IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v365y2020ics0096300319307027.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300319307027
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2019.124710?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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).
    3. 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).
    4. 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).
    5. 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).
    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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:365:y:2020:i:c:s0096300319307027. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.