scholarly journals Application GMDH artificial neural network for modeling of Al2O3/water and Al2O3/Ethylene glycol thermal conductivity

2018 ◽  
Vol 36 (3) ◽  
pp. 773-782 ◽  
Author(s):  
Mohammad Ahmadi ◽  
Fatemeh Hajizadeh ◽  
Mohammad Rahimzadeh ◽  
Mohammad Shafii ◽  
Ali Chamkha ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Ethylene Glycol ◽  
Artificial Neural

Modeling Thermal Conductivity Ratio of CuO/Ethylene Glycol Nanofluid by Using Artificial Neural Network

2018 ◽  
Vol 388 ◽  
pp. 39-43 ◽  
Author(s):  
Mohammad Alhuyi Nazari ◽  
Mohammad Hossein Ahmadi ◽  
Giulio Lorenzini ◽  
Heydar Maddah ◽  
Morteza Fahim Alavi ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Ethylene Glycol ◽  
Correlation Coefficient ◽  
Thermal Conductivity Ratio ◽  
Conductivity Ratio ◽  
Artificial Neural ◽  
Input Variables ◽  
Trained Neural Network

The thermal conductivity of nanofluids depends on several factors such as temperature, concentration, and temperature. These parameters have the most significant effect on thermal conductivity compared with other factors. In the present study, the accuracy of trained Perceptron neural network with 10 neurons and three input variables including size of nanoparticles, temperature, and concentration is evaluated. The sum of squared errors and the correlation coefficient of the trained neural network are equal to 0.99293 and 0.00031, respectively.

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