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Phase Change Materials (PCMs) have being used in different solar energy systems for thermal energy storage and performance enhancement. Improving heat transfer from PCMs leads to reductions in charge and discharge durations, which makes them more favorable as storage units. Dispersion of conductive solid materials with nano dimensions is a practical approach to enhance their thermal features. Several nano-sized materials have been added to PCMs to improve their heat/cool storage abilities. The studies in this field reveal that employing nanotechnology is an efficient method for heat transfer improvement. In the current article, studies concerning applications of nanotechnology in PCMs are reviewed. According to the results, employing nanotechnology can noticeably enhance the heat transfer rate, which results in a reduction of charge and discharge times of the storage units equipped with the PCMs. The enhancement in the melting process of nano PCMs, which indicates the storage performance, depends on several factors such as concentration and kind of solid nano-sized particles, type of base PCM and the parameters related to operating conditions such as Rayleigh and Darcy numbers. The improvement in thermal performance due to nano PCMs is mainly attributable to the increased thermal conductivity and decrease in latent heat of fusion, which accelerates the melting/solidification processes.

Heat pipes are broadly applied for thermal management of devices with high heat flux. Due to the dependency of their efficient operating range on the boiling point of the working fluids, employing special types of fluids is necessary to have applicable heat pipes at very low temperatures. Hydrogen, neon and nitrogen are among the most attractive working fluids for heat pipes in order to reach efficient heat transfer in cryogenic conditions. According to the reviewed studies on cryogenic heat pipes, similar to the medium or high-temperature heat pipes, their thermal performance depends on different factors such as filling ratio, input power, structure and type of heat pipe and working fluid. Employing appropriate fluids, applicable for the required operating temperature, provides heat pipes with high effective thermal conductivity in cryogenic conditions. The effective thermal conductivity of the cryogenic heat pipes is several orders of magnitudes higher than that of the conductive metals in the similar temperature which makes them as desirable thermal management devices for low-temperature applications such as cooling superconductors. In this paper, a comprehensive review is carried out on the researches focused on the cryogenic heat pipes and the factors influencing their heat transfer features to provide a useful and applicable reference for the future studies with relevant topics. This review study is categorized based on the type of heat pipes: conventional heat pipes, pulsating heat pipes and thermosyphons (gravity-assisted heat pipes). The reviewed studies have demonstrated that the operating fluid and heat load have substantial role in the performance of heat pipes. Finally, some recommendations for future studies are provided to reach further enhancement in the performance of cryogenic heat pipes.

Employing renewable energy sources for power generation have been developed in recent years due to their several benefits including low emission of greenhouse gases and their inexhaustible nature. Among the applicable renewable energy sources for electricity generation, geothermal energy is one of the most appropriate ones owing to its independent availability from the weather condition. Due to the acceptable performance of Organic Rankine cycles (ORCs) in generating power from low- or medium-temperature heat sources, these cycles are appropriate choices for utilization in geothermal power plants. There are several parameters that influence the rate of power production and the efficiency of geothermal-based ORCs. The impacts of influential parameters have been investigated in several studies by modeling and optimization of these cycles. This article provides a comprehensive review of the previous studies focused on modeling and optimization of the ORCs with different configurations and operating conditions, which would be useful for the researchers working in this field of science. Conclusions of the reviewed studies reveal that geothermal-based ORCs performance is significantly influenced by the applied operating fluid in the cycle, working condition in addition to configuration and architecture of the applied cycle. Moreover, studies that concentrated on the optimization of geothermal-based ORCs are also reviewed and their key conclusions are reflected as well. In the majority of the studies in this field, technical and economic objectives have been considered for optimization of the cycles. Depending on the characteristics of the case studies, the efficiency and cost of the generated power in optimized geothermal-based ORCs vary greatly. Finally, several recommendations are proposed for future studies in this field.

Solar energy has several advantages compared to other types of renewable sources of energy such as easy accessibility and superior predictability. This source of energy is utilized for several purposes including heating, desalination and power generation. Generally, PV panels are employed for direct electricity generation. On the other hand the harvested solar thermal energy can be used to generate power indirectly by employing a medium such as a solar tower. PV panels are preferred for small-scale and off-grid electricity production purposes and their performance depends on various factors such as their building material, solar irradiation and operating temperature. The efficiency of the panels degrade by increasing the temperature. As such, different approaches are suggested to cool the PV panels and enhance their efficiency. The cooling methods are categorized as active and passive. An examples of the active method would be utilizing water flow, while for the passive method a thermal medium such as heat pipes is employed. In this work, the common methods utilized for cooling PV panels are reviewed, analyzed and compared. According to the outcomes of relevant published studies, efficiency enhancement of PV cells rely on several factors including solar irradiation, employed cooling approach, and the operating conditions. Normally, using thermal management approaches for hot climate conditions is more useful due to the corresponding high working temperatures of the PV cells which ultimately reduces their electricity generation capabilities.

