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An attempt is made to improve the overall performances of channel heat exchangers. The techniques of baffles and nanofluids are combined to enhance the dynamic and thermal behaviors within the channel exchanger. Baffles under various attack angles are used as vortex generators. In addition, oil/multiwalled carbon nanotubes (MWCNT) is used as a working fluid. Both inclinations in the upstream and downstream directions were considered, referenced as Case A (UIB) and Case B (BIB), respectively. While the channel equipped with vertical baffles is referenced as Case C. The proposed models with combined techniques allowed a considerable enhancement in the overall efficiency. The comparison between the three cases revealed that the most significant value of thermal enhancement factor (TEF) of 5.634 was reached with vertical baffles (Case C) at the highest value of Reynolds number. When using inclined baffles, the 75° upstream attack angle (Case A) allowed the highest TEF of 4.814, compared with Case B. [ABSTRACT FROM AUTHOR]

Recently, energy‐related CO2 emissions are considered as one of the most crucial issues and are promptly augmented due to further urbanization. In this paper, in order to model and calculate yearly CO2 emission, an artificial neural network is used. For the first time, the IWO‐SVM method has been applied in modeling energy‐related CO2 emissions. In this regard, consumption of different energy sources such as renewable energy, natural gas, coal, and oil, and GDP of the G8 countries in various years (from 1990 to 2016) are regarded as input in the present study. For the aim of evaluating the exact ability of the SVM and SVM‐IWO models, the performance of these models in three different modes is carried out on the basis of the number of data in the test and train sections. For this purpose, implementations are split into three categories (a = 80% of the data for the train section and 20% for the test section; b = 70% of the data for the train section and 30% for the test section; and c = 60% of the data for the train section and 40% for the test section). Furthermore, five scenarios were selected on the basis of the number of input parameters and input parameters for achieving the best model. As indicated in the results in all scenarios, the correlation of the model with the hybrid invasive weed algorithm based on SVM is more favorable than that in the support vector machine model, due to better training of the SVM‐IWO model than the SVM model. Moreover, the technological orientations of the G8 countries to mitigate CO2 emissions are determined through patent analysis. While the patents have essential information, investigating the published patent by a country could be helpful for determination of technological orientations. Hence, all published patents by these countries are extracted and deeply analyzed. In the next step, to find out main technological approaches, all patents and their intents have been studied. Eventually, the technological life cycles and trends of each main technology are drawn. [ABSTRACT FROM AUTHOR]

Abstract In this study, the CuO/water nanofluid was used to increase the performance of heat pipe heat exchanger. The results showed that the rise in the input power of the heat pipe leads to increase the wall temperature of the pipe, whereas augmenting the concentration of nanoparticles leads to reduce the wall temperature and decrease the temperature difference between the evaporator and the condenser. Using the nanoparticles increases the thermal conductivity of the base fluid and minimizes the temperature gradient within it. Therefore, the thermal power of the heat pipe increases and its resistance decreases. Also, the results showed that with increasing evaporation filling ratio, the resistance reduces (by Fr = 0.45) and then increases. Additionally, experimental data was compared with published data for the heat pipe in order to accurately evaluate the results obtained in this study. The results showed that the measured data are highly accurate. Also, the heat conductivity resistance equation was calculated based on the experimental data. In the next step, the model of neural networks was used to predict thermal performance, FR, the concentration of nanofluid and input power. The results showed that the network with an accuracy of 0.9938 is able to predict the heat transfer coefficient. A review of the predicted and experimental results for the verification part indicated that the residuals are scattered around the zero axis. [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]

