Coupling DRNN with Numerical Simulations for the Thermal Performance Analysis of a Shell-and-Tube Heat Exchanger Using Cu/Water Nanofluid

This study investigates the thermal performance of a shell-and-tube heat exchanger, enhanced by using a Cu-water nanofluid on the shell side. A combined experimental and numerical approach was used to assess the effects of varying nanoparticle concentrations (2% to 6%) and cold-side flow rates (0.047 to 4 l/min). At 6% concentration, results showed a temperature increase of up to 7 C on the shell side, a 12% improvement in thermal efficiency, and a global heat transfer coefficient reaching 8516.87 W, compared to pure water. Additionally, a deep recurrent neural network (DRNN) was implemented to predict key performance indicators: thermal efficiency, heat transfer coefficient, and outlet temperature on the shell side. The model, trained on various thermal parameters, demonstrated excellent predictive accuracy (R2 = 0.997, 0.998, and 0.984) with low RMSE, MAE, and MSE values.