Numerical analysis of magnetohydrodynamics in an Eyring-Powell hybrid nanofluid flow on wall jet heat and mass transfer.

The customization of hybrid nanofluids to achieve a particular and controlled growth rate of thermal transport is done to meet the needs of applications in heating and cooling systems, aerospace and automotive industries, etc. Due to the extensive applications, the aim of the current paper is to derive a numerical solution to a wall jet flow problem through a stretching surface. To study the flow problem, authors have considered a non-Newtonian Eyring-Powell hybrid nanofluid with water and CoFe2O4and TiO2nanoparticles. Furthermore, the impact of a magnetic field and irregular heat sink/source are studied. To comply with the applications of the wall jet flow, the authors have presented the numerical solution for two cases; with and without a magnetic field. The numerical solution is derived with a similarity transformation and MATLAB-based bvp4c solver. The value of skin friction for wall jet flow at the surface decreases by more than 50% when the magnetic fieldMA=0.2is present. The stream function value is higher for the wall jet flow without the magnetic field. The temperature of the flow rises with the dominant strength of the heat source parameters. The results of this investigation will be beneficial to various applications that utilize the applications of a wall jet, such as in car defrosters, spray paint drying for vehicles or houses, cooling structures for the CPU of high-processor laptops, sluice gate flows, and cooling jets over turbo-machinery components, etc.

Development of machine learning algorithm for assessment of heat transfer of ternary hybrid nanofluid flow towards three different geometries: Case of artificial neural network.

The focus of this paper revolves around the examination of flow of ternary hybrid nanofluid, specifically the Al2O3-Cu-CNT/water mixture, with buoyancy effect, across three distinct geometries: a wedge, a flat plate, and a cone. The study takes into account the presence of quadratic thermal radiation and heat source/sink of non-uniform nature. To develop the model, the Cattaneo-Christov theory is utilized. The equations governing the flow are solved by applying similarity transformations and employing the "bvp4c function in MATLAB" for numerical analysis and solution. Conventional methods for conducting parametric studies often face challenges in producing significant conclusions owing to the inherent complex form of the model and the method involved. To address the aforementioned issue, this paper explores the potential of machine learning methods to foresee the conduct of the flow characterized by multiple interconnected parameters. By utilizing simulated data, an artificial neural network is trained using the Levenberg-Marquardt algorithm to learn and comprehend the underlying patterns. Subsequently, the trained neural network is employed to estimate the Nusselt number on the surfaces of all three geometries. This approach offers a promising alternative to traditional parametric studies, enabling more precise predictions and insights into the behavior of complex systems. The Nusselt number is highest for THNF flow over the cone. The mean squared error (MSE) values for the ANN algorithm, across all analyzed cases, range from 0 to 0.03972. The findings contribute to an improved understanding of the characteristics and dynamics of ternary hybrid nanofluid flow in various geometries, assisting in the design and optimization of heat transfer systems involving such fluids.