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    Improving Nanofluid Stability and Thermal Efficiency: An Experimental Study Employing ANN Modeling for Industrial Development
    (Springer Heidelberg, 2025) Sahin, Fevzi; Kapusuz, Murat
    Nanofluids are very promising as advanced heat transfer fluids; however, their widespread industrial application is hampered by their intrinsic instability issue. This study uses artificial neural networks (ANN) to evaluate nanofluid stability while accounting for surfactant content. Experimental measurements of the thermal characteristics of the most stable nanofluids were made between 20 and 60 degrees C. ANN models were then improved to predict the experimental data, yielding high regression values: R = 0.99984 for stability, R = 0.99849 for thermal conductivity, and R = 0.99975 for viscosity. The discovered correlations provide valuable new insights into the intricate link between surfactant concentration and nanofluid stability. By developing new correlations for viscosity, thermal conductivity, and stability, this work shows how nanofluids can be used for better heat transfer applications. The thermal performance of nanofluids was evaluated using performance indicators such as Mouromtseff number (Mo) and the properties enhancement ratio (PER). The highest PER value, which hit an incredible 3.941, demonstrated the nanofluids improved heat transmission capabilities. The use of nanofluids across various flow regimes is further supported by the computation of the minimal Mo value, which were found to be 1.016 and 0.981 for laminar and turbulent flow conditions, respectively.