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

Numerical Investigation of Heat Transfer Enhancement in a Rectangular Heated Pipe for Turbulent Nanofluid

Thermal characteristics of turbulent nanofluid flow in a rectangular pipe have been investigated numerically. The continuity, momentum, and energy equations were solved by means of a finite volume method (FVM). The symmetrical rectangular channel is heated at the top and bottom at a constant heat flux while the sides walls are insulated. Four different types of nanoparticles Al2O3, ZnO, CuO, and SiO2 at different volume fractions of nanofluids in the range of 1% to 5% are considered in the present investigation. In this paper, effect of different Reynolds numbers in the range of 5000 < Re < 25000 on heat transfer characteristics of nanofluids flowing through the channel is investigated. The numerical results indicate that SiO2-water has the highest Nusselt number compared to other nanofluids while it has the lowest heat transfer coefficient due to low thermal conductivity. The Nusselt number increases with the increase of the Reynolds number and the volume fraction of nanoparticles. The results of simulation show a good agreement with the existing experimental correlations

Entropy Generation during Turbulent Flow of Zirconia-water and Other Nanofluids in a Square Cross Section Tube with a Constant Heat Flux

The entropy generation based on the second law of thermodynamics is investigated for turbulent forced convection flow of ZrO2-water nanofluid through a square pipe with constant wall heat flux. Effects of different particle concentrations, inlet conditions and particle sizes on entropy generation of ZrO2-water nanofluid are studied. Contributions from frictional and thermal entropy generations are investigated, and the optimal working condition is analyzed. The results show that the optimal volume concentration of nanoparticles to minimize the entropy generation increases when the Reynolds number decreases. It was also found that the thermal entropy generation increases with the increase of nanoparticle size whereas the frictional entropy generation decreases. Finally, the entropy generation of ZrO2-water was compared with that from other nanofluids (including Al2O3, SiO2 and CuO nanoparticles in water). The results showed that the SiO2 provided the highest entropy generation

Flow visualization and analysis of thermal distribution for the nanofluid by the integration of fuzzy c-means clustering ANFIS structure and CFD methods

Abstract: A nanofluid containing copper (Cu) nanoparticles was simulated in a rectangular cavity using computational fluid dynamic (CFD). The upper and lower walls of the cavity were adiabatic, while the right and left walls had warm and cold temperatures, respectively. This temperature difference causes a thermal flow from the right wall to the left wall. The elements of the coordination system in different directions, including velocity in the Y direction (V) and fluid temperature, were obtained using CFD. Adaptive network-based fuzzy inference system (ANFIS) was used to train the CFD outputs and provided artificial flow field and temperature distribution along the cavity domain. The CFD outputs were used as input and output data for the ANFIS method. The position of the fluid layer in X and Y computing directions and fluid velocity (Y axis) were used as three inputs, and the fluid temperature was taken as the output in the ANFIS method training process. The data were categorized using fuzzy c-means clustering, and different numbers of clusters were taken as a key parameter in this method. Using the fuzzy inference system, it is possible to predict the nodes in the cavity not generated through CFD simulation so that different coordination of the fluid at these points can be computed. Using ANFIS method, it is possible to reduce the computation time of CFD method so that more nodes are predicted in a shorter period of time, while clustering method can enhance the computing time for each neural cell. The ANFIS method can also visualize the flow in the cavity and display the thermal distribution along with the heat source. Graphic abstract: [Figure not available: see fulltext.]. © 2019, The Visualization Society of Japan

Review of Magnetic Shape Memory Polymers and Magnetic Soft Materials

Magnetic soft materials (MSMs) and magnetic shape memory polymers (MSMPs) have been some of the most intensely investigated newly developed material types in the last decade, thanks to the great and versatile potential of their innovative characteristic behaviors such as remote and nearly heatless shape transformation in the case of MSMs. With regard to a number of properties such as shape recovery ratio, manufacturability, cost or programming potential, MSMs and MSMPs may exceed conventional shape memory materials such as shape memory alloys or shape memory polymers. Nevertheless, MSMs and MSMPs have not yet fully touched their scientific-industrial potential, basically due to the lack of detailed knowledge on various aspects of their constitutive response. Therefore, MSMs and MSMPs have been developed slowly but their importance will undoubtedly increase in the near future. This review emphasizes the development of MSMs and MSMPs with a specific focus on the role of the magnetic particles which affect the shape memory recovery and programming behavior of these materials. In addition, the synthesis and application of these materials are addressed

Effect of magnetic field on thermo-physical and hydrodynamic properties of different metals-decorated multi-walled carbon nanotubes-based water coolants in a closed conduit

