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The analysis in this study focuses on the transfer of heat within a wavy cavity in the presence of nonlinear mixed convection and MHD. We conduct the investigation under conditions where the upper wall of the cavity remains cold, the lower wall receives heat, and the left and right wavy walls remain adiabatic. Using the PDE solver in COMSOL, the non-dimensional system is simplified, employing the finite element method. The study aims to explore the impact of parameters such as nanoparticle concentration, Hartmann number, Reynolds number, Richardson number, and nonlinear mixed convection parameters on fluid flow and heat transfer. An artificial neural network has been employed to examine the behaviour of the Nusselt number within a cavity. The isotherms reveal that the temperature is higher near the lower wall for all parameters due to the heated source. Analysis of the streamline plots indicates that heat increases as it moves towards the upper wall, influenced by the Hartman number and volume fraction parameter. The use of ANN in the study forecasts the accuracy, smoothness, and convergence of the data through plots of mean squared error (MSE), error histogram, regression analysis, and transition state plot. The authors believe that using artificial neural networks to explore the heat transfer rate under the assumptions of MHD and nonlinear mixed convection is a novel approach that has not been previously investigated. Additionally, researchers can utilise ANN concepts to predict intricate cavity formations through multiphysics. This approach will require less human effort and computing time to analyse the heat response of any dataset. |
