, to predict the separation characteristics of the flow evolution due to the complex flow mechanisms. In this work, Computational Fluid Dynamics (CFD) simulations of the silicone oil and water two-phase flow in a horizontal pipe are performed. Several cases are explored with different mixture velocities and oil fractions (15%-60%). OpenFOAM (version 8.0) is used to perform Eulerian-Eulerian simulations coupled with population balance models. The 'blending factor' in the multiphaseEulerFoam solver captures the retardation of the droplet rising and coalescing due to the complex flow behaviour in the dense packed layer (DPL). The blending treatment provides a feasible compensation mechanism for the mesoscale uncertainties of droplet flow and coalescence through the DPL and its adjacent layers. In addition, the influence of the turbulent dispersion force is also investigated, which can improve the prediction of the radial distribution of concentrations but worsen the separation characteristics along the flow direction. Although the simulated concentration distribution and layer heights agree with the experiments only qualitatively, this work demonstrates how improvements in drag and coalescence modelling can be made to enhance the prediction accuracy.