Artificial neural network modeling of MHD stagnation point flow and heat transfer towards a porous stretching sheet

Elayarani, M.; Shanmugapriya, M.

doi:10.1063/1.5127634

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…Sabir et al 35 investigated the solution of singular three‐point second‐order boundary value problems by utilizing novel meta‐heuristic computing solver. The numerical or experimental results with ANN modeling in nanofluids has been examined by the researchers in recent articles 36–40 …”

Section: Introductionmentioning

confidence: 99%

“…The numerical or experimental results with ANN modeling in nanofluids has been examined by the researchers in recent articles. [36][37][38][39][40] In the current analysis, the subsequent dimensionless nanofluid model is constructed using Lie symmetry technique and solved computationally with shooting technique for predicting multiple solutions, followed by temporal stability analysis. The impact of Maxwell nanofluids transport phenomena induced by stretching/shrinking surface in presence of chemical reaction and heat source/sink under various administering conditions has been investigated.…”

Section: Introductionmentioning

confidence: 99%

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Critical points and stability analysis in MHD radiative non‐Newtonian nanoliquid transport phenomena with artificial neural network prediction

Rana

Bhardwaj

Makkar

et al. 2022

Math Methods in App Sciences

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In the present framework, flow and thermal transport behavior of non-Newtonian viscoelastic fluid induced by stretching/shrinking of the horizontal sheet under the influence of Lorentz force, volumetric heat source/sink, and radiation (assuming optically thick medium) has been investigated. Multiple solutions (Branches) have been predicted numerically using Lie symmetry transformation and Runge Kutta Dormand Prince (RKDP) algorithm-based Shooting method for the different controlling parameters, stretching/shrinking (𝛾 c < −1 < 𝛾 < ∞), and suction (𝛼 c < 𝛼 < ∞) with substantial impact with UCM parameter, 𝛽 > 0. Some of the results have also been compared with MATLAB built-in solvers to validate our in-house code. The deviations in critical (turning) points (𝛽 c , 𝛾 c ) have been noticed for different values of M and 𝛼. The temporal stability analysis is performed for these parameters (𝛽, 𝛾), and the upper (lower) branch is only physically feasible (infeasible). The combined effect on Nusselt number and skin-friction coefficient is also depicted in the form of contour plots. The outcomes from artificial neural network (ANN) using Levenberg-Marquardt algorithm give a preferred estimate for current numerical simulation. In ANN prediction, the optimal number of neurons in the hidden layer is used, having minimum value of RMSE and RMRE with maximum R 2 for different cases of non Newtonian stretching/shrinking sheet. Finally, the results conclude that the applied ANN model can accurately predict the output results.

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Section: Introductionmentioning

confidence: 99%