Thermal performance and entropy assessment of a nanofluid-filled wavy cavity utilizing finite-element modeling approach and artificial intelligence

The effective thermal control of sophisticated energy and cooling systems in sophisticated enclosures depends on the efficient thermal regime of complex fluids. Literature rarely attempts to extend the study of the joint mechanisms of nanoparticle shape, entropy generation, and artificial intelligence (AI)-based prediction in these geometrically complex domains. To fill these gaps, this study investigates the determinants of flow stability, heat transfer, and energy dissipation by the nanoparticle volume fraction (0 ≤ φ ≤ 0.08), Reynolds (50 ≤ Re ≤ 400), Richardson (0.01 ≤ Ri ≤ 1), and Hartmann numbers (0 ≤ Ha ≤ 80) and the concentration and morphology of nanoparticles. The purpose of this study is to examine the combined effects of Re, Ri, Ha, φ, and particle shape on the behavior of the flow, heat transfer, and entropy generation in a T-shaped wavy cavity through a finite element framework with the assistance of artificial intelligence models. This study also concentrates on the unexplored relationships between inertia, buoyancy, magnetic damping, and nanoparticle attributes in a T-shaped wavy cavity with an obstacle comprising a heated circular region. It builds a steady, incompressible Al₂O₃-water nanofluid flow model based on a finite element framework and heat line visualization with stacked ensemble learning models to assess predictive performance. Key findings mention that the heat transfer is greatly increased (up to ∼35–40%) with increased Reynolds numbers (Re ≈ 400), and medium-sized magnetic fields (Ha ≈ 40) enhance the stability of the flow and suppress entropy generation. Optimal thermal performance occurs at φ ≈ 0.04 and Ri ≈ 0.1, and lamina-shaped nanoparticles offer the best heat transfer enhancement as compared to other shapes. The stacked ensemble learning-based predictive model achieves high accuracy (R² ≈ 0.9937), indicating that it can serve as an alternative to full numerical simulation, as it is fast and reliable. These results can be valuable for the construction and optimization of more sophisticated thermal systems and underscore the usefulness of AI-assisted predictive modeling in complex thermo-fluids.