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Improvement of drag reduction prediction in viscoelastic pipe flows using proper low-Reynolds k-ε turbulence models

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  • Rasti, Ehsan
  • Talebi, Farhad
  • Mazaheri, Kiumars

Abstract

Developing proper turbulence models in order to predict flow characteristics of viscoelastic drag-reducing fluids has become as a considerable portion of non-Newtonian flows researches. In this numerical study, three different low-Reynolds-number k-ε models namely, Launder–Sharma, Lam–Bremhorst and Malin are used based on the adopted viscoelastic turbulence closure of Cruz et al. (2004). Simulation results for friction factor, mean axial velocity, turbulent kinetic energy and Reynolds shear stress are obtained for three aqueous polymer solutions (0.3% carboxymethyl cellulose, 0.2% xanthan gum and 0.2% polyacrylamide) and validated against the corresponding experimental data. While a good agreement exists for the bulk parameters like drag reduction (DR) or, equivalently, the friction factor, some discrepancies are observed in other simulation results of turbulence characteristics, in particular for situations with higher DR levels. The Malin’s model which accounts for the shear-thinning property of the polymeric solutions, leads to the closest results for carboxymethyl cellulose and xanthan gum cases, while for polyacrylamide with the lowest power index n, the friction factor is larger than other simulation results and an increasing difference from the experimental data is observed.

Suggested Citation

  • Rasti, Ehsan & Talebi, Farhad & Mazaheri, Kiumars, 2019. "Improvement of drag reduction prediction in viscoelastic pipe flows using proper low-Reynolds k-ε turbulence models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 412-422.
    • Handle: RePEc:eee:phsmap:v:516:y:2019:i:c:p:412-422
    DOI: 10.1016/j.physa.2018.10.009
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    1. Ahmadi Balootaki, Azam & Karimipour, Arash & Toghraie, Davood, 2018. "Nano scale lattice Boltzmann method to simulate the mixed convection heat transfer of air in a lid-driven cavity with an endothermic obstacle inside," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 681-701.
    2. Hemmat Esfe, Mohammad & Kamyab, Mohammad Hassan & Afrand, Masoud & Amiri, Mahmoud Kiannejad, 2018. "Using artificial neural network for investigating of concurrent effects of multi-walled carbon nanotubes and alumina nanoparticles on the viscosity of 10W-40 engine oil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 610-624.
    3. Nemati, Maedeh & Shateri Najaf Abady, Ali Reza & Toghraie, Davood & Karimipour, Arash, 2018. "Numerical investigation of the pseudopotential lattice Boltzmann modeling of liquid–vapor for multi-phase flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 65-77.
    4. Karimipour, Arash & Hemmat Esfe, Mohammad & Safaei, Mohammad Reza & Toghraie Semiromi, Davood & Jafari, Saeed & Kazi, S.N., 2014. "Mixed convection of copper–water nanofluid in a shallow inclined lid driven cavity using the lattice Boltzmann method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 150-168.
    5. Goodarzi, Marjan & D’Orazio, Annunziata & Keshavarzi, Ahmad & Mousavi, Sayedali & Karimipour, Arash, 2018. "Develop the nano scale method of lattice Boltzmann to predict the fluid flow and heat transfer of air in the inclined lid driven cavity with a large heat source inside, Two case studies: Pure natural ," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 210-233.
    6. Toghaniyan, Abolfazl & Zarringhalam, Majid & Akbari, Omid Ali & Sheikh Shabani, Gholamreza Ahmadi & Toghraie, Davood, 2018. "Application of lattice Boltzmann method and spinodal decomposition phenomenon for simulating two-phase thermal flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 673-689.
    7. Hemmat Esfe, Mohammad & Reiszadeh, Mahdi & Esfandeh, Saeed & Afrand, Masoud, 2018. "Optimization of MWCNTs (10%) – Al2O3 (90%)/5W50 nanofluid viscosity using experimental data and artificial neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 731-744.
    8. Hemmat Esfe, Mohammad & Rostamian, Hossein & Esfandeh, Saeed & Afrand, Masoud, 2018. "Modeling and prediction of rheological behavior of Al2O3-MWCNT/5W50 hybrid nano-lubricant by artificial neural network using experimental data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 625-634.
    9. Safaei, Mohammad Reza & Karimipour, Arash & Abdollahi, Ali & Nguyen, Truong Khang, 2018. "The investigation of thermal radiation and free convection heat transfer mechanisms of nanofluid inside a shallow cavity by lattice Boltzmann method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 515-535.
    10. Jourabian, Mahmoud & Darzi, A. Ali Rabienataj & Toghraie, Davood & Akbari, Omid ali, 2018. "Melting process in porous media around two hot cylinders: Numerical study using the lattice Boltzmann method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 316-335.
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