A Bayesian approach to characterising multi-phase flows using magnetic resonance: application to bubble flows

Magnetic Resonance (MR) imaging is difficult to apply to multi-phase flows due to both the inherently short T?* 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%).