Research Projects

This research focuses on exploring the convective heat and mass transfer characteristics of hybrid and trihybrid nanofluids within a porous medium. These nanofluids, formed by dispersing two or more nanoparticles in a base fluid, exhibit superior thermal properties compared to conventional fluids, and the Artificial Neural Networks (ANNs) will be utilized here to model, predict, and optimize the transport phenomena, aiming to enhance energy efficiency in science, engineering and industries applications, which provide insights into optimizing energy-intensive processes, such as cooling systems, solar collectors, heat exchangers, heating and cooling systems, energy conversion systems, microelectronic fields, fossil fuels, hybrid-powered engines, refrigerators, microelectronic boards circuit, centrifugal and axial blades compressors, energy production, gas turbines blades, tinning of copper wire, extrusion processes, air-conditioners, fiber technology and many more others. Ordinally, the researcher focused on pure base fluids like ethylene glycol (C₂H₆O₂), water (H₂O), and oils over various geometries in several studies, but the pure base fluids have low thermal conductivity due to this the heat and mass transfer efficiency also reduced, and the results were limited to the heat transfer enhancement. Some authors recognized this issue and moved to higher thermal conductive fluids; Choi [1] is one who introduced nanofluids which is a mixture of the base fluid and nano-size particles (1 to 100nm), that is better thermal conductive than the base fluids, and many researchers examined it in various cases in the last 2 decades where behaviour and characteristics of different nanofluids involving metals (Ag, Fe, Cu, Au), carbon nanotubes, nitride (TiC, SiC, SiN) and metallic oxides (Al₂O₃, TiO₂, CuO), but still nanofluids are not perfect thermal conductive fluids which is why higher conductive fluids are required to maximize the energy efficiency, and in very recent trend hybrid-nanofluid came in better existence as a better thermal conductive than nanofluids and more advanced are binary hybrid and trihybrid nanofluid which is what this project will target in the case of various geometries with the presence of physical effects such as: thermal radiation, heat generation and chemical reaction, magnetic field, viscous dissipation and Joule heating, etc. The flow model will be studied theoretically and computationally with the invention of some numerical techniques. The main objective of the research project is to investigate, model, and optimize the heat and mass transfer characteristics of hybrid and trihybrid nanofluids within porous media using ANNs which combines theoretical insights, computational modeling, and machine learning approaches to advance the science of nanofluid-based heat transfer in porous media.