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LES Investigation of Drag-Reducing Mechanism of Turbulent Channel Flow with Surfactant Additives

Jingfa Li1, Bo Yu1,*, Qianqian Shao2, Dongliang Sun1

1 School of Mechanical Engineering, Beijing Key Laboratory of Pipeline Critical Technology and Equipment for Deepwater Oil & Gas Development, Beijing Institute of Petrochemical Technology, Beijing, 102617, China
2 Guangdong University of Petrochemical Technology, Maoming, 525000, China

* Corresponding Author: Bo Yu. Email: email

Computer Modeling in Engineering & Sciences 2020, 125(2), 541-563. https://doi.org/10.32604/cmes.2020.011835

Abstract

In this work, the drag-reducing mechanism of high-Reynoldsnumber turbulent channel flow with surfactant additives is investigated by using large eddy simulation (LES) method. An N-parallel finitely extensible nonlinear elastic model with Peterlin’s approximation (FENE-P) is used to describe the rheological behaviors of non-Newtonian fluid with surfactant. To close the filtered LES equations, a hybrid subgrid scale (SGS) model coupling the spatial filter and temporal filter is applied to compute the subgrid stress and other subfilter terms. The finite difference method and projection algorithm are adopted to solve the LES governing equations. To validate the correctness of our LES method and in-house code, the particle image velocimetry (PIV) experiment is carried out and representative measured results are compared with LES results in detail. Then the flow characteristics and drag-reducing mechanism of turbulent channel flow with surfactant are investigated from the perspective of drag reduction rate, mean velocity, fluctuation of deformation rate, shear stress, transport and dissipation of turbulent kinetic energy, and turbulent coherent structures. This research can shed a light on the application of turbulent drag reduction technique in district heating, petroleum transport, etc.

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Li, J., Yu, B., Shao, Q., Sun, D. (2020). LES Investigation of Drag-Reducing Mechanism of Turbulent Channel Flow with Surfactant Additives. CMES-Computer Modeling in Engineering & Sciences, 125(2), 541–563. https://doi.org/10.32604/cmes.2020.011835



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