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% $ biblatex auxiliary file $
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\refsection{0}
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    \entry{MEYER2021103936}{article}{}
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        {{hash=37d55c92e258fd275792cded40da151b}{%
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      \field{extraname}{1}
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      \field{sortinit}{1}
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      \field{abstract}{Over the past years, tomographic scanning techniques like micro-CT have become popular for the acquisition of high-fidelity void-space geometries of natural porous media (e.g., Bultreys et al., 2016; Raeini et al., 2017). Limitations both in computing time and memory prohibit, however, direct numerical simulations of flow and transport in large resp. detailed sample geometries. Flow or pore networks derived from scans alleviate this limitation, but still necessitate a methodology to extrapolate to larger samples. In this work, we present a network generation algorithm that is particularly suited for heterogeneous irregular networks. While emulating from an existing base network new networks of equal or larger sizes, the outlined algorithm scales approximately linearly with the network node or pore count and maintains (1) node connectivity resp. pore coordination-number statistics, (2) geometrical pore/throat properties, as well as (3) the potentially inhomogeneous spatial clustering of pores. While existing methods address the first two properties, the third point is crucial especially in heterogeneous media to match flow/transport properties like the permeability that have a strong dependence on the spatial distance between connected pores. Moreover, the cubical networks generated by our algorithm are periodic in all spatial directions, thus eliminating topological boundary effects, which are not present in natural media. Bounded networks of arbitrary sizes can then be recovered by cutting the generated networks and thus flow/transport processes at larger scales can be studied while incorporating physically-based descriptions of pore-scale processes.}
      \field{issn}{0309-1708}
      \field{journaltitle}{Advances in Water Resources}
      \field{title}{Random generation of irregular natural flow or pore networks}
      \field{volume}{152}
      \field{year}{2021}
      \field{pages}{103936}
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      \keyw{Dendrogram,Clustering,Networks,Generator,Connectivity,Heterogeneity}
    \endentry
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    \entry{MEYER2021101592}{article}{}
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      \field{abstract}{State-of-the-art tomographic scanning techniques provide detailed pore-space geometries of natural porous media, which are central for the study of subsurface flow and transport. Due to experimental and computational limitations, the extraction of high-resolution images is limited to relatively small sample volumes. To reduce the amount of data and the physical complexity, pore-space geometries are routinely translated into pore network models. Subsequently, such networks are expanded in space with suitable computational methods to determine effective medium parameters at larger scales relevant in engineering applications. While existing methods can provide networks with effective flow parameters being consistent with experimental data for comparably homogeneous media such as bead packs and sandstones, these methods are inadequate for more complex heterogeneous rocks such as carbonates or become too expensive for large networks. The netflow Python library accompanying this paper extends existing methods by preserving pore clusters that are a key characteristic of heterogeneous rocks. To this end dendrograms are extracted from experimental data and perturbed when generating larger networks. Moreover, the methods included in the netflow library are implemented in computationally efficient ways and enable the generation of large periodic networks that virtually eliminate boundary effects, which interfere in existing methods. • The netflow Python library enables the generation of large irregular networks, as it preserves pore or node clusters which are present in certain natural rock types. • The netflow Python library allows for the generation and flow analysis of boundary-free periodic networks. It further includes methods to convert periodic networks into conventional cubical ones.}
      \field{issn}{2215-0161}
      \field{journaltitle}{MethodsX}
      \field{title}{Netflow Python library – A free software tool for the generation and analysis of pore or flow networks}
      \field{volume}{8}
      \field{year}{2021}
      \field{pages}{101592}
      \range{pages}{1}
      \verb{doi}
      \verb https://doi.org/10.1016/j.mex.2021.101592
      \endverb
      \verb{urlraw}
      \verb https://www.sciencedirect.com/science/article/pii/S2215016121003824
      \endverb
      \verb{url}
      \verb https://www.sciencedirect.com/science/article/pii/S2215016121003824
      \endverb
      \keyw{Dendrogram,Cluster,Heterogeneity,Connectivity,Porous media,Pathway,Periodic,Unbounded,Digital rock analysis}
    \endentry
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    \entry{hypre-web-page}{misc}{}
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        {Lawrence Livermore National Laboratory}%
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      }
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      \field{labeltitlesource}{title}
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      \field{note}{\url{http://www.llnl.gov/CASC/hypre/}}
      \field{title}{{\sl hypre}: High Performance Preconditioners}
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    \endentry
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