Deep Learning Analysis and Numerical Simulation of Exergy and Nanofluid Heat Transfer Efficiency in a Two-Compartment Heat Exchanger with Internal Heat Generation and Baffles

This paper investigates the heat transfer enhancement in a two-compartment heat exchanger using nanofluids, employing numerical simulations and deep learning. The study systematically examines the influence of key parameters: Rayleigh number $(Ra=10^{6}-10^{9})$, conductivity ratio $(kr=1-15)$, nanoparticle volume fraction $(\varphi=0-3\%)$, nanofluid temperature (Temp=293-323K), and scaled heat exchanger wall thickness (0.02-0.05). The first compartment features internal heat generation, while the second incorporates baffles and nanofluids to optimize mixing and heat transfer. Computational Fluid Dynamics (CFD) is used to analyze Nusselt number, isotherms, streamlines, velocity vector magnitude, exergy loss, entropy generation, and the Bejan number. Deep learning models are developed to predict and optimize heat transfer performance based on these five input parameters. Results demonstrate that increasing the Rayleigh number and conductivity ratio significantly enhances heat transfer, while nanoparticles higher volume fractions improve performance, albeit with potential viscosity increases. Exergy analysis reveals opportunities for design optimization to minimize entropy generation. The integrated approach of CFD and deep learning provides a powerful tool for optimizing the design and operation of nanofluid-based heat exchangers for improved thermal management in various applications.