IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i15p4448-d599876.html
   My bibliography  Save this article

Development of Empirical Correlation of Two-Phase Pressure Drop in Moisture Separator Based on Separated Flow Model

Author

Listed:
  • Woo-Shik Kim

    (Innovative System Safety Research Division, Korea Atomic Energy Research Institute, Daejeon 34057, Korea)

  • Jae-Bong Lee

    (Innovative System Safety Research Division, Korea Atomic Energy Research Institute, Daejeon 34057, Korea)

  • Ki-Hwan Kim

    (Innovative System Safety Research Division, Korea Atomic Energy Research Institute, Daejeon 34057, Korea)

Abstract

Pressure drop across the moisture separator installed in the steam generator of a nuclear power plant affects the power generation efficiency, and so accurate pressure drop prediction is important in generator design. In this study, an empirical correlation is proposed for predicting the two-phase pressure drop through a moisture separator. To ensure the applicability of the correlation, a series of two-phase air-water experiments were performed, and the results of the present test and of the benchmark test of high-pressure steam-water were used in developing the correlation. Based on the experimental results, quality, dimensionless superficial velocity, density ratio of the working fluid, and the geometrical factor were considered to be important parameters. The two-phase pressure drop multiplier was expressed in terms of these parameters. The empirical correlation was found to predict the experimental results within a reasonable range.

Suggested Citation

  • Woo-Shik Kim & Jae-Bong Lee & Ki-Hwan Kim, 2021. "Development of Empirical Correlation of Two-Phase Pressure Drop in Moisture Separator Based on Separated Flow Model," Energies, MDPI, vol. 14(15), pages 1-13, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4448-:d:599876
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/15/4448/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/15/4448/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Xinping Li & Nailiang Li & Xiang Lei & Ruotong Liu & Qiwei Fang & Bin Chen, 2023. "Study on Artificial Neural Network for Predicting Gas-Liquid Two-Phase Pressure Drop in Pipeline-Riser System," Energies, MDPI, vol. 16(4), pages 1-13, February.
    2. Kihwan Kim & Woo-Shik Kim & Jae-Bong Lee, 2021. "An Experimental Performance Evaluation for a Swirl-Vane Separator Using an Air-Water Test Facility," Energies, MDPI, vol. 14(21), pages 1-14, October.

    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:14:y:2021:i:15:p:4448-:d:599876. 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.