Annular flow pattern recognition using statistical data analyses of Electrical Impedance Tomography

Collecting very large amount of data from experimental multiphase measurement is a common practice in almost every scientific domain. There is a great need to have specific techniques capable of extracting synthetic information, which is essential to understand and model the specific flow phenomena. The intention of developing a method for recognition of flow regime using decomposition mathematical technique comes from the fact that each regime is characterised by typical dynamic behaviour. To recognise the flow dynamic structures, means indeed the recognition of the prevalent regime moreover indicates the actual flow conditions of the monitored area. The direct approach of Proper Orthogonal Decomposition (POD) as introduced by Lumley and the Linear Stochastic Estimation (LSE) as introduced by Adrian are used to identify typical multiphase flow instability. The present approach of statistical data-analysis extends the current evaluation procedure of Electrical Impedance Tomography (EIT) applied on air-water flow measurement. Wavelet Transformation and Kalman Filtering was used as complementary techniques for motion of fluid and flow structures detection and decomposed EIT signal similarity estimation. The paper demonstrates the capability of EIT measurement techniques combined with POD/LSE post-processing for studying annular flow patterns in vertical and horizontal pipeline.