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) | Zendy

Budi Santoso | Zendy; Indarto Indarto | Zendy; Deendarlianto Deendarlianto | Zendy; Sanish Thomas | Zendy

Open Access

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(s) -

Budi Santoso,

Indarto Indarto,

Deendarlianto Deendarlianto,

Sanish Thomas

Publication year - 2012

Publication title -

modern applied science

Language(s) - English

Resource type - Journals

eISSN - 1913-1852

pISSN - 1913-1844

DOI - 10.5539/mas.v6n9p56

Subject(s) - flow (mathematics) , spectral density , artificial neural network , two phase flow , plug flow , mean flow , slug flow , power (physics) , support vector machine , mechanics , mathematics , materials science , computer science , statistics , physics , thermodynamics , artificial intelligence , turbulence

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.

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