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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

An improved k-epsilon turbulence model for FENE-P fluids capable to reach high drag reduction regime

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Author(s):
Resende, P. R. [1] ; Afonso, A. M. [2] ; Cruz, D. O. [3]
Total Authors: 3
Affiliation:
[1] Sao Paulo State Univ Unesp, Inst Sci & Technol, Sorocaba - Brazil
[2] Univ Porto, Fac Engn, Ctr Estudos Fenomenos Transporte, Porto - Portugal
[3] Univ Fed Rio de Janeiro, COPPE, DEM, Programa Engn Mecan, Rio de Janeiro - Brazil
Total Affiliations: 3
Document type: Journal article
Source: INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW; v. 73, p. 30-41, OCT 2018.
Web of Science Citations: 0
Abstract

An improved k-epsilon turbulence model for viscoelastic fluids is developed to predict turbulent flows in complex geometries, with polymeric solutions described by the finitely extensible nonlinear elastic-Peterlin constitutive model. The k-epsilon model is tested against a wide range of direct numerical simulation data, with different rheological parameters combinations, and is capable to capture the drag reduction for all regimes of low, intermediate and high, with good performance. Two main contributions are proposed, one through the viscoelastic closures present in the turbulent kinetic energy and dissipation equations, and the other, by modifying eddy viscosity model damping function to incorporate the viscoelastic effect close to the wall, especially at the buffer layer. In addition, improvements have been made to the cross-correlations between the fluctuating components of the polymer conformation and rate of strain tensors present in the Reynolds-averaged transport equation for the conformation tensor. The main advantage is the capacity to predict all components of the tensor with good performance. (AU)

FAPESP's process: 13/01521-4 - Development of RANS turbulence models for complex geometries in turbulent viscoelastic fluid flows
Grantee:Pedro Miguel Rebelo Resende
Support Opportunities: Regular Research Grants