LES Study on High Reynolds Turbulent Drag-Reducing Flow of Viscoelastic Fluids Based on Multiple Relaxation Times Constitutive Model and Mixed Subgrid-Scale Model
KAUST DepartmentComputational Transport Phenomena Lab
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Online Publication Date2018-06-12
Print Publication Date2018
Permanent link to this recordhttp://hdl.handle.net/10754/628317
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AbstractDue to complicated rheological behaviors and elastic effect of viscoelastic fluids, only a handful of literatures reporting the large-eddy simulation (LES) studies on turbulent drag-reduction (DR) mechanism of viscoelastic fluids. In addition, these few studies are limited within the low Reynolds number situations. In this paper, LES approach is applied to further study the flow characteristics and DR mechanism of high Reynolds viscoelastic turbulent drag-reducing flow. To improve the accuracy of LES, an N-parallel FENE-P constitutive model based on multiple relaxation times and an improved mixed subgrid-scale (SGS) model are both utilized. DR rate and velocity fluctuations under different calculation parameters are analyzed. Contributions of different shear stresses on frictional resistance coefficient, and turbulent coherent structures which are closely related to turbulent burst events are investigated in details to further reveal the DR mechanism of high Reynolds viscoelastic turbulent drag-reducing flow. Especially, the different phenomena and results between high Reynolds and low Reynolds turbulent flows are addressed. This study is expected to provide a beneficial guidance to the engineering application of turbulent DR technology.
CitationLi J, Yu B, Zhang X, Sun S, Sun D, et al. (2018) LES Study on High Reynolds Turbulent Drag-Reducing Flow of Viscoelastic Fluids Based on Multiple Relaxation Times Constitutive Model and Mixed Subgrid-Scale Model. Computational Science – ICCS 2018: 174–188. Available: http://dx.doi.org/10.1007/978-3-319-93713-7_14.
SponsorsThe study is supported by National Natural Science Foundation of China (No. 51636006), project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges under Beijing Municipality (No. IDHT20170507) and the Program of Great Wall Scholar (CIT&TCD20180313).
Conference/Event name18th International Conference on Computational Science, ICCS 2018