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آموزش مهندسی ایران (16072316) (102)pp. 103-117
This paper examines the role of entrepreneurship in energy engineering education, emphasizing the importance of incorporating entrepreneurial skills into the curriculum. Given the global challenges associated with sustainable energy, entrepreneurship is identified as a crucial driver for developing innovative technologies in the energy sector. The paper addresses the obstacles and challenges of integrating entrepreneurship into energy engineering education programs and highlights the need for curriculum reform, enhanced industry collaboration, and the utilization of appropriate resources to foster entrepreneurship. Additionally, the paper underscores the importance of balancing technical and entrepreneurial content within energy engineering courses, and the necessity of providing suitable incentives and opportunities for students to engage in entrepreneurial activities. The societal demand for sustainable energy solutions and the pressing global challenges underscore the need for energy engineering students to develop entrepreneurial skills. This development is presented as a vital strategy for driving economic growth and fostering innovation in the energy industry. In conclusion, the paper argues that the integration of entrepreneurship into energy engineering education is essential for preparing students to contribute effectively to the energy sector’s evolution and to address the urgent need for sustainable and innovative energy solutions.
International Journal of Engineering, Transactions B: Applications (1728144X) 34(8)pp. 1974-1981
Neural networks are powerful tools for evaluating the thermophysical characteristics of nanofluids to reduce the cost and time of experiments. Dynamic viscosity is an important property in nanofluids that usually needs to be accurately computed in heat transfer and nanofluid flow problems. In this paper, the rheological properties of nanofluid phase change material containing mesoporous silica nanoparticles are predicted by the artificial neural networks (ANNs) method based on the experimental database reported in literature. Experimental inputs include nanoparticle mass fractions (0-5 wt.%), temperatures (35-55℃), shear rates (10-200 s-1), targets include dynamic viscosities and shear stresses. A multilayer perceptron feedforward neural network with Levenberg-Marquardt back-propagation training algorithm is utilized to predict rheological properties. The optimal network architecture consists of 22 neurons in the hidden layer based on the minimum mean square error (MSE). The results showed that the developed ANN has an MSE of 6.67×10-4 and 6.55×10-3 for the training and test dataset, respectively. The predicted dynamic viscosity and shear stress also have the maximum relative error of 6.26 and 0.418%, respectively. © 2021 Materials and Energy Research Center. All rights reserved.
International Journal of Energy Research (1099114X) 45(10)pp. 15092-15109
The weak thermal conductance of a phase change material (PCM) can be intensified by dispersing nanostructured materials called nano-PCM. Accurate thermal conductivity (TC) prediction of nano-PCM is essential to evaluate heat transport during phase change processes, namely, melting and solidification. The present study develops an artificial neural network (ANN) to forecast the TC of n-octadecane as a PCM with dispersed oxide nanoparticles. A total of 122 experimental datasets from existing literature with a wide range of temperatures (5-60°C), nanoparticles (CuO, Al2O3, TiO2, and mesoporous SiO2), nanoparticle mass fractions (0.5-12 wt%) are compiled to train a multi-layered feed-forward ANN with Levenberg-Marquardt back-propagation algorithm. An optimal architecture of the neural network is acquired by varying the number of network hidden layers, the number of neurons in each layer, and the transfer function of layers. The minimum mean square error (MSE) of 1.3512 × 10−5 is obtained for the best developed ANN. Results show that average absolute deviation (AAD) of 0.002458, mean absolute percentage error (MAPE) of 0.8260%, and correlation coefficient (R) of 0.999964948 are achieved for training data. Moreover, MAPE, AAD, and R values are, respectively, 0.9478, 0.002167, and 0.9999715861 for testing data. The maximum percentage errors of ANN computed values are 2.31%, and 0.812% for liquid and solid phases, respectively. This indicates that the ANN model accurately predicts the enhanced TC of nano-PCM across various oxide nanoparticles, temperatures, and nanoparticle loadings. © 2021 John Wiley & Sons Ltd.
