Numerical and AI-Based Analysis of Natural Convection in a Square Cavity for Advanced Engineering Applications

Natural convection in square cavities is an important heat transfer phenomenon widely encountered in many engineering and industrial systems, where efficient thermal management and reduced computational cost are essential for optimal performance. In the present investigation, a comparative numerical and artificial intelligence-based analysis has been conducted to analyze the heat transfer and fluid flow characteristics inside a square cavity filled with nanofluid. The prominent features of this model is that the left vertical wall of the cavity is maintained at a constant hot temperature (Th), while the right boundary is kept at a constant cold temperature (Tc), whereas the horizontal walls are considered thermally insulated. The cavity is entirely filled with nanofluid to enhance thermal conductivity and convective heat transfer performance. The conservative partial differential equations are transformed into dimensionless form and then solved numerically by the finite difference method together with iterative pressure–velocity coupling techniques. The simulated thermal and flow fields are examined through temperature contours, streamline patterns, velocity profiles, and average Nusselt number for varying Rayleigh numbers (103 ≤ Ra ≤ 106) and nanoparticle volume fractions (0 ≤ φ ≤ 0.04). In addition, an artificial neural network model is developed using numerical simulation data to predict temperature and velocity distributions under different operating conditions. The novelty of this investigation lies in combining classical numerical simulation with AI-based predictive modeling for natural convection in square cavity systems, providing both physical insight and rapid engineering prediction capability.

Artificial Intelligence (AI), finite difference method, nanofluid, natural convection, square cabinet.

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