IDENTIFYING THE MECHANISM OF THE MIXED-PHASE FLOW IN THE HORIZONTAL PIPELINE USING COMPUTATIONAL FLUID DYNAMIC APPROACH

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Abstract

In this study, computational analysis has been carried out using computational fluid dynamics (CFD). These calculations have been made to investigate the rheological behavior of the mixed-phase flow in horizontal pipelines. In order to study the shear stress in a vertical pipe, a new numerical model for oil-water dispersion in three dimensions has been developed. CFD software has been used to study the wall shear stress function and water droplet pressure. Using Reynolds numbers and the Navier-Stokes equations with k–turbulence factor to save energy, the flow range for the continuous process was explained. The results from a recent study on experimental methodology were simulated. In this study, the diameter of the tube is 40 mm and the length is 3.5 m and modeled and analyzed using Ansys software. Thus, the geometry has been imported and modeled using the CFD tool. The meshed model has been tested and converged accordingly. The primary data of the simulation have been verified with experimental results successfully. Oil droplet widths have previously been thought to be dependent on the flow Reynolds number, which was confirmed in this case study. Droplet diameter Dd was measured at 6 mm while the mixture moved at a speed of 1.9 m/s. It was found that the largest shear stress value was found at the top of the pipe, where the oil fraction (cut-off) was 0.3, in the simulation results for varied velocities (1.6, 2.5, 2.9 m/s) and oil fraction (cut-off) values. The results of the simulation analysis of the two-phase flow of crude oil for the horizontal pipe are wall shear stresses with different velocities for crude oil in the two-phase flow. As well as pressure drop at different velocities for the same fluids

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Aned, A. M., Raheemah, S. H., & Fadheel, K. I. (2022). IDENTIFYING THE MECHANISM OF THE MIXED-PHASE FLOW IN THE HORIZONTAL PIPELINE USING COMPUTATIONAL FLUID DYNAMIC APPROACH. Eastern-European Journal of Enterprise Technologies, 2(7–116), 44–50. https://doi.org/10.15587/1729-4061.2022.254214

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