Turbulent drag reduction in channel flow with viscosity stratified fluids

2018 ◽  
Vol 176 ◽  
pp. 260-265 ◽  
Author(s):  
Somayeh Ahmadi ◽  
Alessio Roccon ◽  
Francesco Zonta ◽  
Alfredo Soldati
Keyword(s):  
Drag Reduction ◽  
Channel Flow ◽  
Stratified Fluids ◽  
Turbulent Drag Reduction ◽  
Turbulent Drag

Simulation Results for Micro-Bubbles and Turbulent Drag Reduction (Keynote)

2003 ◽  
Author(s):  
M. R. Maxey ◽  
J. Xu ◽  
S. Dong ◽  
G. E. Karniadakis
Keyword(s):  
Drag Reduction ◽  
Numerical Simulations ◽  
Channel Flow ◽  
Turbulent Channel Flow ◽  
Turbulent Drag Reduction ◽  
Void Fractions ◽  
Turbulent Drag ◽  
Simulation Results ◽  
Small Bubbles ◽  
Near Wall

A series of numerical simulations of small bubbles seeded in a turbulent channel flow have been made at average void fractions up to 10%. Initial near-wall seeding in general leads to a transient reduction in drag while smaller bubbles are more effective in producing sustained drag reduction.

On the mechanism of turbulent drag reduction with super-hydrophobic surfaces

2015 ◽  
Vol 773 ◽  
Author(s):  
Amirreza Rastegari ◽  
Rayhaneh Akhavan
Keyword(s):  
Reynolds Number ◽  
Surface Layer ◽  
Drag Reduction ◽  
Channel Flow ◽  
Free Fraction ◽  
Turbulent Channel Flow ◽  
Governing Equations ◽  
Turbulent Drag Reduction ◽  
Turbulent Drag ◽  
Effective Slip

The mechanism of turbulent drag reduction (DR) with super-hydrophobic (SH) surfaces is investigated by direct numerical simulation (DNS) and analysis of the governing equations in channel flow. The DNS studies were performed using lattice Boltzmann methods in channels with ‘idealized’ SH surfaces on both walls, comprised of longitudinal micro-grooves (MG), transverse MG, or micro-posts. DRs of $5\,\%$ to $83\,\%$, $-4\,\%$ to $20\,\%$, and $14\,\%$ to $81\,\%$ were realized in DNS with longitudinal MG, transverse MG, and micro-posts, respectively. By mathematical analysis of the governing equations, it is shown that, in SH channel flows with any periodic SH micro-pattern on the walls, the magnitude of DR can be expressed as $DR=U_{slip}/U_{bulk}+O({\it\varepsilon})$, where the first term represents the DR resulting from the effective slip on the walls, and the second term represents the DR or drag increase (DI) resulting from modifications to the turbulence dynamics and any secondary mean flows established in the SH channel compared to a channel flow with no-slip walls at the same bulk Reynolds number as the SH channel. Comparison of this expression to DNS results shows that, with all SH surface micro-patterns studied, between 80 % and 100 % of the DR in turbulent flow arises from the effective slip on the walls. Modifications to the turbulence dynamics contribute no more than 20 % of the total DR with longitudinal MG or micro-posts of high shear-free fraction (SFF), and a DI with transverse MG or micro-posts of moderate SFF. The effect of the SH surface on the normalized dynamics of turbulence is found to be small in all cases, and confined to additional production of turbulence kinetic energy (TKE) within a thin ‘surface layer’ of thickness of the order of the width of surface micro-indentations. Outside of this ‘surface layer’, the normalized dynamics of turbulence proceeds as in a turbulent channel flow with no-slip walls at the friction Reynolds number of the SH channel flow.

scholarly journals Prediction of Drag Reduction Rate in Turbulent Channel Flow Based on BP Neural Network

2020 ◽  
Vol 194 ◽  
pp. 05049
Author(s):  
Yuchen Cao ◽  
Yongwen Yang
Keyword(s):  
Neural Network ◽  
Drag Reduction ◽  
Polymer Solution ◽  
Channel Flow ◽  
Bp Neural Network ◽  
Reduction Rate ◽  
Turbulent Channel Flow ◽  
Solution Concentration ◽  
Turbulent Drag Reduction ◽  
Turbulent Drag

The technology of turbulent drag reduction by viscoelastic additives cannot be widely applied in practical engineering due to the difficulty in judging the effect of drag reduction. To solve this problem, the experiment of drag-reducing channel flow of polymer solution was carried out based on the comprehensive analysis of the factors affecting the drag reduction rate. Abundant drag reduction rate data were obtained. A three-layer BP neural network prediction model was established with polymer solution concentration, Reynolds number and injection flow rate as input parameters. Based on the test results, the prediction accuracy on drag reduction rate of the model was analysed. The prediction and model validation of drag reduction rate are carried out further according to the historical data in literature. The results show that the predicted drag reduction rate of BP neural network is close to the real drag reduction rate in the drag-reducing flow of polymer solution. The prediction is with high accuracy and with good generalization ability. It is expected to be applied to practical projects and to promote the development of turbulent drag reduction technology by additives.

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