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 /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.

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 CoFeOand TiOnanoparticles.