A novel design of inverse multiquadric radial basis neural networks to analyze MHD nanofluid boundary layer flow past a wedge embedded in a porous medium under the influence of radiation and viscous effects

Butt, Zeeshan Ikram; Ahmad, Iftikhar; Shoaib, Muhammad; Ilyas, Hira; Raja, Muhammad Asif Zahoor

doi:10.1016/j.icheatmasstransfer.2022.106516

Cited by 33 publications

(3 citation statements)

References 52 publications

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“…The key features of GAs functioning depend on quite reliable values of elitism, mutation, and crossover with selection. The most recent research in which GAs performed a key role is given in [52–56].…”

Section: Problem Methodologymentioning

confidence: 99%

Inverse multiquadric kernel‐based neuro heuristic approach to analyze the unsteady MHD nanofluid flow via permeable elongating surface

Butt,

Ahmad,

Hussain

et al. 2023

Z Angew Math Mech

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In this study, a novel neuro heuristic approach is designed to investigate the flow properties of magnetohydrodynamic (MHD) nanofluid along an exponentially extending sheet with a permeable medium with the impact of radiation as well as fluctuating heat source/sink. The designed scheme to handle the suggested problem is established through the well‐known biologically inspired neural networks (BINNs) by exploiting the inverse multiquadric kernel (IMQK), that is, BINNs‐IMQK which is quite a new approach. The partial differential equations (PDEs) which govern the fluidic flow are reformed into a nonlinear system of ordinary differential equations (ODEs) using the most fitted similarity transformations rules and numerically solved by varying the parametric values including unsteady parameter, Brownian motion parameter, suction/injection parameter, radiation parameter, Schmidt number together with Prandtl number to visualize the velocity, thermal gradient, and mass transfer in the suggested fluid problem. It is noticed that nanofluid temperature hikes by uplifting the value of the Brownian motion parameter but this effect is reversed in case of unsteady parameter. The obtained numerical results are verified through reference solution using the well‐known Adams method and the efficacy of the suggested solver is endorsed using a variety of statistical operators.

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Section: Problem Methodologymentioning

confidence: 99%

Inverse multiquadric kernel‐based neuro heuristic approach to analyze the unsteady MHD nanofluid flow via permeable elongating surface

Butt,

Ahmad,

Hussain

et al. 2023

Z Angew Math Mech

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“…Te viscous dissipation efects on the MHD boundary layer stream of nanofuid across a wedge embedded in porous medium were examined numerically via the spectral quasilinearization method (SQLM) by Ibrahim and Tulu [3]. A few inquiries involving the Falkner-Skan fow with various types of physical characteristics past a wedge can be found in the studies of Kudenatti and Amrutha [4], Haq et al [5], and Butt et al [6].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Magnetized Casson Nanofluid Flow along Wedge: Gaussian Process Regression

Shanmugapriya,

Sundareswaran,

Gopi Krishna

et al. 2024

International Journal of Mathematics and Mathematical Sciences

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An unsteady two-dimensional magnetized Casson nanofluid flow model is constructed over a wedge under the effect of thermal radiation and chemical reaction. The multiple slip effects are also assumed near the surface of the wedge along with the convective boundary restrictions. This study investigates the application of soft computing techniques to address the challenges posed by the complexity of problem modeling and numerical methods. Traditional approaches incorporating various model factors may struggle to provide accurate solutions. To resolve this issue, Gaussian process regression (GPR) is employed to predict the solution of the proposed flow model. With the help of the numerical shooting method together with Runge–Kutta–Fehlberg fourth-fifth-order (RKF-45) reference data, the GPR model is trained. The numerical simulation illustrated that the Casson fluid parameter β and the unsteadiness parameter S strengthen the friction factor, and the heat transfer rate is enhanced as the radiation parameter Rd becomes larger. In addition, the Biot numbers Bi1 & Bi2 lead to strengthen nanoparticle temperature; an opposite behavior is noticed with the skin friction coefficient S˜fxRex0.5, heat transfer rate H˜tx Rex0.5, and nanoparticle transfer rate C˜txRex0.5. The GPR model with the exponential Kernel function provided better performance than other functions on both training and checking datasets to predict S˜fxRex0.5,H˜tx Rex0.5, and C˜txRex0.5. Statistical metrics including RMSE, MAE, MAPE, MSE, R2, and R are employed to check the accuracy and convergences of the predicted and numerical solutions obtained through GPR and RKF-45. It is observed that all three GPR models had an R2 value of higher than 0.9. The proposed study demonstrates the advantages of employing soft computing methods (GPR) to effectively analyse the behavior of complex flow models.

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“…[6] studied the role of CNTs on an MHD Casson Marangoni boundary layer flow past a porous medium with combined effects of thermal radiation and suction/injection. [7] reported a unique design of inverse multiquadric radial basis neural network to study MHD nanofluid boundary layer flow over a wedge submerged in a porous medium with viscous and radiation effects. Other relevant literature can be seen in [8]- [11].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Approximation for Natural Convection Flow Past A Vertical Moving Plate With Nonlinear Thermal Radiation Effect

Jha,

Samaila

2023

J. Mod. Sim. Mater.

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This piece of work contains significant insight associated with the analysis of fluid transport in the vicinity of a constantly moving vertical plate with nonlinear thermal radiation. The heat transport at the wall surface is assumed to be influenced by convective boundary conditions. Furthermore, thermal transport is considered to be enhanced by nonlinear temperature variation with temperature (NDT). The boundary layer approximation equations are simplified through suitable alteration known as the similarity transformation. The resulting ODEs are translated into the IVP via shooting techniques and then integrated using the RKF45 algorithm in Maple. The impact of the dimensionless parameters dictating the fluid behaviour is demonstrated via graphs and tables. In the cause of the analysis, it is observed that the heat transfer enhances when the fluid flow in the direction +ve x-axis whereas the plate moves in the direction of the -ve x-axis but decreases when the plate and fluid move in the same direction. The skin friction coefficient decreases when the fluid flow is directed toward the +ve x-axis whereas the plate moves toward the -ve x-axis but is enhanced when the plate and the fluid move in the same orientation. The temperature and velocity profiles appreciate with the nonlinear thermal radiation when the motion of the plate and the fluid are on the same axis. The temperature gradient near the wall depreciated gradually due to nonlinear thermal radiation growth but appreciate in the free stream.

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A novel design of inverse multiquadric radial basis neural networks to analyze MHD nanofluid boundary layer flow past a wedge embedded in a porous medium under the influence of radiation and viscous effects

Cited by 33 publications

References 52 publications

Inverse multiquadric kernel‐based neuro heuristic approach to analyze the unsteady MHD nanofluid flow via permeable elongating surface

Inverse multiquadric kernel‐based neuro heuristic approach to analyze the unsteady MHD nanofluid flow via permeable elongating surface

Investigation of Magnetized Casson Nanofluid Flow along Wedge: Gaussian Process Regression

Nonlinear Approximation for Natural Convection Flow Past A Vertical Moving Plate With Nonlinear Thermal Radiation Effect

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