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Predicting the effect of functionalized multi-walled carbon nanotubes on thermal performance factor of water under various Reynolds number using artificial neural network


Abdulwahab A. Alnaqi, Sina Sayyad Tavoos Hal, Alireza Aghaei, Mehdi Soltanimehr, Masoud Afrand, Truong Khang Nguyen*

Source title: 
Physica A: Statistical Mechanics and its Applications, 521: 493-500, 2019 (ISI)
Academic year of acceptance: 

The aim of the current paper is modeling the thermal performance factor of water under influence of functionalized multi-walled carbon nanotubes. For this purpose, artificial neural networks (ANNs) have been employed. The thermal performance factor of the nanofluid is related to the drop in pressure and heat transfer. Therefore, usually the data associated with it does not follow a specific trend. In such cases, the neural network can be used well. Here, an optimal neural network was designed with 65 neurons, the inputs of which were Reynolds number and volume fractions. Moreover, 78 data were used for modeling, which included 62 data for training and 16 data for testing the model. With the aim of evaluation of the precision of the modeling by ANN, the experimental results were compared with neural network outputs in all cases. The results revealed that optimal ANN model has adequate accurateness to predict the complex trend of thermal performance factor.