Articles
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.
