Designing artificial neural network of nanoparticle diameter and solid–fluid interfacial layer on single‐walled carbon nanotubes/ethylene glycol nanofluid flow on thin slendering needles

Abstract

In this study, an artificial neural network (ANN) has been developed to predict the boundary layer flow of a single‐walled carbon nanotubes nanofluid toward three different nonlinear thin isothermal needles of paraboloid, cone, and cylinder shapes with convective boundary conditions. Different effects of particle diameter and solid–fluid interface coating have been taken into account in the thermal conductivity model of nanofluid in which ethylene glycol has been used as the base fluid. Single and dual phase approach is used to establish the management model under the phenomenon of zero heat and mass flux. A dataset has been developed for different scenarios of the fluid model by changing the relevant parameters with the Runge–Kutta based shooting technique. Two different ANN models have been developed to predict Nusselt number and skin friction coefficient (SFC) values. The values obtained from ANN models have been compared with the numerical data, which are the target values. In addition, mean square error and R values have also been examined in order to analyze the prediction performance of ANN models more comprehensively. The calculated R values for Nusselt number and SFC were obtained as 0.9999. The results obtained showed that ANN can predict Nusselt number and SFC values with high accuracy.

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

“…A feedforward MLPNN has three layers of input, interiors, and output 44 – 46 . MLPNN benefits from a unique training approach known as the backpropagation, and the utilized activation functions in this method are non-linear 47 .…”

Electromagnetohydrodynamic boundary layer flow in hybrid nanofluid with thermal radiation effect: Numerical simulation

“…Mixed convective hybrid nanofluid flow over a three-dimensional porous stretching surface is discussed by Joshi et al 36 Impact of thermal radiation in copper-alumina hybrid nanofluid past a pervious elongating/shrinking surface is presented by Yashkun et al 37 This study provides detailed information about the stability of dual solutions for suction parameter and also proves that suction parameter increases Nusselt number for both nanofluid models. The artificial neural network model on carbon nanotubes-based nanofluid flow over the thin needle with ethylene glycol as base fluid is developed by Shafiq et al 38 The similar neural network model on stretching sheet using Williamson nanofluid flow is reported by Shafiq and Sindhu. 39 Recently Gholinia et al 40 discussed nanoparticle shape on hybrid base fluid and proved that blade-shaped nanoparticles improve Nusselt number.…”

Quadratic mixed convective nanofluid flow past a moving yawed cylinder in the presence of thermal radiation and diffusive liquids

“…8,9 Recently, Shafiq et al 10 have worked on bioconvective tangent hyperbolic nanofluid over a stretching surface, and their results reveal that the mass transfer rate is increased by increasing Lewis number. Shafiq et al 11 have worked on nanofluid flow over thin slandering needles, and their results reveal that the existence of SWCNT also facilitates the observable characteristics of the flow. Furthermore, Shafiq and colleagues [12][13][14] have worked on nanofluid by considering different geometries.…”