Wire-Mesh Sensor Super-Resolution Based on Statistical Reconstruction

• Published in: IEEE transactions on instrumentation and measurement 2021, Vol.70, p.1-12
• Main Authors: Dias, Felipe D. A., Pipa, Daniel R., Morales, Rigoberto E. M., Silva, Marco J. da
• Format: Article
• Language: English
• Subjects: Algorithms; Conductivity; Deformation effects; Electric fields; Electrodes; Finite element method; Image resolution; Inverse problem; Liquid hold up; Multiphase flow; Parameter estimation; Pipes; Pressure drop; Receivers; Reconstruction; Regularization; Sensors; Slug flow; Spatial resolution; statistical image reconstruction; Transmitters; Void fraction; Wire; wire-mesh sensor (WMS); Wires
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2021.3058362

A wire-mesh sensor (WMS) is a widely used instrument to visualize and estimate derived parameters of multiphase flows, e.g., gas void fraction or liquid hold-up. The spatial resolution of obtained flow images is associated with the number of crossing points formed by the transmitter and receiver wires of a given sensor. This may be a limitation for applications that require high spatial resolution since WMS is an intrusive device and the increase of electrodes may increase pressure drop and deform/fragment bubbles. In order to minimize such undesirable effects and maximize the sensor resolution, we employed a reconstruction algorithm based on the minimum mean-square error (MMSE) estimator to increase image resolution of WMS with fewer wires than commonly reported in the literature, i.e., here, we apply 8\times 8 , 6\times 6 , 4\times 4 , and 2\times 2 sensors for 1-in pipe. Since standard regularization approaches may provide incorrect solutions for such configurations, a new methodology to obtain the prior model is presented. In our approach, the prior is assumed as a multivariate Gaussian model, which is extracted from experimental flow data of a 16\times 16 WMS (the most common resolution for 1-in pipe). Finally, the sensitivity matrix obtained by electric field simulation and the experimental prior model is incorporated into the MMSE algorithm to restore experimental flow data of the low-resolution sensors. The experiments were performed in a flow loop operating at slug flow. The experimental results suggest that the MMSE estimator combined with the experimental prior model has a high potential not only to improve image resolution but also to correct the average void fraction estimation.