IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i24p6726-d465341.html
   My bibliography  Save this article

Modeling Study on Heat Transfer in Marangoni Dropwise Condensation for Ethanol-Water Mixture Vapors

Author

Listed:
  • Jinshi Wang

    (State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Ziqiang Ma

    (State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Yong Li

    (Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China)

  • Weiqi Liu

    (State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Gen Li

    (State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

In this paper, a model was developed to predict the heat transfer characteristics of Marangoni dropwise condensation. In accordance with the feature of Marangoni condensation, condensation was treated as dropwise condensation of mixture vapors. The condensation space was divided into two parts: the vapor diffusion layer and the condensate layer. For the condensate layer, the classical heat transfer calculation method of dropwise condensation was imitated to obtain the heat transfer characteristics. For the vapor diffusion layer, the heat transfer characteristics were achieved by solving the conservation equations. These heat transfer characteristics were coupled through the conjunct boundary, which was the vapor-liquid interface. The model was applied to the condensation of water-ethanol mixture vapors. A comparison with the existing experimental data showed that the developed model could basically reflect the influences of vapor-to-surface temperature difference, vapor concentration, vapor pressure, and vapor velocity on heat transfer characteristic of Marangoni condensation. Results showed that some differences existed between the calculation results and experimental results, but the prediction deviation of the model could be acceptable in the range of vapor-to-surface temperature difference where the condensation heat transfer coefficients reached peak values.

Suggested Citation

  • Jinshi Wang & Ziqiang Ma & Yong Li & Weiqi Liu & Gen Li, 2020. "Modeling Study on Heat Transfer in Marangoni Dropwise Condensation for Ethanol-Water Mixture Vapors," Energies, MDPI, vol. 13(24), pages 1-19, December.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:24:p:6726-:d:465341
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/24/6726/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/24/6726/
    Download Restriction: no
    ---><---

    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:gam:jeners:v:13:y:2020:i:24:p:6726-:d:465341. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.