An N-parallel FENE-P constitutive model and its application in large-eddy simulation of viscoelastic turbulent drag-reducing flow
KAUST DepartmentComputational Transport Phenomena Lab
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
KAUST Grant NumberBAS/1/1351-01
Online Publication Date2018-10-05
Print Publication Date2018-11
Permanent link to this recordhttp://hdl.handle.net/10754/628915
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AbstractIn this paper, an N-parallel FENE-P constitutive model based on multiple relaxation times is proposed, it can be viewed as a simplified version of the multi-mode FENE-P model under the assumption of identical deformation rate. The proposed model holds the merit of multiple relaxation times to preserve good computational accuracy but could reduce the computational cost, especially in the application of high-fidelity numerical simulation of viscoelastic turbulent drag-reducing flow. Firstly the establishment of N-parallel FENE-P model and the numerical approach to calculate the apparent viscosity are introduced. Then the proposed model is compared with the experimental data and the conventional FENE-P model in estimating rheological properties of two common-used viscoelastic fluids to validate its performance. This work is an extended version of our ICCS conference paper . To further judge the performance of the proposed FENE-P model in complex turbulent flows, the extended application of the proposed model in large-eddy simulation of viscoelastic turbulent drag-reducing channel flow is carried out.
CitationLi J, Yu B, Sun S, Sun D, Kawaguchi Y (2018) An N-parallel FENE-P constitutive model and its application in large-eddy simulation of viscoelastic turbulent drag-reducing flow. Journal of Computational Science. Available: http://dx.doi.org/10.1016/j.jocs.2018.09.016.
SponsorsThe study is supported by the 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), National Key R&D Program of China (Grant No. 2016YFE0204200), the Program of Great Wall Scholar (CIT&TCD20180313), and the Research Funding from King Abdullah University of Science and Technology (KAUST) through the grants BAS/1/1351-01.
JournalJournal of Computational Science