Computational and deep learning based optimization of CuO nanofluid thermal performance in heat exchangers

Efficient thermal management is essential in industrial heat-exchange systems including power plants, refrigeration units, chemical processing industries, and renewable energy applications. This study investigates the thermal performance enhancement of a shell-and-tube heat exchanger using CuO-water nanofluids under counterflow conditions. A computational framework based on ANSYS Fluent 19.2 is employed to analyze fluid-flow and heat transfer characteristics at different nanoparticle volume concentrations. An Artificial Neural Network (ANN) model is developed using CFD-generated datasets to predict and optimize thermal performance. The results indicate that CuO-water nanofluids significantly improve heat transfer characteristics compared with pure water, with the highest concentration producing the maximum LMTD of 12.88°C. The ANN model demonstrated excellent predictive accuracy with an overall R 2 value of approximately 0.996. The proposed CFD-ANN methodology provides an effective approach for designing energy-efficient heat exchangers and optimizing nanofluid-based thermal management systems.