English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/151378
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Exportar a otros formatos:

Title

Anomalous transport in disordered fracture networks: Spatial Markov model for dispersion with variable injection modes

AuthorsKang, Peter K.; Dentz, Marco ; Le Borgne, Tanguy; Lee, Seunghak; Juanes, Rubén
KeywordsAnomalous transport
Continuous time random walks
Discrete fracture networks
Injection modes
Lagrangian velocity
Spatial Markov model
Stochastic modeling
Time domain random walks
Issue DateApr-2017
PublisherElsevier
CitationAdvances in Water Resources 2017
AbstractWe investigate tracer transport on random discrete fracture networks that are characterized by the statistics of the fracture geometry and hydraulic conductivity. While it is well known that tracer transport through fractured media can be anomalous and particle injection modes can have major impact on dispersion, the incorporation of injection modes into effective transport modeling has remained an open issue. The fundamental reason behind this challenge is that-even if the Eulerian fluid velocity is steady-the Lagrangian velocity distribution experienced by tracer particles evolves with time from its initial distribution, which is dictated by the injection mode, to a stationary velocity distribution. We quantify this evolution by a Markov model for particle velocities that are equidistantly sampled along trajectories. This stochastic approach allows for the systematic incorporation of the initial velocity distribution and quantifies the interplay between velocity distribution and spatial and temporal correlation. The proposed spatial Markov model is characterized by the initial velocity distribution, which is determined by the particle injection mode, the stationary Lagrangian velocity distribution, which is derived from the Eulerian velocity distribution, and the spatial velocity correlation length, which is related to the characteristic fracture length. This effective model leads to a time-domain random walk for the evolution of particle positions and velocities, whose joint distribution follows a Boltzmann equation. Finally, we demonstrate that the proposed model can successfully predict anomalous transport through discrete fracture networks with different levels of heterogeneity and arbitrary tracer injection modes. © 2017 Elsevier Ltd.
Publisher version (URL)https://doi.org/10.1016/j.advwatres.2017.03.024
URIhttp://hdl.handle.net/10261/151378
DOIhttp://dx.doi.org/doi.org/10.1016/j.advwatres.2017.03.024
Appears in Collections:(IDAEA) Artículos
Files in This Item:
File Description SizeFormat 
dfntransport_preprint.pdf4,27 MBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.