Literature DB >> 20561710

Mixing, spreading and reaction in heterogeneous media: a brief review.

Marco Dentz1, Tanguy Le Borgne, Andreas Englert, Branko Bijeljic.   

Abstract

Geological media exhibit heterogeneities in their hydraulic and chemical properties, which can lead to enhanced spreading and mixing of the transported species and induce an effective reaction behavior that is different from the one for a homogeneous medium. Chemical heterogeneities such as spatially varying adsorption properties and specific reactive surface areas can act directly on the chemical reaction dynamics and lead to different effective reaction laws. Physical heterogeneities affect mixing-limited chemical reactions in an indirect way by their impact on spreading and mixing of dissolved species. To understand and model large-scale reactive transport the interactions of these coupled processes need to be understood and quantified. This paper provides a brief review on approaches of non-reactive and reactive transport modeling in geological media.
Copyright © 2010 Elsevier B.V. All rights reserved.

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Year:  2010        PMID: 20561710     DOI: 10.1016/j.jconhyd.2010.05.002

Source DB:  PubMed          Journal:  J Contam Hydrol        ISSN: 0169-7722            Impact factor:   3.188


  11 in total

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4.  Scalar dissipation rates in non-conservative transport systems.

Authors:  Nicholas B Engdahl; Timothy R Ginn; Graham E Fogg
Journal:  J Contam Hydrol       Date:  2013-03-26       Impact factor: 3.188

5.  Trait-specific dispersal of bacteria in heterogeneous porous environments: from pore to porous medium scale.

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7.  Pore-Scale Hydrodynamics in a Progressively Bioclogged Three-Dimensional Porous Medium: 3-D Particle Tracking Experiments and Stochastic Transport Modeling.

Authors:  M Carrel; V L Morales; M Dentz; N Derlon; E Morgenroth; M Holzner
Journal:  Water Resour Res       Date:  2018-03-24       Impact factor: 5.240

8.  Fast micro-computed tomography data of solute transport in porous media with different heterogeneity levels.

Authors:  Stefanie Van Offenwert; Veerle Cnudde; Marijn Boone; Tom Bultreys
Journal:  Sci Data       Date:  2021-01-20       Impact factor: 6.444

9.  Machine learning to predict effective reaction rates in 3D porous media from pore structural features.

Authors:  Min Liu; Beomjin Kwon; Peter K Kang
Journal:  Sci Rep       Date:  2022-03-31       Impact factor: 4.379

10.  Surrogate modelling for the prediction of spatial fields based on simultaneous dimensionality reduction of high-dimensional input/output spaces.

Authors:  D Crevillén-García
Journal:  R Soc Open Sci       Date:  2018-04-25       Impact factor: 2.963

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