General theory of dispersion in porous media

Scheidegger, Adrian E.

doi:10.1029/jz066i010p03273

Cited by 533 publications

(287 citation statements)

References 2 publications

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“…In conventional 1-D hydraulic models, the transport of a fully mixed, passive pollutant or tracer is described by the 1-D advection-dispersion equation, also known as the Fickian model [40,33]: major questions are the following: Is most of the pollutant fully mixed and is the dispersive equilibrium reached so that the Fickian model applies? Are transient storage effects negligible or would they require the consideration of additional terms?…”

Section: Introductionmentioning

confidence: 99%

Calibrating pollutant dispersion in 1-D hydraulic models of river networks

Launay¹,

Coz²,

Camenen³

et al. 2015

Journal of Hydro-environment Research

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The objective of this article is to investigate the major issues associated with the calibration of the pollutant dispersion in 1-D hydraulic models applied to river networks, especially large, complex, artificialized ones where ecological and socio-economical threats are important. Such issues are illustrated and discussed using the results of five fluorescent tracer experiments conducted in contrasted open-channel systems, ranging from a simple trapezoidal canal to a more complex river network. Experimental dispersion values were quantified using both the change of moment method and a simple fit-by-eye procedure for eight river reaches with homogeneous hydraulic conditions and an achieved tracer mixing and dispersive equilibrium. Since dispersion coefficient values depend on the assumed dispersion model, ideally they should be calibrated using the same model in which they are to be used, as was done in this study. We also derived concurrent longitudinal dispersion values using the velocity field measured by hydro-acoustic profilers (ADCP), which appears as a promising and cost-efficient technique for documenting dispersion in large river systems. It appears that the formulae for which the fit was mainly based on the cross-sectional aspect ratio are generally more appropriate for field data than those which are sensitive to the velocity to shear velocity ratio. The interpretation of complex dispersion and mixing processes, along with the selection of relevant dispersion coefficient predictors are key to minimizing errors in the numerical simulation of pollution dynamics in river networks.

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Section: Introductionmentioning

confidence: 99%

Calibrating pollutant dispersion in 1-D hydraulic models of river networks

Launay¹,

Coz²,

Camenen³

et al. 2015

Journal of Hydro-environment Research

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“…Another theory of dispersion in porous media is that of Scheidegger (1955Scheidegger ( , 1961, He agrees with Saffman that com plexity of the pore system is the cause of individual fluid elements to be mixed with each other. This is the process of dispersion which is distinguished from diffusion which is caused by the intrinsic motion of the molecules.…”

Section: Some Aspects On Diffusion and Leachingsupporting

confidence: 52%

“…Another feature of the flow which should be noted is the variation of velocity along the streamlines, A particle may be delayed or accelerated at various points along its path. The particle's average velocity over the entire length of path may differ greatly from the average velocity of the whole fluid (see Scheidegger, 1961). The presence of soluble salts in the soil-water system changes the physical properties of a soil-water depending on the surface tension system and density of the soil solution or hydration and flocculation of the soil colloid (see Richards and Weaver, 1944), Case of intermittent ponding followed by gravity drainage In intermittent ponding all that occurs in continuous ponding is repeated after each water increment application.…”

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Development and comparison of some methods for leaching saline soils

Bakr

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“…We model this phenomenon by an apparent dispersion tensor, D, dependant on the local Darcy velocity of the fluid u (see e.g., [49,51]) as follows:…”

Section: Mathematical Model: Surface Flux In C-ed Processes In Porousmentioning

confidence: 99%

Gaussian process modelling for uncertainty quantification in convectively-enhanced dissolution processes in porous media

Crevillén-García

Wilkinson

Shah

et al. 2017

Advances in Water Resources

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(2017) Gaussian process modelling for uncertainty quantification in convectively-enhanced dissolution processes in porous media. Advances in Water Resources, 99 . pp. 1-14. Permanent WRAP URL:http://wrap.warwick.ac.uk/85680 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. AbstractNumerical groundwater flow and dissolution models of physico-chemical processes in deep aquifers are usually subject to uncertainty in one or more of the model input parameters. This uncertainty is propagated through the equations and needs to be quantified and characterised in order to rely on the model outputs. In this paper we present a Gaussian process emulation method as a tool for performing uncertainty quantification in mathematical models for convection and dissolution processes in porous media. One of the advantages of this method is its ability to significantly reduce the computational cost of an uncertainty analysis, while yielding accurate results, compared to classical Monte Carlo methods. We apply the methodology to a model of convectively-enhanced dissolution processes occurring during carbon capture and storage. In this model, the Gaussian process methodology fails due to the presence of multiple branches of solutions emanating from a bifurcation point, i.e., two equilibrium states exist rather than one. To overcome this issue we use a classifier as a precursor to the Gaussian process emulation, after which we are able to successfully perform a full uncertainty analysis in the vicinity of the bifurcation point.

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General theory of dispersion in porous media

Cited by 533 publications

References 2 publications

Calibrating pollutant dispersion in 1-D hydraulic models of river networks

Calibrating pollutant dispersion in 1-D hydraulic models of river networks

Development and comparison of some methods for leaching saline soils

Gaussian process modelling for uncertainty quantification in convectively-enhanced dissolution processes in porous media

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