Hybrid Nanofluid Heat Transfer and Thermal Flow Modeling
This community focuses on the numerical simulation and experimental analysis of heat transfer in fluids containing suspended nanoparticles, specifically to enhance thermal performance in solar collectors, heat exchangers, and industrial cooling systems.
The work centers on "hybrid nanofluids" and "ternary hybrid" mixtures, where nanoparticles such as copper oxide, aluminum oxide, and carbon nanotubes are dispersed in base fluids like water or ethylene glycol. Recurring themes include "nanofluid flow," "heat transfer," and "thermal conductivity," often analyzed within "porous medium" environments or over "stretching sheet" geometries. The research frequently employs "magnetic field" effects (MHD) and "entropy generation" to model complex fluid dynamics. Applications are explicitly named in titles, including "solar collector" efficiency, "pool boiling" enhancement, and "heat exchanger" performance. Methodologies rely heavily on "numerical" solutions, "finite element" analysis, and increasingly, "neural network" and "artificial neural" network approaches for prediction and optimization.
The largest share of the community's output is found in copper research, accounting for 0.5% of all tracked copper studies, with aluminium and silver also contributing significant portions at 0.5% and 0.4% respectively. Copper also leads in raw paper count with 1,496 papers, while carbon follows with 1,387 papers.
The community comprises 11,177 papers, publishing most frequently in the International Journal of Heat and Mass Transfer, Journal of Thermal Analysis and Calorimetry, and Case Studies in Thermal Engineering.
Recent work continues to focus on optimizing hybrid nanofluid performance in solar air heaters and photovoltaic-thermal systems, with a growing emphasis on using neural networks to predict thermal behavior in complex flows, including non-Newtonian fluids and bioconvection scenarios.