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Research Article

Improving flow efficiency in curved pipes during multi-phase, immiscible fluid flow using edge-tailored guide vanes

C. J. Ejeh1,2( )F. H. Alawwa1A. Kofi2A. Ingrid2
Department of Mechanical Engineering, School of Engineering, Khalifa University of Science and Technology, Abu Dhabi, P.O. Box 127788, United Arab Emirates
Department of Oil and Gas Engineering, All Nations University, Koforidua, P.O. Box 1908, Eastern Region, Ghana
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Abstract

High static pressure due to flow transitioning associated with recirculation, mixing, and separation around a pipe bend is a possible cause for a decrease in flow efficiency. This paper aims to use edge-tailored guide vanes to ease flow transition and improve flow efficiency, numerically. Here, flow efficiency serves the purpose for qualifying the effectiveness of the proposed technology. Sensitivity studies were performed on the influence of number of guide vane and guide vane thickness in a 45° pipe elbow. In setting up the numerical model for the assumed two-phase flow system (crude oil and water), the volume of fluid model was activated to model time-dependent fluctuations of each interacting phase volume fraction throughout the flow period. Furthermore, the improved delayed detached-eddy simulation turbulence model is employed to resolve the flow features in and outside the wall boundary layer. From the findings, an improvement in flow performance was witnessed using guide vanes. Furthermore, three thin and non-contacting guide vanes were strategically positioned at the center and towards the circumference of the pipe within the bend area, causing an increase in flow efficiency by 78.87%. In addition, the guide vanes played a significant role in limiting turbulence effect to effective flow of the primary phase.

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Experimental and Computational Multiphase Flow
Pages 122-131
Cite this article:
Ejeh CJ, Alawwa FH, Kofi A, et al. Improving flow efficiency in curved pipes during multi-phase, immiscible fluid flow using edge-tailored guide vanes. Experimental and Computational Multiphase Flow, 2023, 5(1): 122-131. https://doi.org/10.1007/s42757-021-0121-7

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Received: 30 April 2021
Revised: 04 July 2021
Accepted: 25 July 2021
Published: 14 December 2021
© Tsinghua University Press 2021
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