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Title: A data fusion and visualisation platform for multi-phase flow by electrical tomography
Author: Wang, Qiang
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2017
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Electrical tomography, e.g. electrical resistance tomography (ERT) and electrical capacitance tomography (ECT), has been successfully applied to many industries for measuring and visualising multiphase flow. This research aims to investigate the data fusion and visualisation technologies with electrical tomography as the key data processing tools of a platform for multiphase flow characterisation. Gas-oil-water flow is a common flow in the gas and oil industries but still presents challenges in understanding its complex dynamics. This research systematically studied the data fusion and visualisation technologies using dual-modality electrical tomography (ERT-ECT). Based on a general framework, two data fusion methods, namely threshold and fuzzy logic with decision tree, were developed to quantify and qualify the flow. The experimental results illustrated the feasibility of the methods integrated with the framework to visualise and measure flows in six typical common flow regimes, including stratified, wavy stratified, slug, plug, annular, and bubble flow. In addition, the performance of ERT-ECT was also evaluated. A 3D visualisation approach, namely Bubble Mapping, was proposed to transform concentration distribution to individual bubbles. With a bubble-based lookup table and enhanced isosurface algorithms, the approach overcomes the limits of the conventional concentration tomograms in visualisation of bubbles with sharp boundaries between gas and liquid, providing sophisticated flow dynamic information. The experiments proved that Bubble Mapping is able to visualise typical flow regimes in different pipeline orientations. Two sensing methods were proposed, namely asymmetrical sensing and imaging (ASI) and regional imaging with limited measurement (RILM), to improve the precision of the velocity profile derived from the cross-correlation method by enhancing ERT sensing speed, which is particularly helpful for industrial flows that their disperse phase velocity is very high, e.g. 20 m/s of the gas phase. It is expected that the outcome of this study will significantly move electrical tomography for multiphase flow applications beyond its current challenges in both quantification and qualification.
Supervisor: Wang, Mi ; Jia, Xiaodong Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available