Showing papers by "Mohammad Hossein Ahmadi published in 2019"
TL;DR: The principles of thermoelectricity are described and an explanation of current and upcoming materials are presented and developed models and various performed optimization of thermOElectric applications by using non-equilibrium thermodynamics and finite time thermodynamics are discussed.
293 citations
TL;DR: In this paper, the effect of nanoparticles on thermal efficiency, entropy generation, heat transfer coefficient enhancement, as well as pressure drop in parabolic trough collectors (PTCs) has been investigated.
Abstract: The present review paper aims to document the latest developments on the applications of nanofluids as working fluid in parabolic trough collectors (PTCs). The influence of many factors such as nanoparticles and base fluid type as well as volume fraction and size of nanoparticles on the performance of PTCs has been investigated. The reviewed studies were mainly categorized into three different types of experimental, modeling (semi-analytical), and computational fluid dynamics (CFD). The main focus was to evaluate the effect of nanofluids on thermal efficiency, entropy generation, heat transfer coefficient enhancement, as well as pressure drop in PTCs. It was revealed that nanofluids not only enhance (in most of the cases) the thermal efficiency, convection heat transfer coefficient, and exergy efficiency of the system but also can decrease the entropy generation of the system. The only drawback in application of nanofluids in PTCs was found to be pressure drop increase that can be controlled by optimization in nanoparticles volume fraction and mass flow rate.
186 citations
TL;DR: In this article, thermal conductivity of thermmosyphons has been investigated for different heat transfer purposes including cooling devices and heat exchangers, and thermal performance of thermal performan...
Abstract: Thermosyphons have high effective thermal conductivity and are applicable for different heat transfer purposes including cooling devices and heat exchangers. In the present study, thermal performan...
152 citations
TL;DR: Results indicate that LSSVM approach has the best performance and the proposed model by this approach has R-squared value equals to 1.
134 citations
TL;DR: In this article, 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.
Abstract: 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 09902, and the mean squared error is 864 × 10−4 for the thermal conductivity ratio of Al2O3/EG
126 citations
TL;DR: In this article, a modified battery layout system is proposed to induce active and passive cooling for each cell of a battery module, where each cell is placed in a 4 1/4mm cylindrical gap enclosure filled with phase change material and interconnected together for further cooling at inter-spacings.
125 citations
119 citations
TL;DR: According to the reviewed scientific sources, the structure of model, such as number of neurons and layers in artificial neural network (ANN), the applied activation function, and utilized algorithm are the most influential factors on the accuracy of the model.
Abstract: Nanofluids are broadly applied in energy systems such as solar collector, heat exchanger and heat pipes. Dynamic viscosity of the nanofluids is among the most important features affecting their thermal behavior and heat transfer ability. Several predictive models, by employing various methods such as Artificial Neural Network, Support Vector Machine and mathematical correlations, have been proposed for estimating dynamic viscosity based on the influential factors such as size, type and volume fraction of nano particles and their temperature. The precision of the models depends on different elements such as the employed approach for modeling, input variables and the structure of the model. In order to have an accurate model for estimating the dynamic viscosity, it is necessary to consider all of the affecting factor. In this regard, the current study aim to review the researches concerns the applications of machine learning methods for dynamic viscosity modeling of nanofluids in order to provide deeper insight for the scientists. According to the reviewed scientific sources, the structure of model, such as number of neurons and layers in artificial neural network (ANN), the applied activation function, and utilized algorithm are the most influential factors on the accuracy of the model. Moreover, based on the studies considered both ANN and mathematical correlations, ANNs are more accurate and confident for estimating the nanofluids’ dynamic viscosity. The majority of the studies in this field used temperature and concentration of nanofluids as input data for their models, while size of nanostructures and shear rate are considered in some researches in addition to mentioned variables.
116 citations
109 citations
TL;DR: In this article, the current theoretical models for nanofluid viscosity prediction are only applicable across a wide range of convective heat transfer phenomena, such as convective convective heating and cooling.
