Journal of Computational Methods in Engineering

Journal of Computational Methods in Engineering

Investigation and Analysis of Turbulence Models in Turbulent Flow in Tubular Thermal Economizers

Document Type : Original Article

Authors
AhmadReza Rahmati
Mohammad Karimi
Department of Mechanical Engineering, University of Kashan, Kashan, Iran
10.47176/jcme.45.1.1021
Abstract
Turbulence models serve as essential tools for analyzing and simulating the behavior of turbulent flows in tubular thermal economizers. This study presents a comprehensive and in‑depth assessment of several turbulence models, each characterized by its specific capabilities and applications. These models facilitate an improved understanding of heat transfer mechanisms and flow behavior under varying operating conditions. For the economizer of the Isfahan thermal power plant with a capacity of 120 MW, the evaluated results indicate that the actual heat transfer coefficient of the outlet water is 98.68 W/m²·K, while that of the outlet flue gas is 78.16 W/m²·K. The simulations and analyses were conducted using ANSYS Fluent. According to the obtained results, the LES model was identified as the most accurate approach, achieving a turbulence prediction accuracy of 99.39%. Furthermore, the LES model estimated the outlet water heat transfer coefficient as 80.68 W/m²·K and the outlet gas heat transfer coefficient as 67.16 W/m²·K, values that closely match the actual measurements from the Isfahan power plant. These findings not only demonstrate the superior performance of the LES model but also highlight its potential for application in the design optimization and performance enhancement of thermal economizers.
Keywords
Tubular Economizer
Turbulence models
Turbulent flow
LES
Subjects
Liu, W., Fang, J., Rolfo, S., Moulinec, C. Emerson, D. An iterative machine-learning framework for RANS turbulence modeling. International Journal of Heat and Fluid Flow. 2021; 90:108822.
     https://doi.org/10.1016/j.ijheatfluidflow.2021.108822
2.  Marty J, Uribe C. Impact of Underlying RANS Turbulence Models in Zonal Detached Eddy Simulation: Application to a Compressor Rotor. International Journal of Turbomachinery, Propulsion and Power. 2020; 5(3):22.
       https://doi.org/10.3390/ijtpp5030022
3.  O’Connor, J., Whalley, R. D., Wynn, A., Laizet, S. A dataset of high-resolution snapshots of the viscous sublayer from direct numerical simulation of a turbulent boundary layer up to Reθ=2400. Data in Brief. 2026; 66: 112767.
       https://doi.org/10.1016/j.dib.2026.112767
4.  Wang, N., Shi, Y., Cui, F., Wen, J., and Jia, J. Improving the Heat Transfer Efficiency of Economizers: A Comprehensive Strategy Based on Machine Learning and Quantile Ideas. Energies. 2025; 18(16): 4227.
      https://doi.org/10.3390/en18164227
5.   Feng, Z., Xin, C., Zhou, T., Zhang, J., Fu, T. Airside thermal-hydraulic and fouling performances of economizers with integrally-molded spiral finned tubes for residual heat recovery. Applied Thermal Engineering. 2022; 211: 118365. https://doi.org/10.1016/j.applthermaleng.2022.118365
6.  Yang, X. I. A., Zhang, W., Abkar, M., and Anderson, W. Computational Fluid Dynamics: its Carbon Footprint and Role in Carbon Emission Reduction. J. Renewable Sustainable Energy. 2024; 16(5): 055906.
      https://doi.org/10.1063/5.0217320
7.  Aljuhaishi, S., Timimi, Y. K. Al., and Wahab, B. I. Comparing turbulence models for CFD simulation of UAV flight in wind tunnel experiments. Periodica Polytechnica Transportation Engineering. 2024; 52(3): 301–309. https://doi.org/10.3311/PPtr.24004.
 
 
 
 
 
 
 
 
8.  Dharmasena, P., and Nassif, N. Testing, Validation, and Simulation of a Novel Economizer Damper Control Method. Buildings. 2024; 14(9): 2937. https://doi.org/10.3390/buildings14092937
9.  Zhang, Y., Jiang, H., and Zhang, D. Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning Approaches. Journal of Marine Science and Engineering. 2023; 11(7): 1440.       https://doi.org/10.3390/jmse11071440
10. Zhai, Z. J., Zhang, Z., Zhang, W., and Chen, Q. Y. Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 1—Summary of Prevalent Turbulence Models. HVAC&R Research, 2007; 13(6): 853–870.
        https://doi.org/10.1080/10789669.2007.10391459
11. Benhamadouche, S., Merzari, E. Modelling and Simulation of Turbulence in Energy Systems. 1st Edition, Woodhead Publishing, 2019.
        https://www.amazon.ca/-/fr/Sofiane-Benhamadouche/dp/0081023952
12. Wang, G., Dbouk, T., Wang, D., Pei, Y., Peng, X., Yuan, H., and Xiang, S. Experimental and numerical investigation on hydraulic and thermal performance in the tube-side of helically coiled-twisted trilobal tube heat exchanger. International Journal of Thermal Sciences. 2020; 153: 106328.
       https://doi.org/10.1016/j.ijthermalsci.2020.106328
13. Naved, M., and Dewan, A. Turbulence modeling and machine learning for performance optimization of solar air heaters: State-of-the-art and future directions. Applied Energy. 2026; 407: 127321.
       https://doi.org/10.1016/j.apenergy.2025.127321
 

تحت نظارت وف ایرانی