A review on the utilized machine learning approaches for modeling the dynamic viscosity of nanofluids

Mahdi Ramezanizadeh, Mohammad Hossein Ahmadi, Mohammad Alhuyi Nazari, Milad Sadeghzadeh and Lingen Chen

Renewable and Sustainable Energy Reviews, 2019, vol. 114, issue C, -

Abstract: Nanofluids are broadly applied in energy systems such as solar collector, heat exchanger and heat pipes. Dynamic viscosity of the nanofluids is among the most important features affecting their thermal behavior and heat transfer ability. Several predictive models, by employing various methods such as Artificial Neural Network, Support Vector Machine and mathematical correlations, have been proposed for estimating dynamic viscosity based on the influential factors such as size, type and volume fraction of nano particles and their temperature. The precision of the models depends on different elements such as the employed approach for modeling, input variables and the structure of the model. In order to have an accurate model for estimating the dynamic viscosity, it is necessary to consider all of the affecting factor. In this regard, the current study aim to review the researches concerns the applications of machine learning methods for dynamic viscosity modeling of nanofluids in order to provide deeper insight for the scientists. According to the reviewed scientific sources, the structure of model, such as number of neurons and layers in artificial neural network (ANN), the applied activation function, and utilized algorithm are the most influential factors on the accuracy of the model. Moreover, based on the studies considered both ANN and mathematical correlations, ANNs are more accurate and confident for estimating the nanofluids’ dynamic viscosity. The majority of the studies in this field used temperature and concentration of nanofluids as input data for their models, while size of nanostructures and shear rate are considered in some researches in addition to mentioned variables.

Keywords: Nanofluid; Dynamic viscosity; Shear rate; Artificial neural network; Review (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.rser.2019.109345

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