LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

A turbulent duct flow investigation of drag-reducing viscoelastic FENE-P fluids based on different low-Reynolds-number models

Photo from wikipedia

Abstract Drag reduction of viscoelastic fluids under turbulent flow regime is an important phenomenon and well observed by many researchers. In this paper, turbulent flow characteristics under drag reduction condition… Click to show full abstract

Abstract Drag reduction of viscoelastic fluids under turbulent flow regime is an important phenomenon and well observed by many researchers. In this paper, turbulent flow characteristics under drag reduction condition are compared using four different low-Reynolds number k- e models namely, Lam–Bremhorst, Launder–Sharma, Nagano–Hishida and Chien. The viscoelastic turbulence closure of Resende et al. (2011) is adopted. Time-averaged momentum and rheological constitutive equations based on finitely extensible nonlinear elastic-Peterlin (FENE-P) model are used and a polymeric contribution to the eddy viscosity equation is considered. The simulations are conducted for two sets of rheological parameters identified by Re τ 0 = 395, β = 0 . 9 , L2 = 900 and We τ 0 = 25, 100 corresponding to drag reductions of 18% and 37%. Validation against DNS data is carried out for profiles of mean velocity, viscoelastic stress tensor, turbulent kinetic energy and its rate of dissipation. The differences are mainly limited to the viscous and buffer layer regions. While Nagano–Hishida and Chien models overpredict the DNS drag reduction results in both cases, the two other models underpredict them. The Lam–Bremhorst has the most accurate drag reduction results as a consequence of better capturing the near-wall effects.

Keywords: reynolds number; drag reduction; different low; number models; low reynolds; flow

Journal Title: Physica A: Statistical Mechanics and its Applications
Year Published: 2019

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.