Abstract: Nanofluid viscosity is an important physical property in convective heat transfer phenomena. However, the current theoretical models for nanofluid viscosity prediction are only applicable across a ...
106 citations
TL;DR: In this article, an overview of solar energy systems is represented, and afterwards, applications of hybrid nanofluids in various solar technologies, especially solar thermal, are reviewed in order to gain a deeper insight into the advantages of using nanofluidic systems.
Abstract: Hybrid nanofluids have several advantages compared with the conventional types due to their modified properties. Their enhanced thermophysical and rheological properties make them more appropriate for solar energy systems. In this review paper, an overview of solar energy systems is represented, and afterwards, applications of hybrid nanofluids in various solar technologies, especially solar thermal, are reviewed. Comparison between the nanofluidic systems, and the conventional ones is performed in order to gain a deeper insight into the advantages of using nanofluids. According to the results of the reviewed studies, the most important reason for performance enhancement of nanofluidic solar energy systems can be attributed to the improved thermal properties of the convective fluid. In addition, it can be concluded that the nanofluids’ specifications such as concentration of the nanostructures, type of the solid phase, etc., have significant impact on the behavior of the considered systems.
TL;DR: In this paper, two ways of arranging cooling components: liquid filled battery cooling systems (LfBS) and liquid circulated battery cooling system (LcBS) are presented for both water and nanofluid as cooling media at 2C and 4C discharge rates.
TL;DR: In this paper, a comprehensive review is carried out on the cooling approaches mentioned above and their advantages and disadvantages, and the results show that the configuration of cooling system noticeably influence their performance, and using some ideas such as applying nanofluids, as the fluids with enhanced thermal properties, can be useful for heat transfer enhancement.
TL;DR: The National Natural Science Foundation of China, Hubei province and the Provincial Natural Science foundation of China as discussed by the authors, Key Project of ESI Discipline Development and Scientific Research Foundation of Wuhan University of Technology
Abstract: The National Natural
Science Foundation of China, Hubei
Provincial Natural Science Foundation of China, Key Project of ESI Discipline Development
of Wuhan University of Technology and the Scientific Research Foundation of Wuhan University of
Technology.
TL;DR: It is concluded that the structure of the NN, including numbers of hidden layer and neurons, can noticeably influence their performance and, compared with mathematical correlations, obtained by curve fitting, ANNs are more accurate.
Abstract: Intelligence methods, including Artificial Neural Networks (ANNs) and Support Vector Machine, are among the popular approaches for modeling the engineering systems with high accuracy. Nanofluid’s thermal conductivity depends on several factors such as the dimensions of nanoparticles, their concentration, synthesis method and temperature. Intelligence methods are appropriate tools to precisely estimate nanofluids’ thermal conductivity. Different methods and structures are used for the modeling of this property. In the present article, the related studies, using intelligence methods in thermal conductivity estimation, are comprehensively reviewed. According to the literature review, the accuracy of the predictive models has an association with their structure, utilized functions, selected input variables and employed algorithm. For instance, compared with mathematical correlations, obtained by curve fitting, ANNs are more accurate. Moreover, it is concluded that the structure of the NN, including numbers of hidden layer and neurons, can noticeably influence their performance. In the reviewed articles, trial and error are performed to distinguish the most favorable structure of ANNs. Due to the dependency of the models on the input variable, considering all the factors affecting the nanofluid’s thermal conductivity results in higher precision of the models.
TL;DR: In this article, a neural network was used to predict the heat transfer coefficient and the heat conductivity resistance equation for a heat pipe heat exchanger, and the results showed that the network with an accuracy of 0.9938 was able to accurately evaluate the results obtained in this study.
TL;DR: In this article, four LSSVM-based algorithms, namely GA-LSSVM, PSO-LSVM, HGAPSO-LssVM, and ICA-LCSVM, were employed to model the dynamic viscosity of Al2O3/water.
