Using finite volume method for simulating the natural convective heat transfer of nano-fluid flow inside an inclined enclosure with conductive walls in the presence of a constant temperature heat source

In the present work, natural convective heat transfer of water/Al2O3 nano-fluid in an inclined square enclosure is investigated. The side walls of the cavity are cold and the upper and lower ones are insulated. A wall with a thermal-conductivity of 100 and a thickness of 0.5 is located on the cold walls. Moreover, there is a constant temperature heat source in the center of the enclosure. The enclosure is located under the influence of an inclined magnetic field (MF). The governing equations were solved using the finite volume method (FVM) and solved using the SIMPLE algorithm. The results show that the heat transfer rate intensifies up to 3.11 times with intensifying the Rayleigh number (Ra). The maximum heat transfer occurred at weak magnetic fields. By augmenting the angle of the enclosure, the heat transfer rate on the right and left walls intensifies by 33% and declines by 55%, respectively. The heat transfer rate on the right wall intensifies by 14% by augmenting the angle of the MF. The addition of nano-additives also results in intensification in the heat transfer rate

Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting

The neural network is a technique to reduce cost and time that can be a good alternative to practical testing. This technique, which has become more important with the advancement of computer science, can also be used to predict the properties of nanofluids. To prove this claim, in this research, an optimal artificial neural network (ANN) was designed to evaluation the thermal conductivity enhancement of the SWCNTs/EG-water nanofluid using experimental data. For this goal, reported experimental enhancement for various concentrations and temperatures were employed. 35 measured data obtained from experiments have been applied to utilize ANN modeling. 80% data were chosen for network training and the remaining data were adopted for network testing. Based on the minimum mean square error (MSE), ANN model with two hidden layers and 4 neurons in each layer was selected. In addition, a new correlation was presented for predicting the thermal conductivity enhancement. Comparative results showed ANN model can forecast the thermal conductivity enhancement of nanofluids appropriately. © 2019 Elsevier B.V

Prediction of rheological behavior of a new hybrid nanofluid consists of copper oxide and multi wall carbon nanotubes suspended in a mixture of water and ethylene glycol using curve-fitting on experimental data

In the current study incorporating nanoparticles of CuO/MWCNTs into the base fluid water/EG (70:30) has been done to investigate the nanofluid rheological behavior in the temperature of 20–60 °C. Employing two-step method, homogeneous and stable samples of nanofluid at various nanoparticles volume fractions (0.025, 0.05, 0.1, 0.25, 0.5 and 1%) have been prepared. Based on the experiment results, base fluid (water/EG (70:30)) is treated as a fluid with Newtonian behavior. Incorporating nanoparticles at volume fractions of 0.025, 0.05, 0.1 and 0.25 has no effects on Newtonian behavior of the base fluid, while in the volume fractions of 0.5 and 1% changes the behavior from Newtonian to non-Newtonian. For Newtonian behavior, adding nanofluid led to increase in viscosity up to 95.67%. It was found that sensitivity of the viscosity to the volume fraction at low temperatures is more, while less viscosity sensitivity to the temperature at low volume fractions. © 2020 Elsevier B.V

Recent advances in preparation methods and thermophysical properties of oil-based nanofluids: A state-of-the-art review

International audienceThermal oils due to their high ability in cooling and lubricating have many applications in high-tech industries such as the automotive industry. Dispersing nanoparticles in a thermal oil (which the resulted new liquid is called nanofluid) can modify further the cooling and lubrication efficiencies. This review paper aims to present the recent advances in the preparation methods and thermophysical properties measurements of oil-based nanofluids. Effects of various parameters such as nanoparticle concentration, size, and temperature on the values of properties including thermal conductivity, viscosity, density, and specific heat are reviewed. The correlations available for the properties of oil-based nanofluids are gathered from the literature that could be a worthy source for those who aim to work on oil-based nanofluids