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)

Santoso, Budi and Indarto, and Deendarlianto, and W., Thomas S. (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, 6 (9). pp. 56-67. ISSN 1913-1844 E-ISSN 1913-1852

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    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.

    Item Type: Article
    Subjects: T Technology > T Technology (General)
    T Technology > TJ Mechanical engineering and machinery
    T Technology > TK Electrical engineering. Electronics Nuclear engineering
    Divisions: Lembaga Penelitian dan Pengabdian Kepada Masyarakat - LPPM
    Depositing User: Anis Fagustina
    Date Deposited: 08 May 2014 10:25
    Last Modified: 08 May 2014 10:25
    URI: https://eprints.uns.ac.id/id/eprint/15381

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