Steady nanofluid flow with variable fluid possessions over a linearly extending surface: A Lie group exploration

Das, Kalidas; Sk, Tausif; Kundu, Prabir Kumar

doi:10.1016/j.aej.2016.11.015

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…These low values reached by the MSE values obtained for each data are considered as another proof that MLP models have been developed to have low error values. Figure 18 shows the error rates calculated using Equation (5). The error values calculated for each data point show the deviation between the prediction data obtained from ANN models and the target values.…”

Section: Discussionmentioning

confidence: 99%

“…"Nanofluid idea" was a bit ahead toward this excursion. Although since, several researchers [1][2][3][4][5][6] have made attempts to examine thermal flow in various circumstances of nanofluid flow in order to choose the most effective one. They also attempted to obtain some associated model of numerous thermophysical features such as thermal conductivity, viscosity, and so forth of nanofluid.…”

Section: Introductionmentioning

confidence: 99%

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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

Shafiq

Çolak

Sindhu

2021

Numerical Methods in Fluids

108

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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.

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Section: Discussionmentioning

confidence: 99%