Increasing image resolution for wire-mesh sensor based on statistical reconstruction

Abstract

Wire-mesh sensors (WMS) are able to generate cross-sectional images of multiphase flow and have been widely used to investigate flow phenomena in pilot plant studies. Such devices are able to measure flow parameters such as phase fraction (e.g. gas/liquid fraction) distribution and visualize multiphase flows with high temporal and spatial resolution. Hence, being important tool for detailed flow investigation. However, its sensing principle is based on intrusive electrodes placed inside the pipe where a multiphase flow streams. The image resolution generated by the sensor is given by the number of crossing points formed by the transmitter and receptor wires. In many processes, however, the intrusive effect of such sensor might be a limitation on its use. Therefore, a reduced number of wires could possibly increase the application field of wire-mesh sensors. For this reason, the present work presents an image reconstruction method to increase resolution of WMS data with less than optimal number of electrode wires. In this way, a reduction of intrusive effects on the process under investigation may be achieved. The reconstruction method is based on statistical view of regularization and is known as Maximum a Posterior (MAP). 16x16 WMS flow data are used to determine a Multivariate Gaussian flow model, which in turn is used as regularization in the reconstruction. A sensitive matrix is estimated by finite element method (FEM) to incorporate MAP algorithm. Experimental data are used to validate the proposed method, which is compared with spline interpolation. Experimental results show that the MAP reconstruction performs better than interpolation and achieves deviation in gas void fraction estimation in the range of ±10% in the vast majority of operating points. The tests were performed in a horizontal water-gas flow loop operating at intermittent (slug) flow regime.