Numerical Study of Convection Heat Transfer with Confinement Around a Square Cylinder Submerged in a Water-Based Nanofluid

Author:

Kaur Jaspinder1,Tiwari Atharva2,Ratan Jatinder Kumar1,Tiwari Anurag Kumar1

Affiliation:

1. Department of Chemical Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, 144011, Punjab, India

2. Stevenson High School, Illinois, 60069, United States

Abstract

The novelty of this work lies in the comprehensive investigation of Forced convection heat transfer a square cylinder inclined at 45° using CuO nanofluid employing a single phase approach. A heated square cylinder with constant wall temperature boundary condition, subjected to a flowing nanofluid between two parallel walls, undergoes a laminar, steady and two-dimensional flow within a Reynolds number range of 1 < Re > 40. To obtain solutions for the flow and energy transfer, a Finite Element Method (FEM) is employed to numerically solve the governing differential equations and boundary conditions. The objective of this work is to highlight the effects of Reynolds number (Re), confinement ratio (λ), volume concentration (Φ) and diameter of nanoparticles (dnp) on fluid flow and heat transfer characteristics of nanofluid. To capture the effect of Φ and dnp in nanofluid, the thermo-physical-properties of CuO nanofluid are determined experimentally. In the results, at Re = 40, a secondary separation zone (recirculation zone) is observed near the surface of the channel wall. The drag coefficient value rises as the Φ increases and the vdnp decreases, regardless of other factors such as Re and λ. Conversely, as the confinement ratio and volume fraction of nanoparticles increase, the average Nusselt number also rises, while maintaining a constant value of Re and dnp. In contrast, the size of the nanoparticles exhibits an inverse relationship with the average Nusselt number. The study contributes to the understanding of nanofluid behavior and provides practical insights for applications, supported by correlations and Artificial Neural Network predictions (Parrales et al.).

Publisher

American Scientific Publishers

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