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Ruben Juanes. Kang, Peter Kyungchul Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering. Massachusetts Institute of Technology. Department of Civil and Environmental Engineering 2011-01-26T14:27:31Z 2011-01-26T14:27:31Z 2010 2010 http://hdl.handle.net/1721.1/60805 696007949 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010. Cataloged from PDF version of thesis. Includes bibliographical references (p. 53-55). We study transport in fractured systems using a stochastic particle tracking approach. We represent a fractured system as a two-dimensional lattice network system where the transport velocity in each fracture is a random variable. Our goal is to develop an exact effective macroscopic model for the concentration mean and variance from the microscopic disorder model. Within a Lagrangian transport framework, we derive effective equations for particle transport by coarse graining and ensemble averaging of the local scale Langevin equations. The results show that the mean transport can be captured exactly by an uncoupled continuous time random walk (CTRW) and the variance of the concentration by a novel two-particle CTRW formulation. Information about variance of concentration between realizations is important for understanding predictability. Therefore, ensemble mean together with variance provide critical information for understanding and predicting transport through the lattice network. by Peter Kyungchul Kang. S.M. 55 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Civil and Environmental Engineering. Transport in lattice fracture networks : concentration mean and variance Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="456cf030-bb1d-4eab-9a42-041f13f8a7cd"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Transport in lattice fracture networks : concentration mean and variance</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2010</PublicationDate> <Authors> <Author> <DisplayName>Kang, Peter Kyungchul</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Civil and Environmental Engineering.</Keyword> <Abstract>We study transport in fractured systems using a stochastic particle tracking approach. We represent a fractured system as a two-dimensional lattice network system where the transport velocity in each fracture is a random variable. Our goal is to develop an exact effective macroscopic model for the concentration mean and variance from the microscopic disorder model. Within a Lagrangian transport framework, we derive effective equations for particle transport by coarse graining and ensemble averaging of the local scale Langevin equations. The results show that the mean transport can be captured exactly by an uncoupled continuous time random walk (CTRW) and the variance of the concentration by a novel two-particle CTRW formulation. Information about variance of concentration between realizations is important for understanding predictability. Therefore, ensemble mean together with variance provide critical information for understanding and predicting transport through the lattice network.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>