RT Journal Article
SR Electronic
A1 Guendouz, Bouhelal
A1 Benzenine, Hamidou
A1 Beldjilali, Mohammed
A1 Bahram, Kaddour
A1 Saim, Rachid
T1 Coupling DRNN with Numerical Simulations for the Thermal Performance Analysis of a Shell-and-Tube Heat Exchanger Using Cu/Water Nanofluid
JF Acta Mechanica Slovaca
YR 2025
VO 29
IS 1
SP 28
OP 35
DO 10.21496/ams.2025.009
UL https://www.actamechanica.sk/artkey/ams-202501-0004.php
AB 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 (R<sup>2</sup> = 0.997, 0.998, and 0.984) with low RMSE, MAE, and MSE values.