CommunicationA 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 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%).

Graphical abstractPhotograph of a bubble swarm and comparison of the corresponding magnetic resonance and optical measurements of the bubble size distribution.Download high-res image (147KB)Download full-size imageResearch highlights► Bayesian approach developed to characterise image features directly in k-Space. ► Enables measurements of transient systems that could not be studied conventionally. ► Approach validated by numerical simulations. ► Demonstrated experimentally on bubble sizing in a gas-liquid flow. ► Applicable to low magnetic field and poor signal-to-noise ratio systems.