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The thermophysical specifications of working fluid play a key role in thermal performance of various types of heat pipes. Fluids with high thermal conductivity, low viscosity and surface tension are more favorable to be applied in heat pipes. In order to have fluids with higher thermal conductivity, adding nanoparticles can be an acceptable idea. In the present study, the effects of using nanofluids in several types of heat pipes are reviewed. The nanofluids are categorized based on the types of particles (as carbonic, metallic, etc.). Based on the results of the literature review, applying nanostructures in the base fluid can significantly reduce the thermal resistance of heat pipes compared with utilizing pure as operating fluid. For instance, it is observed that using graphene oxide/water nanofluid in pulsating heat pipe reduces the thermal resistance up to 42% in comparison with the water-filled heat pipe. In addition, reviewed studies revealed that the type of nanoparticle, concentration and their stability are among the most important parameters affecting thermal performance. The enhancement in thermal performance of heat pipes by using nanofluid is mainly attributed to higher thermal conductivity of the nanofluids and increase in nucleation sites. [ABSTRACT FROM AUTHOR]

In this study, a model is proposed by applying the least squares support vector machine (LSSVM). In addition, genetic algorithm is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. In addition to temperature and concentration of nanoparticles, the parameters which are used in most of the modeling procedures for thermal conductivity, the effect of particle size is considered. By considering the size of nanoparticles as one of the input variables, a more comprehensive model is obtained which is applicable for wider ranges of influential factor on the thermal conductivity of the nanofluid. The coefficient of determination (R2) for the introduced model is equal to 0.9902, and the mean squared error is 8.64 × 10−4 for the thermal conductivity ratio of Al2O3/EG. [ABSTRACT FROM AUTHOR]

Thermal conductivity and dynamic viscosity play key role in heat transfer capacity of nanofluids. In the present study, thermal conductivity and dynamic viscosity of Fe2O3/water are modeled by applying various artificial neural network algorithms. The applied algorithms are MLP, GA-RBF, LSSVM, and CHPSO ANFIS algorithms. The data for modeling procedure are extracted from several experimental studies. Obtained results by the different algorithms are compared and it was concluded that the highest R-squared values belonged to GA-RBF algorithm which were equal to 0.9962 and 0.9982 for thermal conductivity ratio and dynamic viscosity, respectively. [ABSTRACT FROM AUTHOR]

Pulsating heat pipes (PHPs) are heat transfer devices which are widely utilized in electronic devices and energy systems. Improvement in thermal performance of PHPs will lead to higher heat transfer capacity and enhancement in the efficiency of systems which PHPs are applied. In this study, an experimental investigation was performed on the thermal performance of a pulsating heat pipe by applying graphene oxide nanofluid as working fluid. Four concentrations of graphene oxide (0.25, 0.5, 1, and 1.5 g/lit) in water as base fluid were used in the PHP. Results indicate that adding graphene oxide sheets increased thermal conductivity and viscidity of the base fluid. Moreover, utilizing graphene oxide can decrease thermal resistance of PHP up to 42%. In addition, high concentration (1.5 g/lit) of the nanofluid worsen thermal performance of the PHP in comparison with pure water which is attributed to increase in dynamic viscosity of nanofluid. Finally, a regression model is proposed in order to compare effects of heat input and concentration of nanofluid on thermal resistance mathematically. [ABSTRACT FROM AUTHOR]

Various parameters affect thermal conductivity of nanofluid; however, some of them are more influential such as temperature, size and type of nano particles and volumetric concentration. In this study, artificial neural network as well as least square support vector machine (LSSVM) are applied in order to predict thermal conductivity ratio of alumina/water nanofluid as a function of particle size, temperature and volumetric concentration. LSSVM, Self-Organizing Map and Levenberg-Marquardt Back Propagation algorithms are applied to predict thermal conductivity ratio. Obtained results indicated that these algorithms are appropriate tool for thermal conductivity ratio prediction. The correlation coefficient values are very favorable and equal to 0.88125 and 0.87575 and 0.89999 by applying SOM, LM-BP algorithms and LSSVM, respectively. [ABSTRACT FROM AUTHOR]

One of the auspicious nanomaterials which has exceptionally enticed researchers is carbon nanotubes (CNTs) as the result of their excellent thermal properties. In this investigation, an experiment was carried out on three kinds of CNTs-nanofluids with various CNTs added to de-ionized water to compared and analyze their thermal conductivity properties. The main purpose of this study was to introduce a combination of experimental and modelling approaches to forecast the amount of thermal conductivity using four different neural networks. Between MLP-ANN, ANFIS, LSSVM, and RBF-ANN Methods, it was found that the LSSVM produced better results with the lowest deviation factor and reflected the most accurate responses between the proposed models. the regression diagram of experimental and estimated values shows an R2 coefficient of 0.9806 and 0.9579 for training and testing sections of the ANFIS method in part a, and in the b, c and d parts of the diagram, coefficients of determination were 0.9893& 0.9967 and 0.9974 & 0.9992 and 0.9996& 0.9989 for training and testing part of MLP-ANN, RBF-ANN and LSSVM models. Also, the effect of different parameters was investigated using a sensitivity analysis method which demonstrates that the temperature is the most affecting parameter on the thermal conductivity with a relevancy factor of 0.66866. [ABSTRACT FROM AUTHOR]

In the present paper, several experimental and theoretical studies conducted on the thermal conductivity of nanofluids are represented and investigated. Based on the reviewed studies, various factors affect thermal conductivity of nanofluids such as temperature, the shape of nanoparticles, concentration and etc. Results indicated the increase in temperature and concentration of nanoparticles usually leads to the higher thermal conductivity of nanofluids. In addition, it is concluded that there are some novel approaches in order to obtain nanofluids with more appropriate thermal properties including using binary fluids as the base fluid or utilizing hybrid nanofluids. [ABSTRACT FROM AUTHOR]

Thermal conductivity of nanofluids depends on several parameters including temperature, concentration, and size of nanoparticles. Most of the proposed models utilized concentration and temperature as influential factors in their modeling. In this study, group method of data handling (GMDH) artificial neural networks is applied in order to model the dependency of thermal conductivity on the mentioned factors. Firstly, temperature and concentration considered as inputs and a model is represented. Afterwards, the size of nanoparticles is added to the input variables and the results are compared. Based on obtained results, GMDH is an appropriate method to predict thermal conductivity of the nanofluids. In addition, it is necessary to consider size of nanoparticles in order to have a more precise model. [ABSTRACT FROM AUTHOR]