A non-invasive study of flow dynamics in membrane distillation hollow fiber modules using low-field nuclear magnetic resonance imaging (MRI)
Low-field bench-top nuclear magnetic resonance imaging (MRI) has been applied to investigate the hydrodynamics in novel hollow fiber modules with four different configurations of randomly-packed, spacer-knitted, curly and semi-curly fibers, specifically designed for the membrane distillation (MD) pr...
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Main Authors: | , , , , |
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Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2013
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/79607 http://hdl.handle.net/10220/17356 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | Low-field bench-top nuclear magnetic resonance imaging (MRI) has been applied to investigate the hydrodynamics in novel hollow fiber modules with four different configurations of randomly-packed, spacer-knitted, curly and semi-curly fibers, specifically designed for the membrane distillation (MD) process. Imaging, spatially resolved velocity maps and propagators (probability distributions of displacement/velocity) were all acquired in the modules with flow in the shell side. The MRI data were correlated with overall module performance.
The results have revealed that the curly configuration exhibited more significant transverse flow and hence enhanced mixing, compared to the randomly packed configuration; this was consistent with an enhanced MD performance in terms of permeation flux. Interestingly, the velocity maps of the spacer-knitted fiber design indicated a significant flow channeling in the center of the module, despite its enhanced MD performance. Fortunately, combined with further investigations on the localized velocity images of this configuration, the acquisition of propagators provided valuable information in revealing the existence of reduced stagnant regions and significant transverse flow at varied operating conditions, which indicated a better overall mixing and hence confirmed its module performance. |
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