Nanofluids are widely applicable in thermal devices with porous structures. Silica nanoparticles have been dispersed in different heat transfer fluids in order to increase their thermal conductivity and heat transfer capability. In this study, group method of data handling (GMDH) and multilayer perceptron artificial neural networks are applied for determining thermal conductivity of nanofluids with silica particles and different base fluids such as ethylene glycol, glycerol, water and ethylene glycol–water mixture. For cases with multilayer perceptron models, trained by applying scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) have been tested as two different training algorithms. The outputs of the applied models have good agreement with the values obtained in experimental studies. The values of $${R}^{2}$$ in the optimum conditions of using GMDH, LM and SCG are 0.9997, 0.9991 and 0.9998, respectively. In addition, the MSE values of the mentioned methods are approximately 0.000010, 0.000032 and 0.0000078, respectively.

Carbon nanotubes (CNTs) are known to be capable of enhancing optical and thermal properties of fluids prominently. Due to the modified features of the nanofluids with dispersed CNTs, they are used in various solar systems including PV/thermal (PV/T), collectors and water heating technologies. In this review article, firstly, the findings of the research performed on the thermal conductivity of nanofluids with CNTs are represented. In the next step of the study, the research conducted on the utilization of the mentioned nanofluids in solar systems are reviewed. On the basis of the reviewed literature, it is concluded that nanofluids with CNTs improve the performance of solar systems from both energy and exergy points of view. The performance of these nanofluids in the solar system is under the influence of various factors including the type of CNTs, operating conditions, concentration and the system they are applied in. Finally, according to the results of our literature review, suggestions are provided for future studies in this field which can lead to further enhancement in the efficiency of solar systems incorporating the investigated nanofluids.

Numerous studies have proposed to correlate experimental results, however there are still significant errors in those predictions. In this study, an artificial neural network (ANN) is considered for a two-phase flow pressure drop in microchannels incorporating four neural network structures: multilayer perceptron (MLP), radial basis function (RBF), general regression (GR), and cascade feedforward (CF). The pressure drop predication by ANN uses six inputs (hydraulic diameter of channel, critical temperature of fluid, critical pressure of fluid, acentric factor of fluid, mass flux, and quality of vapor). According to the experimental data, for each network an optimal number of neurons in the hidden layer is considered in the range 10&ndash, 11. A committee neural network (CNN) is fabricated through the genetic algorithm to improve the accuracy of the predictions. Ultimately, the genetic algorithm designates a weight to each ANN model, which represents the relative contribution of each ANN in the pressure drop predicting process for a two-phase flow within a microchannel. The assessment based on the statistical indexes reveals that the results are not similar for all models, the absolute average relative deviation percent for MLP, CF, GR, and CNN were obtained to be equal to 10.89, 10.65, 7.63, and 5.79, respectively. The CNN approach is demonstrated to be superior to many ANN techniques, even with simple linearity in the model.

The mandibular condyle consists of articular cartilage and subchondral bone that play an important role in bearing loads at the temporomandibular joint (TMJ) during static occlusion and dynamic mastication. The objective of the current study was to examine effects of sex and cartilage on 1) static and dynamic mechanical analysis (DMA) based dynamic energy storage and dissipation for the cartilage-subchondral bone construct of the human mandibular condyle, and 2) their correlations with the tissue mineral density and trabecular morphological parameters of subchondral bone. Cartilage-subchondral bone constructs were obtained from 16 individual human cadavers (9 males, 7 females, 79.00±13.10 years). After scanning with micro-computed tomography, the specimens were subjected to a non-destructive compressive static loading up to 7N and DMA using a cyclic loading profile (-5±2N at 2Hz). After removing the cartilage from the same specimen, the series of loading experiments were repeated. Static stiffness (K) and energy dissipation (W), and dynamic storage (K'), loss (K'') stiffness, and energy dissipation (tan δ) were assessed. Gray values, which are proportional to degree of bone mineralization, and trabecular morphological parameters of the subchondral bone were also measured. After removal of the cartilage, static energy dissipation significantly decreased (p0.009) but dynamic energy dissipation was not influenced (p0.064). Many subchondral bone properties were significantly correlated with the overall mechanical behavior of the cartilage-subchondral bone constructs for males (p0.047) but not females (p0.054). However, after removal of cartilage from the constructs, all of the significant correlations were no longer found (p0.057). The current findings indicate that the subchondral bone is responsible for bearing static and dynamic loading in males but not in females. This result indicates that the female condyle may have a mechanically disadvantageous TMJ loading environment.

