Chandralekha Mahanta

This study thoroughly examines a complex fluid flow problem, integrating entropy optimization, heat source/sink effects, and radiation. Considering slip and convective boundary conditions, the study focuses on a comprehensive numerical investigation of hybrid nanofluids' heat transfer characteristics within convective systems. The hybrid nanofluids, composed of H ₂ O as the base fluid and nanoparticles such as Fe ₃ O ₄ and Ag, exhibit unique thermal properties that significantly enhance heat transfer efficiency. The magnetite and silver nanoparticles contribute stability and a substantial increase in thermal performance. The incorporation of velocity slip at the boundary, along with convective boundary conditions, adds realism to the analysis. The utilization of the shooting‐based Runge–Kutta Fehlberg fourth‐order method enables a thorough numerical exploration to tackle the approximate solutions of the proposed fluid model. The insights gleaned from this study contribute to fundamental knowledge of fluid mechanics and hold practical implications for process optimization. The response surface methodology (RSM) is utilized to optimize the heat transfer efficiency. The entropy generation analysis underscores the potential for reduced irreversibility when incorporating slip boundary conditions in convective systems with hybrid nanofluids, showcasing opportunities for improved thermal management applications. A strong Brinkmann number leads to greater energy dissipation and increased entropy production. Also, a higher Biot number escalates the temperature profile.

This study numerically examines the optimization of entropy in uranium dioxide (UO2)/polyethylene glycol–water (PEG–H2O) 50%–50% mixture of nanofluid flowing between a cone and a disk with porous media. The cone rotates over the expanding disk, and the nanofluid flows between the gap of the cone and the disk with the rule of the Darcy–Brinkmann model. This analysis is performed under stable flow conditions and incorporates a uniform magnetized field and trio dissipation, i.e., Darcy, viscous, and Joule dissipation. The model further conducted exothermic/endothermic reactions with waste discharge conditions to accumulate knowledge of the mass transmission process throughout the system. The bvp4c code is utilized for the graphical outcomes and, subsequently, employs the Runge–Kutta fourth-order to get the solutions. To enhance the computational efficiency of the heat transfer rate coefficient, a supervised model of an artificial neural network is employed. Comprehensive simulations yielded extremely low error (mean square error ≤ 1.9467 × 10−9) and an almost ideal regression coefficient (R ≈ 1). The study concludes that the high Reynolds number and Eckert number lead to better entropy generation of the nanofluid model.

The proposed investigation leads toward the discussion on the dissipative heat on the conducting flow of hybrid nanofluid over an exponentially expanding/contracting plate. The sheet is considered to be permeable therefore, the impact of suction/injection is vital throughout the discussion. The model of hybrid nanofluid is adopted with various thermophysical parameters likely the conductivity and the viscosity properties. By employing valid similarity transformations, the governing set of equations with boundary conditions is converted to the set of non‐linear ODEs. The reduced system of equations is explicated using the bvp4c solver in MATLAB. The numerical outcomes are deliberated and graphically presented in momentum and temperature profiles. The enhancement of the nanofluid heat transfer rate is achieved through the utilization of Response Surface Methodology (RSM). Further, the stability analysis is presented to validate the convergence of the set of time‐dependent profiles for the various contributing constraints. From RSM, the combination of a high level of , high level of are conducive to good heat transfer. Also, high level of and low level of slip parameter value (), high level of and low level emerges as the most conducive operating condition that would maximize the rate of transportation of heat.