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 and Truong Khang Nguyen

Physica A: Statistical Mechanics and its Applications, 2019, vol. 521, issue C, 493-500

Abstract: 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.

Keywords: Nanofluid; Thermal performance factor; MWCNTs; Reynolds number; ANN; Margin of deviation (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437119300603
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:521:y:2019:i:c:p:493-500

DOI: 10.1016/j.physa.2019.01.057

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().