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The artificial neural network analysis of the flow of thin film hybrid nanofluid with variable film thickness in a variable porous medium over a rotating disk

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Abstract

The thin film hybrid nanofluid (HNF) flow over a rotating stretching disk is considered for heat transfer (HT) enhancement applications. The water-based HNF consists of Ag and TiO2 nanoparticles (NPs). The film thickness is kept variable and the flow medium is also considered porous and variable. The variable porous space for the variable thickness of the thin film is the main focus of the present analysis. The variable thickness of the thin film improves heat transfer (HT) between the rotating disk and the surrounding fluid. Thicker film layers provide increased thermal resistance, reducing the (HT) rate, while thinner film layers enhance (HT) by minimizing the thermal resistance. Controlling the thickness of the film through the variable porous space allows for the optimization of (HT) in various engineering applications. Additionally, the presence of Ag and TiO2 nanoparticles (NPs) in the water-based HNF further enhances the heat transfer properties, making it an attractive option for heat transfer (HT) enhancement applications. Moreover, the stability of the variable thin film is more adjustable in the variable porous space. The artificial neural network is used to solve the problem and validate the obtained results, through Training, Testing, and error estimations.

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Data availability

The data that support the findings of this study are available upon reasonable request from the author.

Abbreviations

\(u,\,v\,{\text{and}}\,\,w\) :

Components of the velocity \(\left( {{\text{ms}}^{ - 1} } \right)\)

\(\wp_{0}\) :

Ambient porosity

\(m\) :

Particle diameter \(\left( {\text{m}} \right)\)

\(F^{\prime}\) :

Dimensionless radial velocity

\(\Omega\) :

Disk rotating rate

\(\Pr\) :

Prandtl number

\(T\) :

Free surface temperature \(\left( {\text{K}} \right)\)

\({\text{Da}}\) :

Permeability parameter

\(G\) :

Dimensionless azimuthal velocity

\(T_{0}\) :

Disk surface temperature \(\left( {\text{K}} \right)\)

\(r,\varphi ,z\) :

Cylindrical coordinates

\(k\) :

Thermal conductivity (\({\text{Wm}}^{ - 1} {\text{K}}^{ - 1}\))

\({\text{Re}}\) :

Reynolds number

\(S\) :

Unsteadiness parameter

\(C_{{\text{p}}}\) :

Heat capacitance \((\text{J}\, \text{kg}^{-1}\, \text{K}^{-1})\)

\({\text{Nu}}\) :

Nusselt number

\(C_{{\text{f}}}\) :

Skin friction

\(\rho\) :

Density\(\left( {\text{Kgm}}^{ - 3}\right)\)

\(\mu\) :

Dynamic viscosity \(\left( {{\text{mPa}}} \right)\)

\(\alpha\) :

Rotating parameter

\(\phi\) :

Nanoparticle volume fraction

\(\beta\) :

Dimensionless thin film thickness

\(\theta\) :

Dimensionless temperature

\(\eta\) :

Similarity variable

HNFs:

Hybrid nanofluids

ANN:

Artificial neural network

HAM:

Homotopy analysis method

NPs:

Nanoparticles

TC:

Thermal conductivity

HT:

Heat transfer

hnf:

Hybrid nanofluid

nf:

Nanofluid

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Acknowledgement

The author extends the appreciation to the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number (R-2024-1009).

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  1. Mathematics Department, College of Science Al-Zulfi, Majmaah University, 11952, Majmaah, Saudi Arabia

    Sayer Obaid Alharbi

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Alharbi, S.O. The artificial neural network analysis of the flow of thin film hybrid nanofluid with variable film thickness in a variable porous medium over a rotating disk. J Therm Anal Calorim (2024). https://doi.org/10.1007/s10973-024-13077-9

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