Sustainable Energy Technologies and Assessments (22131388) 39
In this paper, an artificial neural network (ANN) is developed to assess hybrid photovoltaic thermal (PVT) systems for grid-connected (GC) electricity generation, space heating and domestic hot water providing in heating dominated regions of Iran. To do so, monthly and annual performance of a 5 kWp GCPVT system is simulated for a single-family house. The simulation results show that the GCPVT system is very promising whereas the annual yield factor varies from 1506 kWh/kWp to 1891 kWh/kWp. Also, an appropriate solar fractions for covering hot water are achieved in a range from 74.5% to 49.4%. A multilayered perceptron feed-forward neural network which is trained by Levenberg-Marquardt algorithm is used to predict AC electrical energy and solar thermal output of the GCPVT system. The developed ANN is based on global horizontal irradiance, ambient temperature, ambient relative humidity and wind speed as inputs. The proposed configuration of ANN presents a high accuracy in predicting output energy of the GCPVT system according to minimum mean square error and maximum correlation coefficient. Analysis of variance is performed to determine the significant control parameters influencing the output energy of the GCPVT system. © 2020 Elsevier Ltd
Journal of Energy Storage (2352152X) 30
In this paper, heat transfer during the melting process of n-octadecane as a phase change material (PCM) is experimentally studied. This study is followed by an artificial neural network (ANN) to predict the melting characteristics of PCM. Experiments are performed in a rectangular enclosure subjected to a uniform heat flux in one vertical side. Melting heat transfer is characterized by observing the solid-liquid interface and recording the temperature distribution in the enclosure. Experimental results indicate that heat transfer during the melting process is dominated by natural convection. A multilayered perceptron feed-forward neural network trained by the Levenberg-Marquardt algorithm is used to predict the Nusselt number and the melted volume fraction. Rayleigh, Fourier and Stefan numbers are set as input parameters of the network. The optimal structure of the ANN to predict the Nusselt number show high accuracy in estimating the heat transfer characteristics during melting by achieving the mean square error and the correlation coefficient of 4.42 × 10−6 and 0.999, respectively. Based on the proposed ANN, the majority of the data falls within ±6.23% and ±6.54% of the Nusselt number and the melted volume fraction, respectively. © 2020 Elsevier Ltd
Journal of Energy Storage (2352152X) 32
In this paper, a multi-objective optimization was performed to achieve the minimum cooling and heating loads in a residential building integrated with phase change material (PCM). The methods applied to fulfill this objective were numerical modeling by EnergyPlus, Grouped Method of Data Handling (GMDH) type of Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II). In this study, design variables were melting temperature and thickness of PCM, thermal resistance of exterior walls, internal gain and infiltration rate. Additionally, objective functions included annual cooling and heating loads of the building that should be minimized. Therefore, first, EnergyPlus software was used to calculate the values of objective functions and to train the neural network. Afterward, the GMDH-type neural network was applied to derive polynomials computing the objective functions from input variables. Then, Pareto optimal points for objective functions were obtained through using these polynomials and NSGA-II multi-objective optimization. Finally, the optimum point was determined by a financial analysis. According to the obtained results, the thickness and melting temperature of the optimum PCM layer in the residential building in Tehran were equal to 0.032 m and 24.58°C, respectively. © 2020 Elsevier Ltd
Applied Thermal Engineering (13594311) 167
Transient thermal management of electronics is vital to increase their reliability. In the present study, heat transfer characteristics of a phase change material (PCM) heat sink was analyzed experimentally. Then, an artificial neural network (ANN) based on feedforward back-propagation multilayered perceptron (MLP), was utilized to predict transient heat transfer coefficient. Experiments were conducted at three different powers applied to the heat sink in the absence and presence of PCM. It was found that the use of PCM reduces the transient temperature while increasing the time to reach a steady-state temperature. Transient heat transfer during melting at a constant heat flux was characterized by dimensionless numbers including Rayleigh, Fourier and Stefan numbers. An optimal structure MLP network with 15 neurons in the hidden layer was obtained with trial and error method to predict the Nusselt number (Nu) during melting. The correlation analysis results based on ANN for predicting the Nu during PCM melting indicated the high accuracy of the neural network. © 2019 Elsevier Ltd
International Journal of Heat and Mass Transfer (00179310) 109pp. 134-146
