Journal ArticleDOI
Thermal conductivity modeling of nanofluids with ZnO particles by using approaches based on artificial neural network and MARS
Akbar Maleki,Milad Elahi,Mamdouh El Haj Assad,Mohammad Alhuyi Nazari,Mostafa Safdari Shadloo,Narjes Nabipour +5 more
TLDR
In this paper, three methods including MARS, artificial neural network (ANN) with Levenberg-Marquardt for training and GMDH are employed for thermal conductivity of the nanofluids containing ZnO particles.Abstract:
Nanofluids are attractive alternatives for the current heat transfer fluids due to their remarkably higher thermal conductivity which leads to the improved thermal performance. Nanofluids are applicable in porous media for improving their heat transfer. Proposing accurate models for forecasting this feature of nanofluids can facilitate and accelerate the design and modeling of nanofluids’ thermal mediums with porous structure. In the present study, three methods including MARS, artificial neural network (ANN) with Levenberg–Marquardt for training and GMDH are employed for thermal conductivity of the nanofluids containing ZnO particles. The confidence of the models is compared according to various criteria. It is observed that the most accurate model is obtained by using ANN with Levenberg–Marquardt followed by GMDH and MARS. R2 of the mentioned models are 0.9987, 0.9980 and 0.9879, respectively. Finally, sensitivity analysis is performed to find the importance of the input variables and it is concluded that the thermal conductivity of the base fluids has the highest importance followed by volume fraction of solid phase, size of particles and temperature.read more
Citations
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A review of melting and freezing processes of PCM/nano-PCM and their application in energy storage
Sara Rostami,Masoud Afrand,Amin Shahsavar,Mohsen Sheikholeslami,Rasool Kalbasi,Saeed Aghakhani,Mostafa Safdari Shadloo,Hakan F. Oztop +7 more
TL;DR: In this article, a detailed illustration of phase change materials and their working principle, different types, and properties are provided, and a characteristic example of PCM in solar energy storage and the design of PCMs are reviewed and analyzed.
Journal ArticleDOI
Applications of nanofluids containing carbon nanotubes in solar energy systems: A review
Mohammad Ghalandari,Akbar Maleki,Arman Haghighi,Mostafa Safdari Shadloo,Mohammad Alhuyi Nazari,Iskander Tlili +5 more
TL;DR: In this paper, the thermal conductivity of nanofluids with carbon nanotubes (CNTs) is investigated and suggested for future studies in this field which can lead to further enhancement in the efficiency of solar systems incorporating the investigated nanof-luids.
Journal ArticleDOI
Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System
Prabhakar Sharma,Zafar Said,Anurag Kumar,Sandro Nižetić,Ashok K. Pandey,Anh Le Tuan Hoang,Zuohua Huang,Asif Afzal,Changhe Li,Anh-Tuan Le,Xuan Phuong Nguyen,Viet D. Tran +11 more
TL;DR: In this paper , a review of machine learning techniques employed in the nanofluid-based renewable energy system, as well as new developments in machine learning research, is presented.
Journal ArticleDOI
Estimation of Pressure Drop of Two-Phase Flow in Horizontal Long Pipes Using Artificial Neural Networks
Mostafa Safdari Shadloo,Mostafa Safdari Shadloo,Amin Rahmat,Arash Karimipour,Somchai Wongwises,Somchai Wongwises +5 more
TL;DR: In this article, an artificial neural networks (ANNs) model is presented by employing a large number of experimental data to predict the pressure drop for a wide range of operating conditions, pipe diameters, and fluid characteristics.
Journal ArticleDOI
A Review on the Control Parameters of Natural Convection in Different Shaped Cavities with and without Nanofluid
Sara Rostami,Saeed Aghakhani,Ahmad Hajatzadeh Pordanjani,Masoud Afrand,Goshtasp Cheraghian,Hakan F. Oztop,Mostafa Safdari Shadloo +6 more
TL;DR: A review of recent natural convection studies can be found in this article, where the authors classified the articles based on effective parameters such as magnetic forces, fin, porous media and cavity angles.
References
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Multivariate Adaptive Regression Splines
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Multivariate adaptive regression splines and neural network models for prediction of pile drivability
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Thermal conductivity of Cu/TiO2–water/EG hybrid nanofluid: Experimental data and modeling using artificial neural network and correlation☆
Mohammd Hemmat Esfe,Somchai Wongwises,Ali Naderi,Amin Asadi,Mohammad Reza Safaei,Hadi Rostamian,Mahidzal Dahari,Arash Karimipour +7 more
TL;DR: In this article, two new correlations for predicting the thermal conductivity of studied hybrid nanofluids, in terms of solid concentration and temperature, are proposed that use an artificial neural network (ANN) and are based on experimental data.
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A review of thermal conductivity of various nanofluids
TL;DR: In this article, several experimental and theoretical studies conducted on the thermal conductivity of nanofluids are represented and investigated based on the reviewed studies, various factors affect thermal conductivities such as temperature, the shape of nanoparticles, concentration and etc.
Journal ArticleDOI
Experimental investigation of convective heat transfer augmentation for car radiator using ZnO–water nanofluids
TL;DR: In this paper, water-based nanofluids have been used to enhance the heat transfer performance of a car radiator by adding ZnO nanoparticles to base fluid in different volumetric concentrations (0.01, 0.08), 0.2% and 0.3%).