Entropy Generation Analysis of Alumina-Water Nanofluid Turbulent Convective Heat Transfer Using an Elliptic Blending Turbulence Model

Lei Yang^1,2, Yiyun Hu¹, Xianglong Yang^1,*
1 College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China
2 Key Laboratory for Resilient Infrastructures of Coastal Cities (MOE), Shenzhen University, Shenzhen, China
* Corresponding Author: Xianglong Yang. Email: email
(This article belongs to the Special Issue: Computational Advances in Nanofluids: Modelling, Simulations, and Applications)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.083905

Received 13 April 2026; Accepted 05 June 2026; Published online 29 June 2026

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Abstract

Accurate prediction of entropy generation in nanofluid turbulent convection is essential for optimizing thermal system efficiency, yet remains challenging due to complex near-wall phenomena and thermal property variations with temperature. This study applied an elliptic blending turbulence model (SST k-ω-φ-α) to numerically analyze entropy generation in alumina-water nanofluid flow through a uniformly heated circular tube. The model’s performance was validated using both experimental data and established heat transfer and fluid flow correlations at small wall-bulk temperature difference condition, and its superiority was rigorously evaluated against two widely adopted turbulence models (SST k-ω and realizable k-ε). Results show that the SST k-ω-φ-α model demonstrates superior accuracy, with maximum deviations from classical Bejan’s correlations of only 3.75% in heat transfer irreversibility and 1.86% in friction irreversibility, both of which are lower than the deviations of the comparative turbulence models. The SST k-ω-φ-α model was further used to analyze the entropy generation under large wall-bulk temperature difference condition. It was found that the failure of classical Bejan’s correlation is due to the dominant influence of wall temperature on property evaluations and entropy mechanisms. Under large wall-bulk temperature difference condition, total entropy generation varies non-monotonically with Reynolds number (Re), revealing an optimal flow rate that minimizes irreversibility. On the whole, the effect of nanoparticle concentration depends on flow regime, being beneficial at low Re but detrimental at high Re. This work underscores the necessity of employing advanced numerical tools which are capable of resolving detailed near-wall physics using high-fidelity turbulence modeling, especially under significant temperature gradients, for reliable performance prediction in advanced thermal systems using nanofluids.