Study on an N-Parallel FENE-P Constitutive Model Based on Multiple Relaxation Times for Viscoelastic Fluid
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Study on an N-parallel FENE-P constitutive model.pdf
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Conference PaperKAUST Department
Computational Transport Phenomena LabEarth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Date
2018-06-12Online Publication Date
2018-06-12Print Publication Date
2018Permanent link to this record
http://hdl.handle.net/10754/628318
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An 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.Citation
Li 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.Sponsors
The 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).Publisher
Springer NatureConference/Event name
18th International Conference on Computational Science, ICCS 2018Additional Links
https://link.springer.com/chapter/10.1007%2F978-3-319-93713-7_57ae974a485f413a2113503eed53cd6c53
10.1007/978-3-319-93713-7_57