Electrical resistance tomography (ERT) is a non-intrusive technique to image the electrical conductivity distribution of a close vessel by injecting exciting current into the vessel and measuring the boundary voltages induced. ERT image reconstruction is characterized as a severely nonlinear and ill-posed inverse problem with many unknowns. In recent years, there are a growing number of papers published which aim to determine the locations and shapes of inclusions by assuming their conductivities are piece-wise constant and isotropic. In this work, the boundary of inclusions is reconstructed by ERT with Boundary Element Method. The Jacobian matrix of forward problem is firstly calculated with a direct linearization method basing on boundary element, and validated through comparison with that determined by Finite Element Method and Analytical Method. A boundary reconstruction algorithm is later presented based on Levenberg-Marquardt (L-M) method. Several numerical simulations and static experiments were conducted to study the reconstruction quality, where much importance is put on the smoothness of boundaries in the reconstruction, thus a restriction of the curve radius is introduced to adjust the damping parameter for L-M algorithm. Analytical results on the stability and precision of the boundary reconstruction demonstrate that stable reconstruction can be achieved when the conductivity of the objects much differs from that of background medium, and convex boundaries can also be precisely reconstructed. Contrarily, the reconstructions for inclusions with similar conductivities to the background medium are not stable. The situation of an incorrect initial estimation on inclusions’ number is numerically studied and the results show that the boundary of inclusions could be correctly reconstructed with a splitting/merging function under the aforementioned proper operation condition of the present algorithm.

Oil-water two-phase flow widely exists and its measurement is of significance in oil well logging, oil transportations and etc. One of the techniques in common use in oil-water two-phase flow rate measurement is the Differential Pressure (DP) meters combining a theoretic model connecting the mass flow rate of the mixture with the differential pressures generated by the throttling element installed inside the pipeline. Though a number of publications focus on DP meters in measuring gas-water two-phase flows or wet gas, the existing models are still not very compatible in oil-water two-phase flow. In this work, a series of oil-water two-phase flow experiments were conducted in a horizontal pipe of 50mm diameter and the flow rate was measured by a V-cone meter with a diameter ratio of 0.65. Available correlations of DP meters developed from gas-water flow are studied and compared with the measured data from the V-cone meter. A modified correlation is proposed based on the influence of viscosity of oil upon the differential pressure model and three-dimensional Computerized Fluid Dynamic (CFD) simulations. The results have shown that the proposed method achieves better accuracy in oil-water two-phase flow rate measurement than other DP correlations, and it can be extended to other oil-water flow conditions by adjusting the tuning factor.

Gas/liquid two-phase flow is of great importance in various industrial processes. As the most important characteristic of a two-phase flow, the flow regime not only characterizes the flow condition in an explicit way, but also determines the measurement model in each measuring method. Based on the application of Electrical Resistance Tomography (ERT) to a gas/liquid two-phase flow on a vertical pipe, features reflecting the characteristics of gas/liquid two-phase flow are extracted directly from the measured data without reconstruction of the cross-sectional images. The statistical features are derived through time series statistical analysis. Meanwhile features in the wavelet-scale domain are derived through both one-dimensional and two-dimensional wavelet transform. All extracted features are considered as the input of a Support Vector Machine (SVM) algorithm to recognize the flow regime. The preliminary results show that the feature extraction methods of multi-feature fusion and high-dimensional wavelet transform are suitable for the identification of gas/liquid two-phase flow regimes.