Optimised heat exchange in a magnetised nanofluid-filled cavity using hybrid deep neural network and metaheuristic algorithms

Abstract

This study presents a comprehensive numerical investigation into steady-state mixed convection heat transfer within a square ventilated cavity containing a centrally positioned isothermal cold cylinder. The objective is to assess the combined effects of nanofluids and magnetic fields on thermal performance. The working fluids considered include pure water and water-based nanofluids enhanced with copper (Cu) and aluminium oxide (Al2O3) nanoparticles. Simulations were conducted across a range of Richardson numbers (0.1 < Ri < 100), Hartmann numbers (0 < Ha < 100), and nanoparticle volume fractions (0% < φ < 8%), using the finite volume method and the SIMPLER algorithm. Distinct from prior studies, this work bridges two gaps: (i) quantifying how high magnetic fields (Ha > 50) diminish nanoparticle-enhanced heat transfer and (ii) integrating artificial intelligence not only for prediction but also optimisation. Specifically, three machine learning models Decision Tree (DT), K-Nearest Neighbors (KNN), and a Deep Neural Network optimised via Genetic Algorithm (DNN-GA) were trained on 160 high-fidelity simulation datasets to estimate the average Nusselt number. Results demonstrated the DNN-GA’s superior accuracy (R² = 0.999, RMSE = 0.021) over DT (R² = 0.978) and KNN (R² = 0.921). Furthermore, five metaheuristic algorithms Queuing Search Algorithm (QSA), Barnacles Mating Optimiser (BMO), Search and Rescue (SAR), Gradient-Based Optimiser (GBO), and Manta Ray Foraging Optimisation (MRFO) were applied to maximise heat transfer. Optimisation identified Cu nanoparticles at Ri = 109.7, Ha = 9.0, and φ = 6% as optimal (Nu = 34.95), validated experimentally with 0.89% error. The findings confirm that increasing Ri and Ha enhances heat transfer efficiency (by 12–18%), while nanoparticle contribution declines (to 3–5%) at higher Ha. This work offers a dual contribution: advancing understanding of MHD nanofluid interactions in ventilated cavities and demonstrating a robust AI-driven framework for thermal system design. Highlights: Analysis of mixed convection in a ventilated cavity using Cu-water and Al2O3-water nanofluids under varying Richardson and Hartmann numbers. Examination of magnetic field impacts on heat transfer and nanofluid flow. Comparative study of Al2O3 and Cu nanoparticles on heat transfer enhancement. Provides valuable insights into the combined effects of nanoparticles, magnetic fields, and convection parameters. Machine learning models are very useful for predicting the Nusselt number. Metaheuristics algorithms are highly effective in optimising heat transfer processes