Abstract: Due to the enhanced thermophysical specifications of nanofluids, such as thermal conductivity, these types of fluids are appropriate candidates for heat transfer fluids. Nanostructure dispersion in the base fluid increases the dynamic viscosity which affects fluid flow in thermal devices. In order to facilitate design of thermal devices, it is crucial to have accurate predictive models for thermophysical properties of nanofluids. Dimensions of nanoparticles, working temperature and the concentration of nano-sized particles in the fluid are among the most influential factors in predicting dynamic viscosity of nanofluids. In the present research, four LSSVM-based algorithms including GA-LSSVM, PSO-LSSVM, HGAPSO-LSSVM and ICA-LSSVM are employed to model the dynamic viscosity of Al2O3/water. Results revealed that the generated models are accurate tools to calculate the dynamic viscosity of the nanofluid on the basis of the mentioned variables. The highest obtained coefficient of correlation belongs to GA-LSSVM which is equal to 0.9871, while this value for PSO-LSSVM, HGAPSO-LSSVM, and ICA-LSSVM algorithms are 0.9855, 0.9855, and 0.9846, respectively. Another utilized criterion for evaluating model accuracy is MSE value. Results revealed that the MSE values for HGAPSO-LSSVM, GA-LSSVM, PSO-LSSVM, and ICA-LSSVM are 0.00854, 0.00855, 0.00896 and 0.00979, respectively.
TL;DR: In this paper, the authors describe the effect of dynamic viscosity on the heat transfer and flow of fluids with nanostructures, and the authors propose a method to improve the thermophysical properties of these types of fluids.
Abstract: Dynamic viscosity considerably affects the heat transfer and flow of fluids. Due to improved thermophysical properties of fluids containing nanostructures, these types of fluids are widely employed...
TL;DR: In this paper, the feasibility of technical design of WHR from a gas engine to use in electrical power generation for Unit-8, Tehran Cement factory was studied and compared.
Abstract: Cement production is one of the most energy-intensive industries in the world which has high potential for heat recovery. This paper has studied the feasibility of technical design of WHR from a gas engine to use in electrical power generation for Unit-8, Tehran Cement factory. Two different approaches for energy recovery are proposed and compared. In the first scenario, which uses only one heat recovery boiler, total mixed gas enters the boiler before going into the pre-heater and grid cooler; however, in the second scenario, there is a vapor mixture that goes toward the steam turbine. In the current study, it is focused on analyzing and design of the gas engine which its calculations are done under FORTRAN codes, and the final configuration is made as a software. It should be noted that air conditioning of management building in the factory is involved in gas engine heat load calculations. The third analytical part considered in the codes is calculation processes of the combined heat recovery system coupled with a gas engine. Based on obtained results, the amount of recovered heat for the 1st and 2nd scenarios were 23931 kJ/s and 21253 kJ/s, respectively. In addition, the efficiencies of the power generation cycles for the 1st and 2nd scenarios were equal to 23.5% and 22.2%, respectively.
TL;DR: In this article, the effect of utilizing porous substrates on thermal and hydraulic performance of double-layered microchannel heat sinks (MCHSs) is comprehensively analyzed.
TL;DR: In this paper, a comprehensive literature review is performed on the nanofluids' applications in heat pipes, based on reviewed studies, it is observed that there must be a selected amount of concentration for the high-performance utilization of nanoparticles; high concentration of nanop particles causes a higher thermal resistance which is mainly attributed to increment in the dynamic viscosity and the higher possibility of particles' agglomeration.
