Wire-Mesh Sensor Super-Resolution Based on Statistical Reconstruction
| Autor: | Felipe de Assis Dias, Daniel R. Pipa, Marco Jose da Silva, Rigoberto E. M. Morales |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | IEEE Transactions on Instrumentation and Measurement. 70:1-12 |
| ISSN: | 1557-9662 0018-9456 |
| DOI: | 10.1109/tim.2021.3058362 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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