IDEAS home Printed from https://ideas.repec.org/a/ibn/masjnl/v6y2012i9p56.html
   My bibliography  Save this article

The Identification of Gas-liquid Co-current Two Phase Flow Pattern in a Horizontal Pipe Using the Power Spectral Density and the Artificial Neural Network (ANN)

Author

Listed:
  • Budi Santoso
  • Indarto Indarto
  • Deendarlianto Deendarlianto
  • Thomas S. W.

Abstract

This paper presents a new method of the flow pattern identification on the basis of the analysis of Power Spectral Density (PSD) from the pressure difference data of horizontal flow. Seven parameters of PSD curve such as mean (K1), variance (K2), mean at 1-3 Hz (K3), mean at 3-8 Hz (K4), mean at 8-13 Hz (K5), mean at 13-25 Hz (K6) and mean at 25-30 Hz (K7) were used as training vector input of Artificial Neural Networks (ANN) in order to identify the flow patterns. From the obtained experimental of 123 operating conditions consisting of stratified flow, plug and slug, ANN was trained by using 100 data operation and 23 tested data. The results showed that the new method has a capability to identify the flow patterns of gas-liquid two phase flow with a high accuracy.

Suggested Citation

  • Budi Santoso & Indarto Indarto & Deendarlianto Deendarlianto & Thomas S. W., 2012. "The Identification of Gas-liquid Co-current Two Phase Flow Pattern in a Horizontal Pipe Using the Power Spectral Density and the Artificial Neural Network (ANN)," Modern Applied Science, Canadian Center of Science and Education, vol. 6(9), pages 1-56, September.
    • Handle: RePEc:ibn:masjnl:v:6:y:2012:i:9:p:56
    as

    Download full text from publisher

    File URL: https://ccsenet.org/journal/index.php/mas/article/download/20121/13274
    Download Restriction: no
    File URL: https://ccsenet.org/journal/index.php/mas/article/view/20121
    Download Restriction: no
    ---><---

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    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:ibn:masjnl:v:6:y:2012:i:9:p:56. 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: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    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.