Thermophysical properties of hybrid nanofluids remarkably affect their behavior in engineering systems. Among these properties, dynamic viscosity and thermal conductivity are more crucial in thermal sciences. In recent years, several models have been introduced based on intelligence methods for predicting these properties of the hybrid nanofluids. Confidence and accuracy of these models are influenced by the modeling algorithm used, the data implemented to train the model, the input parameters that are considered, etc. In the present review article, models created by several different machine learning approaches are comprehensively reviewed. According to the studies conducted in this field so far, it is concluded that artificial neural network is a very attractive approach for modeling both dynamic viscosity and thermal conductivity. The performance of these ANN-based methods can be modified by applying appropriate optimization approaches in order to find their optimum architecture design which minimize error margins. In addition to available correlations and implementation of ANNs, other intelligent approaches such as support vector machine and adaptive neuro fuzzy interface system are also applicable for accurate modeling of rheological properties of hybrid nanofluids.

A single or an array of metallic porous structures of various geometries are introduced on the inside surface of a partially heated wall of a channel with air or water flow. Forced convective heat transfer rate from the heated wall to the fluid, as well as the average pressure drop along the channel are numerically studied. Effects of the aluminum porous block's structural parameters such as porosity (ϵ), Darcy number (Da) and Forchheimer coefficient (F) are taken into account. The optimum geometrical conditions of the system is extensively explored considering parameters such as blocks' height, width and spacing between them. Our results demonstrate great potential for metal foams to perform as heat sinks and pave the way for their further implementation in applications such as solar thermal collectors and electronic cooling.

Heat transfer reduction capabilities of a vertical or a horizontal adiabatic partial partition fixed in a differentially heated cavity with insulated top and bottom walls are analyzed and compared. The effects of length and location of the partition is taken into account for aspect ratios from 1 to 4 and for Rayleigh numbers from 103 to 106. Different characteristics of square and higher aspect ratio cavities are compared and a comprehensive correlation of the heat transfer reduction is introduced, incorporating all the pertinent parameters. Based on our results, vertical baffles more efficiently reduce heat transfer, in most cases.

The resonant frequencies and flexural sensitivities of an atomic force microscope (AFM) with assembled cantilever probe (ACP) are studied. This ACP comprises a horizontal cantilever, a vertical extension and two tips located at the free ends of the cantilever and the extension, which makes the AFM capable of simultaneous topography at top surface and sidewalls of microstructures especially microgears, which consequently leads to a time-saving swift scanning process. In this work, the effects of the sample surface contact stiffness and the geometrical parameters such as the ratio of the vertical extension length to the horizontal cantilever length and the distance of the vertical extension from clamped end of the horizontal cantilever on both flexural and torsional resonant frequencies and sensitivities are assessed. These geometrical effects are illustrated in some figures. The results show that the low-order vibration modes are more sensitive for low values of the contact stiffness, but the situation is reversed for high values.

The resonant frequencies and torsional sensitivities of an atomic force microscope (AFM) with assembled cantilever probe (ACP) are studied. This ACP comprises a horizontal cantilever, a vertical extension and two tips located at the free ends of the cantilever and the extension which makes the AFM capable of simultaneous topography at top-surface and sidewalls of microstructures especially microgears which consequently leads to a time-saving swift scanning process. In this work, the effects of the sample surface contact stiffness and the geometrical parameters such as the ratio of the vertical extension length to the horizontal cantilever length and the distance of the vertical extension from clamped end of the horizontal cantilever on torsional resonant frequencies and sensitivities are assessed. These geometrical effects are illustrated in some figures. The results show that the low-order vibration modes are more sensitive for low values of the contact stiffness but the situation is reversed for high values.Copyright © 2010 by ASME

The resonant frequencies and flexural sensitivities of an atomic force microscope (AFM) assembled cantilever probe which comprises a horizontal cantilever, a vertical extension and two tips located at the free ends of the cantilever and the extension are studied. This probe makes the AFM capable of simultaneous topography at top-surface and sidewalls of microstructures especially microgears which leads to a time-saving swift scanning process. In this work, the effects of the sample surface contact stiffness and the geometrical parameters such as the ratio of the vertical extension length to the horizontal cantilever length and the distance of the vertical extension from clamped end of the horizontal cantilever on the resonant frequencies and flexural sensitivities are assessed. These geometrical effects are illustrated in some figures. The results show that the low-order vibration modes are more sensitive for low values of the contact stiffness but the situation is reversed for high values.Copyright © 2010 by ASME