Thermal conductivity of non-Newtonian nanofluids: Experimental data and modeling using neural network

2011 ◽  
Vol 54 (5-6) ◽  
pp. 1017-1023 ◽  
Author(s):  
M. Hojjat ◽  
S.Gh. Etemad ◽  
R. Bagheri ◽  
J. Thibault
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Thermal Conductivity

Designing artificial neural network on thermal conductivity of Al2O3–water–EG (60–40 %) nanofluid using experimental data

2016 ◽  
Vol 126 (2) ◽  
pp. 837-843 ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
Mohammad Reza Hassani Ahangar ◽  
Davood Toghraie ◽  
Mohammad Hadi Hajmohammad ◽  
Hadi Rostamian ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Artificial Neural

scholarly journals Thermal Conductivity of Cu-Zn Hybrid Newtonian Nanofluids: Experimental Data and Modeling using Neural Network

2015 ◽  
Vol 127 ◽  
pp. 561-567 ◽  
Author(s):  
Sandeep Kumar Mechiri ◽  
Vasu V. ◽  
Venu Gopal A. ◽  
Satish Babu R.
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Thermal Conductivity

scholarly journals Validation and Optimization of Thermophysical Properties for Thermal Conductivity and Viscosity of Nanofluid Engine Oil using Neural Network

2020 ◽  
Vol 3 (1) ◽  
pp. 53-60
Author(s):  
Amin Moslemi Petrudi ◽  
Masoud Rahmani
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Thermal Conductivity ◽  
Optimization Algorithm ◽  
Thermophysical Properties ◽  
Volume Fraction ◽  
Engine Oil ◽  
Coefficient Of Determination ◽  
Neural Network Modeling ◽  
Nsga Ii

In this study, the thermophysical properties of thermal conductivity and viscosity of a motor oil nanofluid were investigated using experimental data and artificial neural network. NSGA II optimization algorithm was used to maximize thermal conductivity and minimum viscosity with changes in temperature and volume fraction of nanofluids. Also, to obtain the viscosity and thermal conductivity values in terms of nanofluid temperature and volume fraction with 174 experimental data, neural network modeling was performed. Input data include temperature and volume fraction, and output is viscosity and thermal conductivity. Various indices such as R squared and Mean Square Error (MSE) have been used to evaluate the accuracy of modeling in the prediction of viscosity and thermal conductivity of nanofluids. The coefficient of determination R squared is 0.9989 indicating acceptable agreement with the experimental data. In order to optimize and finally results as an objective function, the optimization algorithm is presented and the Parto front and its corresponding optimum points are presented where the maximum optimization results of thermal conductivity and viscosity occur at 1% volume fraction.

scholarly journals Thermal Conductivity Modeling of Propylene Glycol - Based Nanofluid Using Artificial Neural Network

2018 ◽  
Vol 14 (1) ◽  
pp. 5281-5291 ◽  
Author(s):  
R. A. Mohamed ◽  
D. M. Habashy
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Propylene Glycol ◽  
Network Model ◽  
Neural Network Model ◽  
Network System ◽  
The Neural Network ◽  
Neural Network System

The article introduces artificial neural network model that simulates and predicts thermal conductivity and particle size of propylene glycol - based nanofluids containing Al2O3 and TiO2 nanoparticles in a temperature rang 20 - 80oc. The experimental data indicated that the nanofluids have excellent stability over the temperature scale of interest and thermal conductivity enhancement for both nanofluid samples. The neural network system was trained on the available experimental data. The system was designed to find the optimal network that has the best training performance. The nonlinear equations which represent the relation between the inputs and output were obtained. The results of neural network model and the theoretical models of the proposed system were performed and compared with the experimental results. The neural network system appears to yield the best fit consistent with experimental data. The results of the paper demonstrate the ability of neural network model as an excellent computational tool in nanofluid field.

Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network

2014 ◽  
Vol 118 (1) ◽  
pp. 287-294 ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
Seyfolah Saedodin ◽  
Mehdi Bahiraei ◽  
Davood Toghraie ◽  
Omid Mahian ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Conductivity Modeling
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