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   Prediction and modeling of MWCNT/Carbon (60/40)/SAE 10 W 40/SAE 85 W 90(50/50) nanofluid viscosity using artificial neural network (ANN) and self-organizing map (SOM)  
   
نویسنده Heydar Maddah ,Reza Aghayari ,Mohammad Hossein Ahmadi ,Mohammad Rahimzadeh ,Nahid Ghasemi
منبع journal of thermal analysis and calorimetry - 2018 - دوره : 134 - شماره : 3 - صفحه:2275 -2286
چکیده    the present study investigated and predicted the relative viscosity of multiwall carbon nanotube/carbon (60/40)/sae 10 w 40/(society of automotive engineers) sae 85 w 90(50/50) at different temperatures and the different volumetric fraction by applying artificial neural networks based on experimental data. several samples of nanofluid were provided by adding nanoparticles in 0%, 0.1%, 0.3%, 0.5%, 0.8% and 1% volumetric concentrations. dynamic viscosity of the nanofluid was measured in the temperature range of 25–50 °c. initially, a self-organizing 6 × 6 hexagonal network was used. a total of 36 neurons were chosen. the winner neuron was neuron 25, having assigned the most data to itself. then 25 neurons were used for the neural network, which had a very good performance. temperature and concentration were considered as input variables, while the relative viscosity was the output parameter of the neural network. mean-square error, correlation coefficient and standard deviation were utilized in order to assess the results. based on the obtained results, the best model was double-layer perceptron neural network with 25 neurons. the mean square error, correlation coefficient and standard deviation were equal to 2.0193e−008, 1 and 0.00021082, respectively. therefore, the model is able to predict relative viscosity with appropriate accuracy.
کلیدواژه Nanofluid ,Viscosity ,Artificial neural networks ,Self-organizing map
آدرس Payame Noor University (PNU), Department of Chemistry, Iran, Payame Noor University (PNU), Department of Chemistry, Iran, Shahrood University of Technology, Iran, Golestan University, Department of Mechanical Engineering, Iran, Arak Branch, Islamic Azad University, Department of Chemistry, Iran
 
     
   
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