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Quantifying Mixing using Magnetic Resonance Imaging

Quantifying Mixing using Magnetic Resonance Imaging

Published on: January 25, 2012

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

D J Holland¹, A Blake, A B Tayler

¹Department of Chemical Engineering and Biotechnology, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, United Kingdom. djh79@cam.ac.uk

Journal of Magnetic Resonance (San Diego, Calif. : 1997)

|January 29, 2011

Summary

We developed a new Bayesian Magnetic Resonance (MR) method to analyze multi-phase flows without image acquisition. This technique successfully measured bubble size distributions in gas-liquid flows, even at high gas fractions unsuitable for optical methods.

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Area of Science:

Fluid dynamics
Magnetic Resonance (MR) physics
Bayesian inference

Background:

Magnetic Resonance (MR) imaging is challenging for multi-phase flows due to short T₂* relaxation times and lengthy data acquisition.
Existing MR imaging techniques are limited in their applicability to dynamic and complex flow systems.

Purpose of the Study:

To develop a novel Bayesian MR approach for analyzing multi-phase flows without conventional image acquisition.
To extend the applicability of MR techniques to a wider range of challenging flow systems.
To accurately measure bubble size distributions in gas-liquid flows.

Main Methods:

A Bayesian approach was developed to analyze Magnetic Resonance (MR) data directly in k-space.
The method eliminates the need for traditional MR image reconstruction, reducing acquisition time and overcoming T₂* limitations.
Bubble size distributions in gas-liquid flows were measured using the developed MR technique.

Main Results:

The Bayesian MR approach was successfully demonstrated for measuring bubble size distributions in gas-liquid flows.
The technique was validated against an optical method at a low gas fraction (approximately 2%).
The MR method was applied to a high gas fraction system (approximately 15%), where optical measurements were not feasible.

Conclusions:

The developed Bayesian MR technique offers a powerful, non-imaging alternative for studying multi-phase flows.
This approach significantly broadens the scope of MR applications in fluid dynamics, particularly for systems with short T₂*.
The method provides accurate bubble size distribution measurements in challenging gas-liquid flow regimes.