title: A Bayesian approach to characterising multi-phase flows using magnetic resonance: application to bubble flows.
abstract: Magnetic Resonance (MR) imaging is difficult to apply to multi-phase flows due to both the inherently short T₂* characterising such systems and the relatively long time taken to acquire the data. We develop a Bayesian MR approach for analysing data in k-space that eliminates the need for image acquisition, thereby significantly extending the range of systems that can be studied. We demonstrate the technique by measuring bubble size distributions in gas-liquid flows. The MR approach is compared with an optical technique at a low gas fraction (∼2%), before being applied to a system where the gas fraction is too high for optical measurements (∼15%).
authors.affiliation: Department of Chemical Engineering and Biotechnology, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, United Kingdom. djh79@cam.ac.uk
authors.forename: D J
authors.initials: DJ
authors.lastname: Holland
authors.forename: A
authors.initials: A
authors.lastname: Blake
authors.forename: A B
authors.initials: AB
authors.lastname: Tayler
authors.forename: A J
authors.initials: AJ
authors.lastname: Sederman
authors.forename: L F
authors.initials: LF
authors.lastname: Gladden
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D005740:Gases / Q000032:analysis*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008863:Microspheres; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012212:Rheology / Q000379:methods*; D012680:Sensitivity and Specificity
chemical_list: D005740:Gases