4.7 Article

Computational analysis of heat transfer in mixed convective flow of CNTs with entropy optimization by a curved stretching sheet

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.icheatmasstransfer.2020.104881

Keywords

Curved stretching surface; CNTs/blood; Joule heating; Bejan number; Entropy generation; Mixed convection and viscous dissipation

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Here heat transfer analysis in the mixed convective flow of blood-based carbon nanotubes due to a curved stretching sheet is studied. Single and multiple walls carbon nanotubes are taken as nanoparticles and blood as a base liquid. The nanoparticles transportation mechanism is achieved by using a theoretical model modified by Xue. Joule heating and dissipation effects in energy expression are incorporated. Physical characteristics of irreversibility in the thermodynamical system are studied. Entropy optimization expression is developed through thermodynamics second law. Additionally, the examination of entropy rate can be favorable for better consideration of the thermodynamical systems. Furthermore, entropy is used for the measurement of energy quality in a thermal system. The leading equation is established in a curvilinear coordinate system. The nonlinear PDEs are reduced by using compatible variables. The nonlinear ODEs are solved numerically by ND-solve method. Variation of entropy rate, Bejan number, velocity and temperature versus several engineering parameters for both CNTs are deliberated. The computational outcomes of heat transfer rate and velocity gradient for both CNTs are scrutinized in tabulated form. Velocity has a reverse trend versus curvature and magnetic parameters for both carbon nanotubes. For a higher approximation of Eckert number the temperature rises for both CNTs. Entropy generation is amplified for both SWCNTs and MWCNTs via Brinkman number. The surface drag force is augmented for the magnetic parameter for both CNTs. However, the skin friction of MWCNTs is greater than SWCNTs (which creates a higher drag force at the surface). Temperature gradient is improved versus curvature parameter. Here we noticed that augmentation of thermal conduction is more prominent for SWCNTs than MWCNTs.

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