Summary: It is believed that fossil fuel sources are exhaustible and also the major cause of greenhouse gas emission. Therefore, it is required to increase the portion of renewable energy sources in supplying the primary energy of the world. In this study, it is focused on application of nanotechnology in exploitation of renewable energy sources and the related technologies such as hydrogen production, solar cell, geothermal, and biofuel. Here, nanotechnologies influence on providing an alternative energy sources, which are environmentally benign, are comprehensively discussed and reviewed. Based on the literature, employing nanotechnology enhances the heat transfer rate in photovoltaic/thermal (PV/T) systems and modifies PV structures, which can improve its performance, making fuel cells much cost‐effective and improving the performance of biofuel industry through utilization of nanocatalysts, manufacturing materials with high durability and lower weight for wind energy industry. The application of nanotechnology in various renewable energies for efficiency improvementIncrease durability of renewable energy technologies by utilizing nanoscienceAchieving sustainable development by applying nanotechnology, which leads to higher efficiency and lower greenhouse gas emissionEnergy resource harvesting by nanotechnology to overcome future issues related to lack of energy [ABSTRACT FROM AUTHOR]

Surging in energy demand makes it necessary to improve performance of plant equipment and optimize operation of thermal power plants. Inasmuch as thermal power plants depend on fossil fuels, their optimization can be challenging due to the environmental issues which must be considered. Nowadays, the vast majority of power plants are designed based on energetic performance obtained from first law of thermodynamic. In some cases, energy balance of a system is not appropriate tool to diagnose malfunctions of the system. Exergy analysis is a powerful method for determining the losses existing in a system. Since exergy analysis can evaluate quality of the energy, it enables designers to make intricate thermodynamic systems operates more efficiently. These days, power plant optimization based on economic criteria is a critical problem because of their complex structure. In this study, a comprehensive analysis including energy, exergy, economic (3‐E) analyses, and their applications related to various thermal power plants are reviewed and scrutinized. Several thermal power plants in combined cycles are reviewed comprehensively in the present study. Results of the studies based on energy, exergy, and economic analyses are represented for each type of thermal power plants. Integrating thermal power plants with other cycles will enhance the energetic efficiency. Systems integrating will increase investment cost for the power plants. Exergy analysis provide useful information for enhancement of plants' efficiency. [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]

Owing to the eye‐catching pros of high energy and environmental performances, numerous ground‐source heat pump (GSHP) systems have been employed in domestic and industrial buildings throughout the world. One of the proved renewable energy technologies is the GSHPs which is able to fulfill the gap between cooling and heating. The main contribution of this article is to present a fully description and critical review of the GSHP systems along with their current developments. At first, the energy efficiency and working standard of a heat pump are introduced. Furthermore, an expansive description of the GSHPs and its advances, and a fully explanation of the ground‐couplet (GCHP) heat pumps, ground‐water (GWHP), and surface water (SWHP) are provided. The mainly representative ground thermal response examination approaches and simulation for the vertical ground heat exchangers presently existing are reviewed counting the heat transfer progressions inside and outside the holes. Similarly, various info regarding a different GWHP by a heat exchanger with different structures, and the opportunity to gain the enhanced energy efficiency with joint cooling and heating using GCHP are explained. The numerous hybrid GCHP systems for heating or cooling‐dominated constructions are appropriately illustrated. It can be deduced that the GSHP technology can be employed both in hot and cold weather areas and the potential of energy saving is noteworthy. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 1241–1265, 2018 [ABSTRACT FROM AUTHOR]

Nowadays, industrial dryers are used instead of traditional methods for drying. When designing dryers suitable for controlling the process of drying and reaching a high-quality product, it is necessary to predict the gradual moisture loss during drying. Few studies have been conducted to compare thin-layer models and artificial neural network models on the kinetics of pistachio drying in a cabinet dryer. For this purpose, ten mathematical-experimental models with a neural network model based on the kinetic data of pistachio drying were studied. The data obtained was from a cabinet dryer evaluated at four temperatures of inlet air and different air velocities. The pistachio seeds were placed in a thin layer on an aluminum sheet on a drying tray and weighed by a scale attached to the computer at different times. In the neural network, data was divided into three parts: Educational (60%), validation (20%) and testing (20%). Finally, the best mathematical-experimental model using a genetic algorithm and the best neural network structure for predicting instantaneous moisture were selected based on the least squared error and the highest correlation coefficient. [ABSTRACT FROM AUTHOR]

In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO 2 -Al 2 O 3 /water nanofluid. TiO 2 -Al 2 O 3 /water in the role of an innovative type of nanofluid was synthesized by the sol-gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO 2 -Al 2 O 3 /water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable.