Study on an N-Parallel FENE-P Constitutive Model Based on Multiple Relaxation Times for Viscoelastic Fluid
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/10754/628318
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AbstractAn N-parallel FENE-P constitutive model based on multiple relaxation times is proposed in this paper, which aims at accurately describing the apparent viscosity of viscoelastic fluid. The establishment of N-parallel FENE-P constitutive model and the numerical approach to calculate the apparent viscosity are presented in detail, respectively. To validate the performance of the proposed constitutive model, it is compared with the conventional FENE-P constitutive model (It only has single relaxation time) in estimating the apparent viscosity of two common viscoelastic fluids: polymer and surfactant solutions. The comparative results indicate the N-parallel FENE-P constitutive model can represent the apparent viscosity of polymer solutions more accurate than the traditional model in the whole range of shear rate (0.1 s–1000 s), and the advantage is more noteworthy especially when the shear rate is higher (10 s–1000 s). Despite both the proposed model and the traditional model can’t capture the interesting shear thickening behavior of surfactant solutions, the proposed constitutive model still possesses advantage over the traditional one in depicting the apparent viscosity and first normal stress difference. In addition, the N-parallel FENE-P constitutive model demonstrates a better applicability and favorable adjustability of the model parameters.
CitationLi J, Yu B, Sun S, Sun D (2018) Study on an N-Parallel FENE-P Constitutive Model Based on Multiple Relaxation Times for Viscoelastic Fluid. Computational Science – ICCS 2018: 610–623. Available: http://dx.doi.org/10.1007/978-3-319-93713-7_57.
SponsorsThe authors thank for support of 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) and the Program of Great Wall Scholar (CIT&TCD20180313).
Conference/Event name18th International Conference on Computational Science, ICCS 2018