This paper presents an experimental investigation on the melting process of n-octadecane as a phase change material (PCM) with dispersed titanium oxide (TiO2) nanoparticles. Experiments were performed in a rectangular enclosure heated at constant rates from one vertical side corresponding to Rayleigh numbers in the range 0.57 × 108–43.2 × 108and Stefan number in the range 5.7–23.8. The rheological behavior of liquid PCM/TiO2at the mass fractions of 2 and 4% tended to Bingham fluids, thus the melting experiment was conducted for Bingham numbers in the range 0–31.1. Heat transfer during melting was characterized by visualizing the solid-liquid interface as well as recording the temperature distribution in the enclosure. Experimental results showed that at the initial stage of melting, heat transferred by conduction, and at later times, natural convection dominated heat transfer. Dispersing TiO2nanoparticles led to increase in Bingham number and consequently the natural convection and melting rate deteriorated. Two correlations were proposed to predict the Nusselt number and melted volume fraction as a function of Fourier number, Rayleigh number, Stefan number, Bingham number and mass fraction of nanoparticles. © 2017 Elsevier Ltd
Energy Conversion and Management (01968904) 138pp. 162-170
The solidification process of n-octadecane as a phase change material (PCM) with dispersed titanium dioxide (TiO2) nanoparticles was experimentally studied. Experiments were performed in a rectangular enclosure cooled from one vertical side corresponding to the solid Stefan numbers in the range 0.17–0.239. The Rayleigh numbers at the initial of experiment were in the range 0.92–18.3 × 106. The rheological behavior of liquid PCM/TiO2samples at higher concentrations tended to Bingham fluids, thus the solidification experiments were conducted for Bingham numbers in the range 0–2.17. The solidification process was characterized by visualizing the progression of solid-liquid interface as well as recording the temperature distribution inside the enclosure. Experimental results showed that heat conduction was the dominant mode of heat transfer during the solidification. Dispersing TiO2nanoparticles led to enhance in thermal conductance and consequently the increase in solidified volume. An increase of 7%, 9% and 18% in solidified volume fraction was observed at the end of solidification for the mass fractions of 1 wt.%, 2 wt.% and 4 wt.%, respectively. A universal correlation was proposed to predict the solidified volume fraction as a function of Fourier number, Rayleigh number, solid Stefan number, Bingham number and mass fraction of nanoparticles with an error below 11%. © 2017 Elsevier Ltd
Heat and Mass Transfer (09477411) 52(8)pp. 1621-1631
In the present study, carbon-based nanomaterials including multiwalled carbon nanotubes (MWCNTs) and vapor-grown carbon nanofibers (CNFs) were dispersed in n-octadecane as a phase change material (PCM) at various mass fractions of 0.5, 1, 2 and 5 wt% by the two-step method. The transient plane source technique was used to measure thermal conductivity of samples at various temperatures in solid (5–25 °C) and liquid (30–55 °C) phases. The experimental results showed that thermal conductivity of the composites increases with increasing the loading of the MWCNTs and CNFs. A maximum thermal conductivity enhancement of 36 % at 5 wt% MWCNTs and 5 °C as well as 50 % at 2 wt% and 55 °C were experimentally obtained for n-octadecane/MWCNTs samples. Dispersing CNFs into n-octadecane raised the thermal conductivity up to 18 % at 5 wt% and 10 °C and 21 % at 5 wt% and 55 °C. However, the average enhancement of 19 and 21 % for solid and liquid phases of MWCNTs composite as well as 33 and 46 % for solid and liquid phase of CNFs promised a better heat transfer characteristics of MWCNTs in n-octadecane. A comparison between results of the present work and available literature revealed a satisfactory enhancement of thermal conductivity. For the investigated n-octadecane/MWCNTs and n-octadecane/CNFs composites, a new correlation was proposed for predicting the thermal conductivity as a function of temperature and nanomaterials loading. © 2015, Springer-Verlag Berlin Heidelberg.
International Communications in Heat and Mass Transfer (07351933) 73pp. 1-6
In the present paper, the effect of using a heat pipe on the melting and solidification behavior of a phase change material (PCM) in a vertical cylindrical test cell was experimentally studied. The experiments were performed using a constant temperature thermal reservoir to provide constant temperatures above and below the melting point for heating and cooling. The melting and solidification experiments were run in test cells with and without heat pipes. The experimental results indicate that utilizing a heat pipe in PCM test cell dramatically enhance the melting and solidification rate. Heat pipe surface temperature was measured during experiments. It shows heat pipe isothermally transmits heat very well. By applying different reservoir working temperature, it is concluded that a 15 °C increase in reservoir temperature in melting experiment with heat pipe almost decreases the melting time by 53% and a 10 °C decrease in temperature in solidification reduce the solidification time by 49%. The growth of solid layer and solid-liquid interface in PCM during solidification was experimentally investigated. © 2016 Elsevier Ltd.
