Modeling of thermal conductivity and density of alumina/silica in water hybrid nanocolloid by the application of Artificial Neural Networks |
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Authors: | Sathishkumar Kannaiyan Chitra Boobalan Fedal Castro Nagarajan Srinivas Sivaraman |
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Affiliation: | 1.Department of Chemical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai 603 110, India;2.Department of Mechanical Engineering, Aarupadai Veedu Institute of Technology, Paiyanoor, India |
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Abstract: | In this research work, the thermal conductivity and density of alumina/silica (Al2O3/SiO2) in water hybrid nanofluids at different temperatures and volume concentrations have been modeled using the artificial neural networks (ANN). The nanocolloid involved in the study was synthesized by the two-step method and characterized by XRD, TEM, SEM-EDX and zeta potential analysis. The properties of the synthesized nanofluid were measured at various volume concentrations (0.05%, 0.1% and 0.2%) and temperatures (20 to 60℃). Established on the observational data and ANN, the optimum neural structure was suggested for predicting the thermal conductivity and density of the hybrid nanofluid as a function of temperature and solid volume concentrations. The results indicate that a neural network with 2 hidden layers and 10 neurons have the lowest error and a highest fitting coefficient of thermal conductivity, whereas in the case of density, the structure with 1 hidden layer consisting of 4 neurons proved to be the optimal structure. |
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Keywords: | Thermal conductivity Modeling hybrid nanocolloids ANN thermal energy |
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