Identification of Flow Regimes Using Raw EIT Measurements

A. Dupré1,3, G. Ricciardi1, S. Bourennane3,, S. Mylvaganam2

1 CEA Cadarache, STCP/LHC Laboratory, 13115 Saint-Paul-Les-Durance, FRANCE

2 University College of Southeast Norway, Department of EE, IT & Cybernetics, Faculty of Technology, Campus Porsgrunn, NORWAY

3 AMU, CNRS, Centrale Marseille, Institut Fresnel UMR 7249, 13013 Marseille, FRANCE

* antoine.dupre@alumni.epfl.ch

ABSTRACT

In multiphase flow studies, the distribution of the different phases fall into categories called ‘flow regimes’ depending on the prevailing rheological parameters. Analysis and measurements of the different phases are simplified by ascertaining the distribution of phases. The knowledge of flow regimes helps to select the right model and suitable control actions to alleviate conditions involving dangerous flow regimes such as slugs or to select pre-computed sensitivity matrix in back-projection algorithms for enhanced online image processing. EIT (Electrical Impedance Tomography) has been used in flow regime studies in the recent past. Usage of raw measurements from direct time series analysis of the raw EIT data prevents image processing, giving rise to easier and faster recognition of flow regimes using the non-invasive EIT sensor arrays. Simple algorithms can be implemented online providing a priori knowledge of the flow regimes. In the approach proposed by the authors, the time series of each inter-electrode normalized capacitance measurements taken over a suitable duration are characterized by the average and the standard deviation (SD). In an EIT system, criteria based on the eigenvalues of the matrices evolving from twin plane raw data are used to identify stratified, annular and intermittent flows and to quantify the instability level for transitional flows. For intermittent flows, a further analysis of the Fast Fourier Transform (FFT) of the time series is applied to derive the frequency of plugs and slugs.

Keywords Eigenvalues, Electrical Impedance Tomography (EIT), Flow regimes, Raw data, Time series