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Experiment and Artificial Neural Network Prediction of Thermal Conductivity and Viscosity for Alumina-Water Nanofluids.

ABSTRACT: To effectively predict the thermal conductivity and viscosity of alumina (Al₂O₃)-water nanofluids, an artificial neural network (ANN) approach was investigated in the present study. Firstly, using a two-step method, four Al₂O₃-water nanofluids were prepared respectively by dispersing different volume fractions (1.31%, 2.72%, 4.25%, and 5.92%) of nanoparticles with the average diameter of 30 nm. On this basis, the thermal conductivity and viscosity of the above nanofluids were analyzed experimentally under various temperatures ranging from 296 to 313 K. Then a radial basis function (RBF) neural network was constructed to predict the thermal conductivity and viscosity of Al₂O₃-water nanofluids as a function of nanoparticle volume fraction and temperature. The experimental results showed that both nanoparticle volume fraction and temperature could enhance the thermal conductivity of Al₂O₃-water nanofluids. However, the viscosity only depended strongly on Al₂O₃ nanoparticle volume fraction and was increased slightly by changing temperature. In addition, the comparative analysis revealed that the RBF neural network had an excellent ability to predict the thermal conductivity and viscosity of Al₂O₃-water nanofluids with the mean absolute percent errors of 0.5177% and 0.5618%, respectively. This demonstrated that the ANN provided an effective way to predict the thermophysical properties of nanofluids with limited experimental data.

SUBMITTER: Zhao N

PROVIDER: S-EPMC5459072 | biostudies-other | 2017 May

REPOSITORIES: biostudies-other

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Project description:Developing stable nanofluids and improving their thermo-physical properties are highly important in heat transfer applications. In the present work, the stability, thermal conductivity, and rheological properties of tungsten disulphide (WS2) nanoparticles (NPs) with ethylene glycol (EG) were profoundly examined using a particle size analyzer, zeta-sizer, thermal property analyzer, rheometer, and pH measuring system. WS2 NPs were characterized by various techniques, such as XRD (X-Ray Diffraction), FTIR (Fourier Transform Infrared Spectroscopy), FESEM (Field emission scanning electron microscopy), and high-resolution transmission electron microscopy (HRTEM). The nanofluids were obtained with the two-step method by employing three volume concentrations (0.005%, 0.01%, and 0.02%) of WS2. The influence of different surfactants (Sodium dodecyl sulphate (SDS), Sodium dodecylbenzenesulfonate (SDBS), Cetyltrimethylammonium bromide (CTAB)) with various volume concentrations (0.05-2%) on the measured properties has also been evaluated. Pristine WS2/EG nanofluids exhibit low zeta potential values, i.e., -7.9 mV, -9.3 mV, and -5 mV, corresponding to 0.005%, 0.01%, and 0.02% nanofluid, respectively. However, the zeta potential surpassed the threshold (±30 mV) and the maximum values reached of -52 mV, -45 mV, and 42 mV for SDS, SDBS, and CTAB-containing nanofluids. This showed the successful adsorption of surfactants onto WS2, which was also observed through the increased agglomerate size of up to 1720 nm. Concurrently, particularly for 0.05% SDS with 0.005% WS2, thermal conductivity was enhanced by up to 4.5%, with a corresponding decrease in viscosity of up to 10.5% in a temperature range of (25-70 °C), as compared to EG. Conversely, the viscoelastic analysis has indicated considerable yield stress due to the presence of surfactants, while the pristine nanofluids exhibited enhanced fluidity over the entire tested deformation range. The shear flow behavior showed a transition from a non-Newtonian to a Newtonian fluid at a low shear rate of 10 s-1. Besides this, the temperature sweep analysis has shown a viscosity reduction in a range of temperatures (25-70 °C), with an indication of a critical temperature limit. However, owing to an anomalous reduction in the dynamic viscosity of up to 10.5% and an enhancement in the thermal conductivity of up to 6.9%, WS2/EG nanofluids could be considered as a potential candidate for heat transfer applications.

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Experiment and Artificial Neural Network Prediction of Thermal Conductivity and Viscosity for Alumina-Water Nanofluids.

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