Wang, M (2017) Metrology of Tomography for Engineering. In: Proceedings of the 2nd International Symposium on Image based Metrology (ISIMet 2). ISIMet 2, 16-21 Dec 2017, Maui, Hawaii, USA. Virginia Tech
Abstract
Tomographic imaging has unique features at “seeing” through the optical opaque medium and “building” up a volumetric view of multiphase dynamics in process pipelines or reactors in a nonintrusive manner. At other aspects, raw tomograms are reconstructed with a specific algorithm, which usually present distributions of a specific physical property of the process medium, e.g. the electrical conductivity of mixture in the use of electrical resistance tomography. However, these volumetric measurements are not conventionally understood, which may create a level of challenges in industrial application. Normally, specific theories or methods have to be employed to convert raw tomograms to meaningful engineering data. This paper reviews typical methods in tomographic data fusion and their implementations for process applications via a number of case studies carried out by the author and his team, providing a glance of view of the imaging metrology. The electrical resistance tomography is particularly expressed for measurement of various multiphase flows, including mixing processes, slurry, oil-in-water and gas-in-water two-phase flows, as well as 3-dimensional rendered flow regimes. It emphasises the important role of engineering data fusion in the metrology of tomography for engineering application.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Tomography; Metrology; Engineering |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Funding Information: | Funder Grant number EPSRC EP/K503836/1 EURAMET EMRP-MSU ENG58-REG2 EPSRC EP/H023054/1 EPSRC EP/K503836/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 02 Feb 2018 16:51 |
Last Modified: | 02 Feb 2018 16:53 |
Status: | Published |
Publisher: | Virginia Tech |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:126971 |