Comparative Analysis of Mixed Convective Heat Transfer of Cu-Water Nano?uid in Various Geometries Using Einstein and Corcione Models

Dr Banduvula Sailaja

Abstract: This study proposes an integrated computational framework for the analysis and optimization of mixed convective heat transfer in Cu-water nano?uid over four geometrical configurations, namely a vertical plate, inclined plate, stretching plate, and convective boundary plate, under the combined in?uence of magnetic field and internal heat generation e?ects. The e?ective thermophysical properties of the nano?uid are evaluated using Einstein's viscosity model and Corcione's thermal conductivity correlation. The governing continuity, momentum, and energy equations are transformed into nonlinear ordinary di?erential equations via similarity transformations and subsequently solved using a sixth-order Runge-Kutta method coupled with the shooting technique. The numerical solutions generated over a wide range of operating conditions are employed to construct a comprehensive thermal performance database comprising the Nusselt number, skin-friction coefficient, entropy generation number, and Bejan number. A multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is implemented to simultaneously maximize heat transfer enhancement while minimizing frictional and thermodynamic irreversibilities. Furthermore, an Extreme Gradient Boosting (XGBoost) model is developed to provide rapid and highly accurate prediction of the thermal performance metrics. Entropy generation analysis is further performed to quantify thermodynamic irreversibility and assess the overall efficiency of the proposed configurations. Comparative investigations reveal that the stretching plate configuration exhibits the most favorable thermal characteristics, achieving superior heat transfer enhancement, minimum entropy generation, and enhanced thermodynamic efficiency among all considered geometries. The proposed RK-6-NSGA-II-XGBoost framework establishes a robust and intelligent methodology for the prediction, optimization, and design of nano?uid-based thermal systems, with significant potential for applications in heat exchangers, solar thermal collectors, electronic cooling systems, and next-generation thermal management technologies.

Keywords: Cu-water nano?uid, mixed convection, Runge?Kutta method, shooting technique, NSGA-II, XGBoost, entropy generation, thermal optimization, heat transfer enhancement

How to Cite?: Dr Banduvula Sailaja, "Comparative Analysis of Mixed Convective Heat Transfer of Cu-Water Nano?uid in Various Geometries Using Einstein and Corcione Models", Volume 15 Issue 6, June 2026, International Journal of Science and Research (IJSR), Pages: 1715-1722, https://www.ijsr.net/getabstract.php?paperid=SR26627111054, DOI: https://dx.doi.org/10.21275/SR26627111054

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