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Nanofluids as coolant in a shell and tube heat exchanger: ANN modeling and multi-objective optimization

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  • Hojjat, Mohammad

Abstract

In the present study, an artificial neural network (ANN) was developed to predict the thermal and hydrodynamic behavior of two types of Newtonian nanofluids used as coolants in a shell and tube heat exchanger (STHE). Inputs of the ANN model are nanoparticle volume concentration, Reynolds number, nanoparticle thermal conductivity, and Prandtl number. Results indicate that the ANN model predicts the experimental data with very high accuracy. Values of Nusselt number resulted from experiments and those obtained from the ANN have at most 9% difference, this value is 9.6% for the pressure drop. Multi-objective optimization was implemented with the aim of minimizing the total pressure drop and maximizing the nanofluids Nusselt number in the STHE according to NSGA-II algorithm. In optimization procedure nanofluids pressure drop and the Nusselt number (tube-side) was evaluated by the ANN model. To find the shell-side pressure drop method of Delaware was employed. Nanofluids concentration and Reynolds number were selected as decision parameters. The Pareto front was obtained. The best solution adopted from points on the Pareto front by two well-known decision-making methods LINMAP and TOPSIS. The Nusselt number of optimal solutions are about 30% greater than the base fluid and pressure drop of optimal solutions are about 10% lower than the base fluid.

Suggested Citation

  • Hojjat, Mohammad, 2020. "Nanofluids as coolant in a shell and tube heat exchanger: ANN modeling and multi-objective optimization," Applied Mathematics and Computation, Elsevier, vol. 365(C).
    • Handle: RePEc:eee:apmaco:v:365:y:2020:i:c:s0096300319307027
    DOI: 10.1016/j.amc.2019.124710
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    Cited by:

    1. Zhang, Shanhong & Yu, Guanghui & Guo, Yu & Wang, Yang, 2023. "Modelling development and optimization on hydrodynamics and energy utilization of fish culture tank based on computational fluid dynamics and machine learning," Energy, Elsevier, vol. 276(C).
    2. José Luis de Andrés Honrubia & José Gaviria de la Puerta & Fernando Cortés & Urko Aguirre-Larracoechea & Aitor Goti & Jone Retolaza, 2021. "Development and Application of a Multi-Objective Tool for Thermal Design of Heat Exchangers Using Neural Networks," Mathematics, MDPI, vol. 9(10), pages 1-23, May.
    3. Fang, Wenchao & Chen, Sheng & Shi, Shuo, 2022. "Dynamic characteristics and real-time control of a particle-to-sCO2 moving bed heat exchanger assisted by BP neural network," Energy, Elsevier, vol. 256(C).
    4. Sui, Zengguang & Sui, Yunren & Wu, Wei, 2022. "Multi-objective optimization of a microchannel membrane-based absorber with inclined grooves based on CFD and machine learning," Energy, Elsevier, vol. 240(C).
    5. Ma, Ting & Guo, Zhixiong & Lin, Mei & Wang, Qiuwang, 2021. "Recent trends on nanofluid heat transfer machine learning research applied to renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    6. Amir Zolghadri & Heydar Maddah & Mohammad Hossein Ahmadi & Mohsen Sharifpur, 2021. "Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM)," Sustainability, MDPI, vol. 13(16), pages 1-17, August.

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