Colak, Andac BaturBayrak, Mustafa2024-11-072024-11-0720241388-61501588-2926https://doi.org/10.1007/s10973-024-13617-3https://hdl.handle.net/11480/14861The research aimed to experimentally test the thermal conductivity of five distinct Al2O3-Cu/water hybrid nanofluids. These nanofluids were generated at volumetric concentrations of 0.0125, 0.025, 0.05, 0.1, and 0.2. The measurements were conducted within a temperature range of 10-65 degrees C. The primary objective of this research is to tackle the insufficient empirical data on hybrid nanofluids and establish a dependable artificial neural network model for forecasting their thermal conductivity. A multilayer perceptron feed forward back propagation artificial neural network has been created using the acquired experimental thermal conductivity data. The experimental thermal conductivity data have been compared with four commonly used mathematical correlations and the outputs of an artificial neural network. The findings demonstrated that the constructed artificial neural network accurately forecasted the thermal conductivity of the Al2O3-Cu/water hybrid nanofluid, with an average deviation of just 0.4%. Nevertheless, Maxwell's mathematical correlation proved to be the most accurate model in predicting the experimental findings, with an average error margin of just 0.08%.eninfo:eu-repo/semantics/closedAccessAl2O3-CuNanofluidThermal conductivityArtificial neural networkHeat transferArticle10.1007/s10973-024-13617-32-s2.0-85205348534Q1WOS:001325460000001N/A