Enhancement of Heat Transfer Using Water/Graphene Nanofluid and the Impact of Passive Techniques—Experimental, Numerical, and ML Approaches

This study examines heat transfer characteristics by employing a combined augmentation technique that utilises nozzle-type inserts to induce swirling in water/graphene nanofluids at different concentrations. The assessment evaluates its influence on heat transfer, Nusselt number, and thermal performance factor, emphasising its applicability in industrial contexts. This research aims to create a numerical model designed to improve the performance of heat exchangers by employing passive techniques, particularly through the implementation of a convergent–divergent nozzle insert, without the need for experimental validation. The accuracy of the model is confirmed through experimental data, and it is subsequently employed to simulate various Reynolds numbers, generating datasets for training and testing machine learning models. This study also highlights the potential aggregation and flow resistance limitations when combining nanoparticles with passive inserts. The experimental outcomes for the convergent nozzle insert are employed to validate the supervised machine learning model. Subsequently, a numerical analysis of the convergent–divergent nozzle insert is conducted using approximately 220 samples for training and testing purposes. The convergent–divergent nozzle insert improves heat transfer efficiency in heat exchangers by generating high-velocity flow and enhancing temperature gradients. Optimising nozzle geometry through numerical simulations can determine the ideal dimensions for better heat transfer rates. Nanofluids show a thermal performance factor increase of up to 13.2% at higher inlet temperatures than water. The thermal performance factor for nanofluid at inlet higher temperatures is 8.5%, 9.3%, 11.6%, 12.8%, and 13.2% compared to water.