In this paper, the thermo-physical and hydrodynamic properties of heat transfer nanofluids containing metal nanoparticles-decorated multi-wall carbon nanotube (MWCNT) are reported. To this end, Cu-decorated MWCNT, Fe-decorated MWCNT, and Ni-decorated MWCNT (covalently functionalized samples) were synthesized with pre-functionalization with the aspartic acid as the hydrophilic chain. To have a comprehensive comparison, water-based non-covalently functionalized MWCNT nanofluids were also synthesized. A significant increase in the thermal and electrical conductivities of heat transfer nanofluids containing metal nanoparticles-based MWCNT as compared to the non-covalently functionalized sample as well as water has been determined at the same operational conditions. All the prepared nanofluids are stable and the viscosity and density remained approximately the same after loading additives. The present paper also focused on the study of the role of weight concentrations of additives, flow rate and thermo-physical properties of the prepared nanofluids on the convective heat transfer rate and hydrodynamic performances in the laminar flow. Further, the convective heat transfer coefficient, pressure drop, friction factor, performance index and pumping power variation were also investigated under applied magnetic field, which improves the overall thermal performance of the closed conduit insignificantly

Heat transfer enhancement of turbulent nanofluid flow over various types of internally corrugated channels

A numerical study is carried out to investigate the effects of different geometrical parameters and various nanofluids on the thermal performance of rib-grooved channels under uniform heat flux. The continuity, momentum and energy equations are solved by using the finite volume method (FVM). Three different rib-groove shapes are studied (rectangular, semi-circular and trapezoidal). Four different types of nanoparticles, Al2O3, CuO, SiO2 and ZnO with different volume fractions in the range of 1% to 4% and different nanoparticle diameters in the range of 20nm to 60nm, are dispersed in the base fluids such as water, glycerin and ethylene glycol. The Reynolds number varies from 5000 to 25,000. To optimize the shape of rib-groove channels different rib-groove heights from 0.1Dh (4mm) to 0.2Dh (8mm) and rib-groove pitch from 5e (20mm) to 7e (56mm) are examined. Simulation results reveal that the semi-circular rib-groove with height of 0.2Dh (8mm) and pitch equals to 6e (48mm) has the highest Nusselt number. The nanofluid containing SiO2 has the highest Nusselt number compared with other types. The Nusselt number rises as volume fraction increases, and it declines as the nanoparticle diameter increases. The glycerin-SiO2 nanofluid has the best heat transfer compared to other base fluids. It is also observed that in the case of using nanofluid by changing parameters such as nanoparticle diameter, volume fraction and base fluids the skin friction factor has no significant change

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

Evaluating the effect of temperature and concentration on the thermal conductivity of ZnO-TiO2/EG hybrid nanofluid using artificial neural network and curve fitting on experimental data

In this paper, the experimental data on the thermal conductivity of EG based hybrid nanofluid containing zinc oxide and titanium oxide have been used. At the first, three two-variable correlations have been proposed using curve-fitting on experimental data. After that, the best transfer function for training the artificial neural network has been selected. The input variables of neural network were temperature and solid volume fraction, while the output variable was the thermal conductivity enhancement of the nanofluid. Moreover, the correlation outputs, ANN results and experimental data have been compared. The results showed that there is a good agreement between experimental data and neural network results so that the resulting model of the neural network is able to predict the thermal conductivity enhancement of the nanofluid. The findings also indicated that the accuracy of the neural network is much greater than the curve fitting method to predict thermal conductivity enhancement of ZnO-TiO2/EG hybrid nanofluid

Development of a new density correlation for carbon-based nanofluids using response surface methodology

Density is among the fundamental thermo-physical characteristics of fluids that are examined prior to carrying out performance analysis of the fluid. In this study, the effect of the design variables on the density of nanofluids was studied using response surface methodology (RSM). The quadratic model produced by RSM was employed to determine the performance factors, i.e., mass concentration and temperature with reasonably good accuracy. Improved experimental correlations were proposed for the density prediction of the carbon-based nanofluids based on the experimental data. Experimentally measured densities of two different nanofluids at the nanoparticle mass concentration of up to 0.1% and the temperature range of 20–40 °C were examined. The improvement in densities compared to the density of base fluid at 20 and 40 °C is approximately 0.15% for 0.1% fraction of MWCNT–COOH nanoparticles. Additionally, the densities of F-GNP nanofluids are increased by 0.056% compared to the density of distilled water. As a final point, the RSM results were compared with the results which got from the empirical data. It was detected that the optimal RSM model is accurate and the absolute maximum deviation measured values from the predicted densities of MWCNT–COOH and F-GNP nanofluids are 0.012 and 0.009%, respectively