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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]

The costly and time-consuming determination of thermophysical properties of nanofluids through the experimental analysis leads the current investigations to use the soft computing methods like correlating, artificial neural network (ANN) and genetic algorithm. In this study, the application of ANN, empirical correlations and genetic algorithm for modeling and multi-criteria optimization of the thermophysical properties of clove-treated MWCNTs nanofluid which has been synthesized through a facile and eco-friendly procedure has been investigated. In this contribution, totally 6 structures are assessed: networks including one and two hidden layers with 2, 4, and 6 neurons. From assessment of the ANN, it is found that the network including two hidden layers with 4 neurons in every layer results in the least difference between the network outputs and the experimental data, providing the best performance. It is concluded that the optimal ANN model is a more precise and accurate way to predict the thermal conductivity and viscosity of environmentally friendly C-MWCNT/water nanofluid compared to empirical correlations obtained from non-linear regression method. Moreover, the evolutionary algorithm has been implemented for achieving the optimal conditions to maximize the thermal conductivity and to minimize the viscosity of nanofluid. In this regard, based on real-world engineering experience, the final optimal solutions opted from several distinguished procedures of decision-making including the Bellman-Zadeh, TOPSIS and LINMAP approaches has been investigated in parallel. The results of this study revealed that the obtained outputs using TOPSIS and LINMAP procedures are the closest to ideal solution. [ABSTRACT FROM AUTHOR]

In this work, a two-phase mixture model for evaluation of flow and heat transfer performance of SiO 2 /water nanofluids under turbulent flow was proposed by considering the heterogeneity of concentration due to crossed effect and the influences of shear rate, viscosity gradient, thermophoresis and Brownian motion on the diffusion of the nanoparticles. The effects of Peclet number, Reynolds number, nanoparticle size and nanofluid mean concentration on the distribution of nanoparticles have been evaluated. The values of thermal conductivity and viscosity as the main thermophysical properties of nanofluids changed across different layers of the liquid due to the heterogeneous distribution of concentration. It was observed that an increase in the Peclet number caused heterogeneity in the distribution of the properties. The achieved nanoparticle distribution has been implemented for analysis of nanofluid using two-phase mixture model. It was found that the effect of nanofluid concentration on the Nusselt number was more noticeable in lower Reynolds numbers due to the insignificant effect of flow momentum on heat transfer. The maximum of 43.9% enhancement in convection heat transfer was achieved by dispersion of 4% SiO 2 nanoparticles inside DI-water at Re = 25,000. [ABSTRACT FROM AUTHOR]

An experimental study is conducted to investigate the convection heat transfer of Fe 3 O 4 /water nanofluid through a porous metal foam tube with uniform heat flux under the influence of constant magnetic field. This magnetic field is generated by four identical electromagnets. The electromagnets sequence is one of the most important parameters that has considerable effect on the convection heat transfer in this subject. Therefore, four different configurations of electromagnets are considered and the efficient arrangement is obtained by a numerical simulation using single-phase method in order to reach the higher heat transfer rate. Furthermore, the effects of Reynolds number, nanofluid weight fraction and magnetic field intensity on the forced convection heat transfer in the porous metal foam tube are examined under presence and absence of constant magnetic field. The experimental results show that the application of Fe 3 O 4 /water nanofluid enhances the convection heat transfer compared to DI-water. Additionally, implement of constant magnetic field leads to improve the convection heat transfer of nanofluid through the porous metal foam tube. This heat transfer improvement is more intensified in the lower fluid velocities as well as higher nanoparticle weight fractions. The maximum of 23.4% heat transfer enhancement is obtained by dispersion of 2% weight fraction of nanofluid inside the DI-water at Re = 200 under applied magnetic field of 200 G (0.02 T) intensity. A new correlation has been proposed for the Nusselt number of Fe 3 O 4 /water nanofluid in terms of Reynolds number, Prandtl number, nanoparticle weight fraction and magnetic field intensity which predicts the experimental data accurately. [ABSTRACT FROM AUTHOR]

In this work, the evaluation of thermal conductivity of MnFe 2 O 4 /water nanofluids in attendance of magnetic field is conducted. The MnFe 2 O 4 /water is synthesized and prepared in different nanoparticles concentrations, from 0.25% to 3%. The thermal conductivity of nanofluids has been determined at various nanofluid concentrations and temperatures with and without magnetic field. It is found that the thermal conductivity of nanofluid remarkably improves with augmentation of solid volume fraction. Moreover, by incrementing the nanofluid temperature, the thermal conductivity improves under no magnetic field, while it decreases at higher temperatures in attendance of magnetic field. The highest thermal conductivity improvement is achieved 26.4% at 60 °C and 3 vol.% of nanoparticles under no magnetic field and 36.5% under 400 G magnetic field which occurs at 20 °C and 3 vol.% of nanoparticles. Furthermore, an empirical correlation is derived to estimate thermal conductivity of MnFe 2 O 4 /water nanofluid in terms of magnetic field intensity, volume concentration, and temperature. [ABSTRACT FROM AUTHOR]

Highlights • Transient characteristics of nanofluid in a minichannel heat sink with MEPCM layer in its ceiling are studied. • The wall temperature rises sharply after sudden heat-pulse and then tend to rise slowly. • The dispersion of nanoparticles inside water leads to the clear decrement of wall and temperature. Abstract In this work, transient thermal characteristics of pure water or Al 2 O 3 /water nanofluid with 2–8 wt% concentrations as coolant flowing through a minichannel heat sink with MEPCM layer in its ceiling are experimentally investigated. The heat sink consists of eight minichannels with length, height, and width of 50 mm, 3 mm, and 1 mm for each minichannel, respectively. A uniform heat flux in the range of 1.2–6.8 W/cm2 is imposed to heat the heat sink and a heat pulse amplitude of 8 W/cm2 is suddenly applied for 10 min after steady-state heating condition and then the heating power is cut off immediately. The dimensionless wall temperature distribution, temperature of MEPCM layer, maximum wall temperature suppression, and heat transfer effectiveness of the system are assessed under sudden heat-pulse conditions. The measured results reveal that the wall temperature and MEPCM layer temperature rise sharply after sudden heat-pulse and then tend to rise slowly. Moreover, it is observed that the latent heat absorbed by the MEPCM layer during melting process can reduce the severe temperature variation caused by sudden heat-pulse. Furthermore, it is concluded that the dispersion of alumina nanoparticles inside pure water and the increase of its concentration lead to the significant decrement of wall temperature and MEPCM layer temperature and increment of the maximum wall temperature suppression. Finally, the comparison of the results obtained from the employment of nanofluid and pure water in the heat sink shows that the nanofluid-cooled heat sink with MEPCM layer in its ceiling outperforms the water-cooled one with/without MEPCM layer. [ABSTRACT FROM AUTHOR]