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Numerical Heat Transfer, Part A: Applications
An International Journal of Computation and Methodology
Volume 67, 2015 - Issue 3
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Original Articles

Mixed Convection in Power-Law Fluids from a Heated Semicircular Cylinder: Effect of Aiding Buoyancy

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Pages 330-356 | Received 14 Mar 2014, Accepted 28 May 2014, Published online: 23 Oct 2014
 

Abstract

Laminar aiding-buoyancy mixed-convection heat transfer in power-law fluids from a heated semicircular cylinder with its flat base facing the oncoming flow was studied in this work. The coupled momentum and energy equations were solved numerically over the following ranges of conditions: Richardson number (0 ≤ Ri ≤ 2), Reynolds number (0.1 ≤ Re ≤ 25), Prandtl number (1 ≤ Pr ≤ 100), and power-law index (0.3 ≤ n ≤ 1.8). Extensive results on the velocity and temperature fields are presented and discussed in terms of the streamline and isotherm contours adjacent to the heated object. Similarly, the distribution of the pressure coefficient and the local Nusselt number along the surface of the semicircular cylinder are analyzed to delineate the influence of the relevant dimensionless parameters listed above. Next, the pressure and total drag coefficient and surface average values of the Nusselt number describe the momentum and heat transfer aspects at the gross macroscopic level. Both the pressure and total drag coefficient exhibit the expected dependence on Richardson number, Prandtl number, and Reynolds number. Similarly, the average Nusselt number bears a positive dependence on each Richardson number, Prandtl number, and Reynolds number. Broadly, under otherwise identical conditions, heat transfer is augmented in shear-thinning fluids (n < 1) whereas it is somewhat impeded in shear-thickening fluids (n > 1) with reference to that in Newtonian fluids. Finally, the present numerical results of the average Nusselt number (recast in terms of the Colburn j H factor) were correlated as functions of the modified Reynolds number, Prandtl number, and power-law index thereby enabling its prediction in a new application.

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