Characterization of flow transport within pore spaces of an open-work gravel bed

  • Authors

    • Susithra Lakshmanan Dept of Chemical Engineering and Biotechnology, University of Cambridge, UK
    • Gareth Pender Institute for Infrastructure and Environment, Heriot-Watt University, Edinburgh, United Kingdom;
    • Heather Haynes Institute for Infrastructure and Environment, Heriot-Watt University, Edinburgh, United Kingdom;
    • William Holmes Glasgow Experimental MRI Centre, Institute of Neuroscience and Psychology, University of Glasgow,United Kingdom
    2014-09-27
    https://doi.org/10.14419/ijet.v3i4.3430
  • Analysis of the flow dynamics within the near-bed and sub-surface regions of river bed sediment is critical in understanding fluid exchange and related chemical transfer/reactions. The knowledge in above is limited as these regions are difficult to measure using traditional instrumentation methods. In this paper, we tried the use of Magnetic Resonance Imaging (MRI) technique to non-invasively image flow dynamics of simulated river bed. We developed a bespoke MRI-compatible open-channel flume in order to acquire real-time flow images from within the MRI bore and used contrast agent (Gd-DTPA) as a tracer through an immobile, porous gravel bed. Single MR Image slices along the flume length were obtained for analysis. The flow tracer images from within the sediment bed are calibrated from the output data in order to provide fully quantitative maps of tracer concentration at regular time intervals. These ‘white-box’ (i.e. data from within the porous bed) tracer profiles were evaluated with the CXTFIT computer package to estimate the transport parameters. The intention was both, to illustrate the appropriateness of MRI for flow-sediment research and to analyse the relationship between tracer dispersion and gravel framework structure.

    Keywords: Porous Gravel Bed, Magnetic Resonance Imaging, Tracer Transport, CXTFIT.

  • References

    1. Marica,F., Chen, Q., Hamilton, A., Hall, C., Al, T., Balcom, B., Spatially resolved measurement of rock core porosity. Journal of Magnetic Resonance, vol.178, 2006, 136-141. http://dx.doi.org/10.1016/j.jmr.2005.09.003.
    2. Haynes, H., Vignaga, E and Holmes, W.M., Using magnetic resonance imaging for experimental analysis of fine-sediment infiltration into gravel beds. Sedimentology. vol.56, 2009, 1961-1975. http://dx.doi.org/10.1111/j.1365-3091.2009.01064.x.
    3. Johns, M.L. and Gladden, L.F. Surface-to-volume ratio of ganglia trapped in small-pore systems determined by pulsed-filed gradient nuclear magnetic resonance. J. Colloid.Interface Sci, vol.238, 2001, 96-104. http://dx.doi.org/10.1006/jcis.2001.7494.
    4. Baumann, T., Petsch, R., Niessner, R., Direct 3-D measurement of the flow velocity in porous media using magnetic resonance tomography. Environmental Science and Technology, vol.34, 2000, 4242-4248. http://dx.doi.org/10.1021/es991124i.
    5. Haynes, H., Ockelford A-M, Vignaga. E., Holmes, WM, A new approach to define surface/sub-surface transition in gravel beds. Acta Geophysica. Vol.60, 2012, 1589-1606. http://dx.doi.org/10.2478/s11600-012-0067-z.
    6. Bloembergen, N., Proton relaxation times in paramagnetic solutions. J. Chem. Phys, vol. 27, 1957, 572–573. http://dx.doi.org/10.1063/1.1743771.
    7. Phoenix, V.R., Holmes, W.M., Magnetic resonance imaging of structure, diffusivity and copper immobilization in a phototrophic biofilm. Appl. Environ. Microbiol, vol.74, 2008, 4934–4943. http://dx.doi.org/10.1128/AEM.02783-07.
    8. Levitt, MH, Spin Dynamics. John Wiley&Sons: England, 2002.
    9. Haacke, E.M., Brown, R.W., Thompson, M.R., Venkatesan, R., Magnetic resonance imaging: Physical Principles and Sequence Design, Wiley Liss, 1999.
    10. Von der Schulenburg, D.A.G., Holland, D.J., Paterson Beedle, M., Macaskie, L.E., Gladden, L.F., Johns, M.L., Spatially resolved quantification of metal ion concentration in a biofilm-mediated ion exchanger. Biotechnology and Bioengineering, vol.99, 2008, 821-829. http://dx.doi.org/10.1002/bit.21647.
    11. Toride N, Leij FJ, Van Genuchten MT, The CXTFIT code for estimating transport parameters from laboratory or field tracer experiments, version 2.1. U.S. Salinity Laboratory. Agricultural Research Service US Department of Agriculture, Riverside, 1999.
    12. Tang, G., Mayes, M.A., Parker, J.C., Jardine, P.M. CXTFIT/Excel-A modular adaptable code for parameter estimation, sensitivity analysis and uncertainty analysis for laboratory or field tracer experiments. Computers & Geosciences, vol.l36, 2010, 1200-1209.
    13. Wraith, J.M., Or, D., Nonlinear parameter estimation using spreadsheet software. Journal of Natural Resources and Life Sciences Education. vol.27, 1998, 13–19.
    14. Supriya Pal, Somnath Mukherjee and Sudipta Ghosh, Application of Hydrus 1D model for assessment of phenol-soil adsorption dynamics, Environ. Sci. Pollut. Res, 21, 2014, 5249–5261. http://dx.doi.org/10.1007/s11356-013-2467-2.
    15. Disli E, Batch and column experiments to support heavy metals (Cu, Zn and Mn) in alluvial sediments. Ground Water Monitoring & Remediation, vol. 30, no. 3, 2010, 125–139. http://dx.doi.org/10.1111/j.1745-6592.2010.01302.x.
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  • How to Cite

    Lakshmanan, S., Pender, G., Haynes, H., & Holmes, W. (2014). Characterization of flow transport within pore spaces of an open-work gravel bed. International Journal of Engineering & Technology, 3(4), 457-463. https://doi.org/10.14419/ijet.v3i4.3430