Motahar, S. ,
Nikkam, N. ,
Alemrajabi, A.A. ,
Khodabandeh, R. ,
Toprak, M.S. ,
Muhammed, M. International Communications in Heat and Mass Transfer (07351933) 56pp. 114-120
In this research, mesoporous silica (MPSiO2) nanoparticles were dispersed in n-octadecane as an organic phase change material (PCM) in order to produce a novel composite for thermal storage. Stable PCMs containing 1wt.%, 3wt.% and 5wt.% MPSiO2 nanoparticles (PCM/MPSiO2) were fabricated by dispersing MPSiO2 in PCM. MPSiO2 particles were investigated by SEM and TEM techniques, which showed high order of porosity and spherical particles of ca. 300nm. The thermal conductivity in both solid and liquid phases was measured by transient plane source (TPS) technique in the temperature range of 5-55°C. A maximum thermal conductivity enhancement of 5% for 3wt.% MPSiO2 at 5°C, and 6% for 5wt.% MPSiO2 at 55°C was experimentally obtained. Moreover, it was observed that enhancement in thermal conductivity is non-monotonic in solid phase with increasing MPSiO2 particle loading. The viscosity results showed that for mass fractions of nanoparticles greater than 3% in liquid PCM, the behavior of liquid is non-Newtonian. Also, the viscosity of PCM containing MPSiO2 nanoparticles was measured to be increased up to 60% compared to the liquid PCM for 5wt.% MPSiO2 at 35°C. © 2014.
Motahar, S. ,
Nikkam, N. ,
Alemrajabi, A.A. ,
Khodabandeh, R. ,
Toprak, M.S. ,
Muhammed, M. International Communications in Heat and Mass Transfer (07351933) 59pp. 68-74
In the present study, titanium (IV) oxide (TiO2) nanoparticles were dispersed in n-octadecane to fabricate phase change material (PCM) with enhanced properties and behavior. Thermal conductivity (TC) and viscosity of n-octadecane/TiO2 dispersions were experimentally investigated using transient plane source (TPS) technique and rotating coaxial cylindrical viscometer, respectively. The results showed that the TC of n-octadecane/TiO2 dispersion depends on temperature and nanoparticle loading. A non-monotonic behavior of TC enhancement in both solid and liquid phases was observed. In solid phase, the maximum TC enhancement occurred at 3wt.% of nanoparticles. When the nanoparticle mass fraction was over 4% in liquid phase, the TC started to decrease. The rheological behavior of the n-octadecane/TiO2 samples indicated that dispersions with low nanoparticle mass fractions demonstrate Newtonian behavior, and for higher mass factions the shear-thinning behavior was observed. Shear stress vs. shear rate curves showed that the liquid phase of PCM behaves like a Bingham plastic fluid for mass fraction greater than 1%. As expected, the effective viscosity could be influenced by temperature. At the shear rate of 48.92s-1 for 3wt.% nanoparticles, the effective viscosity decreased by 26.8% while temperature increased from 35°C to 55°C. For the investigated n-octadecane/TiO2 dispersions, new thermophysical correlations are proposed for predicting TC and rheological properties. © 2014 Elsevier Ltd.
International Journal Of Thermodynamics (13019724) 13(4)pp. 153-160
In this paper, an exergetic performance analysis of unglazed transpired collectors (UTC), as well as an exergetic optimization of a typical UTC is performed. A steady-state model is used to calculate heat transfers and pressure drop through the perforated plate and back wall. In order to maximize the exergy efficiency, the optimization procedure is carried out for some important parameters including plate hole diameter and hole pitch. A maximum efficieny of 2.28% is obtained. In spite of all the thermal performance advantages, the exergetic efficiency of the UTC is significantly lower than its energetic efficiency. Other parameters such as incident solar radiation, approach velocity, plate hole diameter and pitch are examined in the parametric study.
International Journal of Hydrogen Energy (03603199) 34(5)pp. 2396-2407
This paper presents exergy analysis of a hybrid solid oxide fuel cell and gas turbine (SOFC/GT) system in comparison with retrofitted system with steam injection. It is proposed to use hot gas turbine exhaust gases heat in a heat recovery steam generator to produce steam and inject it into gas turbine. Based on a steady-state model of the processes, exergy flow rates are calculated for all components and a detailed exergy analysis is performed. The components with the highest proportion of irreversibility in the hybrid systems are identified and compared. It is shown that steam injection decreases the wasted exergy from the system exhaust and boosts the exergetic efficiency by 12.11%. Also, 17.87% and 12.31% increase in exergy output and the thermal efficiency, respectively, is demonstrated. A parametric study is also performed for different values of compression pressure ratio, current density and pinch point temperature difference. © 2008 International Association for Hydrogen Energy.