Abstract: Nanotechnology is widely used in heat transfer devices to improve thermal performance. Nanofluids can be applied in heat pipes to decrease thermal resistance and achieve a higher heat transfer capability. In the present article, a comprehensive literature review is performed on the nanofluids’ applications in heat pipes. Based on reviewed studies, nanofluids have a high capacity to boost the thermal behavior of various types of heat pipes such as conventional heat pipes, pulsating heat pipes, and thermosyphons. Besides, it is observed that there must be a selected amount of concentration for the high-performance utilization of nanoparticles; high concentration of nanoparticles causes a higher thermal resistance which is mainly attributed to increment in the dynamic viscosity and the higher possibility of particles’ agglomeration. Enhancement in heat transfer performance is the result of increasing in nucleation sites and the intrinsically greater nanofluids’ thermal conductivity.
TL;DR: In this paper, a group method of data handling (GMDH) neural network was used to predict thermal resistance and effective thermal conductivity of pulsating heat pipes (PHPs) filled with water as working fluid.
Abstract: Thermal performance of pulsating heat pipes (PHPs) is dependent to several factors. Inner and outer diameter of tube, filling ratio, thermal conductivity, heat input, inclination angle, and length of each section are the most influential factors in the design process of PHPs. Since water is a conventional working fluid for PHPs, thermal resistance and effective thermal conductivity of PHPs filled with water are modeled by applying a GMDH (group method of data handling) neural network. The input data of the GMDH model are collected from other experimental investigations to predict the physical properties including thermal resistance and effective thermal conductivity of PHPs filled with water as working fluid. The accuracy of the introduced models are examined through the R2 tests and resulted in 0.9779 and 0.9906 for thermal resistance and effective thermal conductivity, respectively.
TL;DR: In this paper, a cogeneration system that includes a gas turbine, absorption chillers, boilers, and heat exchangers is modeled in EES software, and the system is studied in multiple scenarios.
TL;DR: A wide variety multi criteria decision making (MCDM) methods, investigated by various researchers, are presented to obtain effective criteria in selecting solar plants sites and solar plants technologies.
Abstract: Renewable energies have many advantages and their importance is rising owing to gravely mounting concerns for environmental issues and lack of fossil fuels in the future. Solar energy, well acknowledged as an inexhaustible source of energy, is developing dramatically for different purposes such as desalination and electricity generation. Appropriate solar power plant is very important factor for power generation due to its cost and other constraints. The applied technology is as important as the solar power plants location. In this paper, a wide variety multi criteria decision making (MCDM) methods, investigated by various researchers, are presented to obtain effective criteria in selecting solar plants sites and solar plants technologies. There is not any comprehensive research providing all required criteria for decision making for site and technology selection. Based on the reviewed researches, weight of each criterion depends on many factors such as region, economy, accessibility, power network, maintenance costs, operating costs, etc. The important criteria for site selection are represented and investigated thoroughly in this review paper. © 2019. CBIORE-IJRED. All rights reserved Article History : Received June 17 th 2017; Received in revised form March 7 th 2018; Accepted June 16 th 2018; Available online How to Cite This Article : Ghasempour, R., Nazari, M.A., Ebrahimi, M., Ahmadi, M.H. and Hadiyanto, H. (2019) Multi-Criteria Decision Making (MCDM ) Approach for Selecting Solar Plants Site and Technology: A Review. Int Journal of Renewable Energy Development, 8(1), 15-25. https://doi.org/10.14710/ijred.8.1.15-25
TL;DR: In this article, the required pumping power and number of PV modules for a photovoltaic water pumping system (PVWPS) were determined with the aim of supplying the water demand in a rice paddy located in north of Iran.
TL;DR: In this article, the accurate prediction of SiO2 nanofluid effect on the SiO 2 was investigated, and the results showed that the prediction was accurate in terms of the number of atoms and the amount of energy consumed.
Abstract: Nanofluids have found extended applications in different industrial and engineering systems nowadays. This study aims to investigate the accurate prediction of SiO2 nanofluid effect on the ...
TL;DR: In this paper, the effects of using nanofluids in several types of heat pipes are reviewed, based on the results of the literature review, applying nanostructures in the base fluid can significantly reduce the thermal resistance of heat pipe compared with utilizing pure as operating fluid.
Abstract: 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.