Acta Mechanica Slovaca 2025, 29(1):28-35 | DOI: 10.21496/ams.2025.009
Coupling DRNN with Numerical Simulations for the Thermal Performance Analysis of a Shell-and-Tube Heat Exchanger Using Cu/Water Nanofluid
- 1 Laboratory of Energetic and Applied Thermal, ETAP, Department of Mechanical Engineering, Faculty of Technology,B.P 230, University of Tlemcen, 13000, Algeria
- 2 University of Ain Témouchent, Belhadj Bouchaib, Department of Mechanical Engineering, PO Box 284,46000, Algeria
- 3 University of Ain Témouchent, Belhadj Bouchaib, Department of Material Sciences, PO Box 284,46000, Algeria
- 4 Laboratory of Applied Chemistry (LAC), University of Ain Temouchent, Algeria
- 5 Laboratory of Materials and Reactive Systems LMSR, Department of Mechanical Engineering, University Djilali Liabes, BP 89, Cité Ben M'hidi, Sidi bel Abbes, 22000, Algeria
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.
Keywords: Nanofluids, STHE (Shell-and-Tube Heat Exchanger), CFD Numerical Simulation, Global Heat Transfer Coefficient, Copper.
Received: January 18, 2025; Revised: March 3, 2025; Accepted: March 4, 2025; Published: March 30, 2025 Show citation
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References
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