Impact of thermal energy on MHD Casson fluid through a Forchheimer porous medium with inclined non-linear surface: A soft computing approach

Shoaib, Mohammed; Kausar, Mamoona; Nisar, Kottakkaran Sooppy; Raja, Muhammad Asif Zahoor; Morsy, Ahmed

doi:10.1016/j.aej.2022.06.014

Cited by 29 publications

(15 citation statements)

References 43 publications

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“…The data set for convergence analysis in terms of the viscous, incompressible and electrically conducting fluid flow system’s validation, testing and training is presented in Table 7 for the output response (Nusselt number), with good quality performance obtained at 100, 70, 54 and 38 with time contribution 3, 2, 2 and 2 s. Mu shows the step size, and gradients represent the process of finding vectors during the training of data. The numerical values given in Table 7 are computed using Table 2 of the experimental design as input and output response values (scenario) (Shoaib, 2022a, 2020b).…”

Section: Resultsmentioning

confidence: 99%

“…ANNs can also be used to control fluid flow systems, such as in active flow control or flow rate regulation. Shoaib et al (2022a) studied the ramifications of thermal energy on MHD processes in Casson fluid flow with heat and chemical reaction effects over a spreading surface. They used the Levenberg–Marquardt algorithm based on trained neural networks (LMA-TNN) methodology, which is a trained neural network.…”

Section: Introductionmentioning

confidence: 99%

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Artificial neural network simulation and sensitivity analysis for optimal thermal transport of magnetic viscous fluid over shrinking wedge via RSM

Zeeshan

Khan

Ellahi

et al. 2023

HFF

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Purpose This study aims to model the important flow response quantities over a shrinking wedge with the help of response surface methodology (RSM) and an artificial neural network (ANN). An ANN simulation for optimal thermal transport of incompressible viscous fluid under the impact of the magnetic effect (MHD) over a shrinking wedge with sensitivity analysis and optimization with RSM has yet not been investigated. This effort is devoted to filling the gap in existing literature. Design/methodology/approach A statistical experimental design is a setup with RSM using a central composite design (CCD). This setup involves the combination of values of input parameters such as porosity, shrinking and magnetic effect. The responses of skin friction coefficient and Nusselt number are required against each parameter combination of the experimental design, which is computed by solving the simplified form of the governing equations using bvp4c (a built-in technique in MATLAB). An empirical model for Cfx and Nux using RSM and ANN adopting the Levenberg–Marquardt algorithm based on trained neural networks (LMA-TNN) is attained. The empirical model for skin friction coefficient and Nusselt number using RSM has 99.96% and 99.99% coefficients of determination, respectively. Findings The values of these matrices show the goodness of fit for these quantities. The authors compared the results obtained from bvp4c, RSM and ANN and found them all to be in good agreement. A sensitivity analysis is performed, which shows that Cfx as well as Nux are most affected by porosity. However, they are least affected by magnetic parameters. Originality/value This study aims to simulate ANN and sensitivity analysis for optimal thermal transport of magnetic viscous fluid over shrinking wedge.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial neural network simulation and sensitivity analysis for optimal thermal transport of magnetic viscous fluid over shrinking wedge via RSM

Zeeshan

Khan

Ellahi

et al. 2023

HFF

View full text Add to dashboard Cite

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“…On the other hand, several studies have been conducted to analyze the Magnetohydrodynamic Casson fluid, such as Casson nanofluid flow over a nonlinear slanted extending/shrinking surface, oscillating disk in Darcy–Forchheimer medium under the effect of heat and mass transfer, thermal energy in terms of heat source/sink, thermal radiation and chemical reaction, while a numerical analysis has been conducted for the three-dimensional flow of a hybrid nanofluid under/over a stretching surface using supervised Neural Networks 21 – 24 . More recently, the injection of water-based nanoparticle (NP) suspensions has received attention as a recovery enhancement technique.…”

Section: Introductionmentioning

confidence: 99%

Analytical investigation of an incompressible viscous laminar Casson fluid flow past a stretching/shrinking sheet

Mahabaleshwar

Maranna

Sofos

2022

Sci Rep

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This paper presents an analytical approach on capturing the effect of incompressible, non-Newtonian, viscous, Casson nanofluid flow past a stretching/shrinking surface, under the influence of heat radiation and mass transfer parameter. The governing nonlinear partial differential equations are first transformed into a series of associated nonlinear ordinary differential equations with aid of predictable transformation, while numerical computations follow. The implied nanofluid here is aluminum oxide ($$Al_{2} O_{3}$$ A l 2 O 3 ). The analytical solution is exploited to reveal the accompanying non-dimensional boundary value problem and output results are employed to verify the method's reliability, where it is shown that they agree with current findings in the field. An incomplete gamma function is used to solve temperature equation analytically. We present various instances of the solution, depicting effects of the essential flow factor, the stretching/shrinking parameter, the mass transfer parameter, radiation parameter, and Prandtl number.

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“…However, the stochastic solvers have never been applied to solve the SSP-BVP by using the artificial neural networks (ANNs) along with global search particle swarm optimization (PSO) and local search interior-point algorithm (IPA), i.e., ANNs-PSO-IPA. The artificial neural networks have been implemented to solve a variety of different applications; some recent applications of the stochastic solvers are circuit theory, higher order singular model, fuel ignition model, induction of the motor models, Thomas-Fermi model, doubly singular nonlinear systems, nanotechnology, nanofluidics, chaos control of Bonhoeffer-van der Pol, nonlinear equations, Troesch's problem, controls, communication systems, particle physics, linear and nonlinear fractional order model, physical models signified nonlinear system of equations, financial mathematics, multiple singularities models based on Painleve equations etc., see [9][10][11][12][13] and references cited therein. Keeping view of these facts, authors are inspired to propose new computing criteria through the ANNs modelling.…”

Section: Introductionmentioning

confidence: 99%

A stochastic numerical approach for a class of singular singularly perturbed system

et al. 2022

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In the present study, a neuro-evolutionary scheme is presented for solving a class of singular singularly perturbed boundary value problems (SSP-BVPs) by manipulating the strength of feed-forward artificial neural networks (ANNs), global search particle swarm optimization (PSO) and local search interior-point algorithm (IPA), i.e., ANNs-PSO-IPA. An error-based fitness function is designed using the differential form of the SSP-BVPs and its boundary conditions. The optimization of this fitness function is performed by using the computing capabilities of ANNs-PSO-IPA. Four cases of two SSP systems are tested to confirm the performance of the suggested ANNs-PSO-IPA. The correctness of the scheme is observed by using the comparison of the proposed and the exact solutions. The performance indices through different statistical operators are also provided to solve the SSP-BVPs using the proposed ANNs-PSO-IPA. Moreover, the reliability of the scheme is observed by taking hundred independent executions and different statistical performances have been provided for solving the SSP-BVPs to check the convergence, robustness and accuracy.

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Impact of thermal energy on MHD Casson fluid through a Forchheimer porous medium with inclined non-linear surface: A soft computing approach

Cited by 29 publications

References 43 publications

Artificial neural network simulation and sensitivity analysis for optimal thermal transport of magnetic viscous fluid over shrinking wedge via RSM

Artificial neural network simulation and sensitivity analysis for optimal thermal transport of magnetic viscous fluid over shrinking wedge via RSM

Analytical investigation of an incompressible viscous laminar Casson fluid flow past a stretching/shrinking sheet

A stochastic numerical approach for a class of singular singularly perturbed system

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