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In this research, an experimental evaluation is conducted on the hydrothermal behavior of water-based manganese ferrite nanofluid flowing in a metal foam tube. For this purpose, manganese ferrite nanoparticles are synthesized, and X-ray diffraction and scanning electron microscopy are implemented to specify the samples for determination of phase and size of nanoparticles. The effects of Reynolds number, Prandtl number, and presence of MnFe2O4 nanoparticles inside the water on the Nusselt number and friction factor have been studied. The experimental analysis shows that the increment of Reynolds number, Prandtl number, and nanoparticles concentration improve the heat transfer performance. The maximum of 19.1% and 10.5% increase in Nusselt number and friction factor have been achieved respectively by dispersion of 2 wt% manganese ferrite nanoparticles inside the deionized water at Reynolds number of 1,000. A hydrothermal index is proposed to consider the thermal and hydrodynamic characteristics of the nanofluid, and it is attained that the convection heat transfer improvement dominates the pressure drop in this work. According to the experimental results, the Nusselt number and friction factor of the nanofluid is modeled as a function of Reynolds number, Prandtl number, and nanoparticles concentration using artificial neural network with an acceptable precision. [ABSTRACT FROM AUTHOR]

This paper assesses the contribution of TiO2 nanoparticles on thermal performance of a 0.5 mass% aqueous solution of carboxymethyl cellulose (CMC) in a square channel. In this regard, a neural network model is firstly developed for modeling of power law index, consistency index, and thermal conductivity of the aqueous solution of TiO2/CMC-water non-Newtonian nanofluid in terms of the nanoparticle concentration and temperature. Then, an attempt is made to evaluate the friction factor and heat transfer coefficient relative values. According to the results, it is found that the friction factor ratio is directly proportional to the temperature and nanoparticle content, while it inversely varies relative to the shear rate. Moreover, heat transfer coefficient ratio is improved at elevated nanoparticle content, and this improvement is much more profound at higher temperature conditions. For practical purposes, the nanofluid hydrothermal performance index is examined since the addition of nanoparticles increases both heat transfer and friction factor. The corresponding data disclose that the performance index is directly proportional to the nanoparticle content, especially at decreased shear rate and elevated temperature conditions. The application of TiO2/CMC-water nanofluid is found to be more favorable for applications with elevated shear rate conditions. [ABSTRACT FROM AUTHOR]

In order to avoid the costs of experimental evaluations, soft computing methods like artificial neural network (ANN) and genetic algorithm have remarkably grabbed the attentions of investigators for predicting the hydrothermal characteristics of different types of nanofluids. In this paper, the implementation of ANN and genetic algorithm for modeling and multi-criteria optimizing the hydrothermal behavior of SiO 2 /water nanofluid has been investigated. Using the data obtained from the experimental analysis, an ANN model is developed to estimate the pressure drop and Nusselt number as a function of volume concentration, Reynolds number, and inlet temperature. Different network structures were assessed and it has been achieved that a network with 2 hidden layers and 6 neurons in every layer provides the most accurate prediction. It is revealed that the developed network is satisfactorily accurate to determine the Nusselt number and pressure drop of SiO 2 /water nanofluid compared to the empirical correlations. To optimize the hydrothermal behavior of the nanofluid (i.e. to find the optimal cases with highest Nusselt number and the relatively least pressure drop), the genetic algorithm coupled with compromise programming approach has been implemented considering decision maker's attitude. [ABSTRACT FROM AUTHOR]

In this study, the rheological behavior and dynamic viscosity of 5 W30 engine oil based ZnO-MWCNT (30:70) hybrid nanofluid with various volume fractions (VFs) ranging from 0.05 to 1 vol%, temperatures in the range of 5–55 °C, and shear rates altering from 50 to 1000 rpm are experimentally evaluated. The measured viscosity values at different shear rates (SRs) and temperatures revealed that the hybrid nanofluid under study has a non-Newtonian behavior. For example, with increasing SR from 50 to 300 rpm, the maximum viscosity reduction (= 25.6%) occurs at temperature of 5 °C and VF of 1%, while with increasing SR from 700 to 1000 rpm, the minimum viscosity reduction (= 7.6%) occurs at temperature of 55 °C and VF of 0.05%. Moreover, the power law index is observed less than unity indicating pseudoplastic behavior of hybrid nanofluid under study in all VFs and temperatures. The experimental results show that the increase of the VF of nanoparticles leads to the elevated viscosity. By raising the nanofluid temperature, the viscosity of hybrid nanofluid reduces, while engine oil without additives has a high viscosity at elevated temperatures. As an example, considering constant SR of 300 rpm, the increase of temperature from 5 to 55 °C leads to the 78.4% and 85.5% viscosity reduction for the hybrid nanofluid with VF of 0.5% and 0.75%, respectively. In order to predict the experimental data, a three-variable correlation (depending on the temperature, VF of nanoparticles, and SR) and artificial neural network modeling are developed. It is concluded that an excellent agreement exists between the experimental data, correlation results, and predicted data by neural network, however, neural network modeling demonstrated its capability to estimate the viscosity of 5 W30 engine oil based ZnO-MWCNT hybrid nanofluid more precisely rather than the proposed correlation. Hence, the neural network modeling is highly recommended for prediction of viscosity of the hybrid nanofluid under study. [Display omitted] • Rheological behavior of ZnO-MWCNT (30:70)/5 W30 hybrid nanofluid is evaluated. • Effects of concentration, temperature, and shear rates on the viscosity is studied. • A three-variable correlation for viscosity estimation is developed. • Comparison of correlation results with those of available correlations is done. • A neural network modeling is developed for predicting the experimental data. [ABSTRACT FROM AUTHOR]