A Dual-Head ANN Architecture for Multi-objective Thermal Optimization of MHD Nanofluid Flow within a Wavy Enclosure

This study examines magnetohydrodynamic natural convection of a Cu–H₂O nanofluid inside a wavy undulating cavity and develops an artificial neural network-based dual-task learning framework for predictive heat-transfer modeling and regime classification. To analyze the coupled impacts of Rayleigh number, nanoparticle volume concentration, and wavy shape of cavity on flow motion and convection heat in the present work, a numerical simulation model is used. An ANN is trained to predict the average Nusselt number along the flat plate from the simulation dataset, and then classification of thermal regimes is performed into Low, Medium and High types using a quantile-based discretization method. The regression ANN has a high predictive accuracy, showing good correlation between the values of the simulated mean Nusselt number and R² is 0.9878. The residuals of the model are well-distributed, and its generalization to test samples is stable. Moreover, the classification ANN also achieves
good regime identification with accuracy 0.97, supported by consistent 5-fold cross-validation results and clear inter-class separation in latent-space feature projections. Overall, the integration of CFD-based simulation with ANN-driven regression and classification provides a computationally efficient surrogate modeling framework for rapid heat-transfer estimation and
intelligent thermal regime mapping in complex enclosure geometries.