Prediction of thermal and energy transport of MHD Sutterby hybrid nanofluid flow with activation energy using Group Method of Data Handling (GMDH)

Krishna, S. Gopi; Shanmugapriya, M.; Alsinai, Ammar; Alameri, Abdu

doi:10.1007/s40314-022-01995-z

Cited by 3 publications

(1 citation statement)

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“…Raja et al [28] investigated the 3D hybrid nanofuid fow over biaxial porous stretching/ shrinking sheet with heat transfer, radiative heat, and mass fux solved through Bayesian regularization technique based on backpropagation neural networks. Computational fuid dynamic (CFD) AI techniques were employed for Casson nanofuid [29], MHD Carreau nanofuid fow containing gyrotactic microorganisms [30], biomagnetic ternary hybrid nanofuid [31], MHD Sutterby hybrid nanofuid fow with activation energy [32], and nonlinear radiative magnetized Carreau nanofuid [33].…”

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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