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Commit 9f6b776a authored by sfritschi's avatar sfritschi
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Updated thesis

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netflow/netgen.py
View file @ 9f6b776a
......@@ -155,11 +155,10 @@ class CellList:
def flatten(self, i: int, j: int, k: int) -> int:
return i + self.nCells[0] * (j + self.nCells[1] * k)
def pore_to_triplet(self, poreIdx: int) -> List[int]:
def pore_to_triplet(self, pore: Pore) -> List[int]:
from math import floor
# Shift pore position to be positive first (buffer layer)
porePos = self.fetch_pos(poreIdx)
return [floor( (porePos[i] + self.Lmax) * self.invCellSizes[i]) \
return [floor( (pore.pos[i] + self.Lmax) * self.invCellSizes[i]) \
for i in range(self.dim)]
# Compute index of given pore in cell list based on position
def pore_to_index(self, pore: Pore) -> int:
......
thesis/bib/netflow.bib 0 → 100644
View file @ 9f6b776a
@article{MEYER2021101592,
title = {Netflow Python library – A free software tool for the generation and analysis of pore or flow networks},
journal = {MethodsX},
volume = {8},
pages = {101592},
year = {2021},
issn = {2215-0161},
doi = {https://doi.org/10.1016/j.mex.2021.101592},
url = {https://www.sciencedirect.com/science/article/pii/S2215016121003824},
author = {Daniel W. Meyer},
keywords = {Dendrogram, Cluster, Heterogeneity, Connectivity, Porous media, Pathway, Periodic, Unbounded, Digital rock analysis},
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.}
}
\ No newline at end of file
thesis/thesis.aux
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......@@ -22,6 +22,8 @@
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......@@ -34,12 +36,19 @@
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thesis/thesis.bbl
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......@@ -33,6 +33,7 @@
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......@@ -56,6 +57,44 @@
\endverb
\keyw{Dendrogram,Clustering,Networks,Generator,Connectivity,Heterogeneity}
\endentry
\entry{MEYER2021101592}{article}{}
\name{author}{1}{}{%
{{hash=37d55c92e258fd275792cded40da151b}{%
family={Meyer},
familyi={M\bibinitperiod},
given={Daniel\bibnamedelima W.},
giveni={D\bibinitperiod\bibinitdelim W\bibinitperiod}}}%
}
\strng{namehash}{37d55c92e258fd275792cded40da151b}
\strng{fullhash}{37d55c92e258fd275792cded40da151b}
\strng{bibnamehash}{37d55c92e258fd275792cded40da151b}
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\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\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
\entry{petsc-web-page}{misc}{}
\name{author}{34}{}{%
{{hash=0661bc042688479e939e81e7f9185f66}{%
......@@ -235,8 +274,8 @@
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......@@ -253,8 +292,8 @@
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{Lawrence Livermore National Laboratory}%
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\field{note}{\url{http://www.llnl.gov/CASC/hypre/}}
\field{title}{{\sl hypre}: High Performance Preconditioners}
......
thesis/thesis.bcf
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thesis/thesis.blg
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thesis/thesis.log
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thesis/thesis.out
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\BOOKMARK [2][-]{subsection.2.2}{Solver}{section.2}% 4
\BOOKMARK [2][-]{subsection.2.3}{Limitations}{section.2}% 5
\BOOKMARK [2][-]{subsection.2.4}{Results}{section.2}% 6
\BOOKMARK [1][-]{appendix.A}{PETSc Installation}{}% 7
\BOOKMARK [1][-]{section.3}{Parallel Network Generation Algorithm}{}% 7
\BOOKMARK [2][-]{subsection.3.1}{Cell-Lists}{section.3}% 8
\BOOKMARK [1][-]{appendix.A}{PETSc Installation}{}% 9
thesis/thesis.pdf
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</requires>
<requires type="editable">
<file>bib/RandomGenerationFlowNetworks.bib</file>
<file>bib/netflow.bib</file>
<file>bib/petsc.bib</file>
<file>bib/hypre.bib</file>
</requires>
......
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thesis/thesis.tex
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......@@ -14,6 +14,7 @@
% references
\addbibresource{bib/RandomGenerationFlowNetworks.bib}
\addbibresource{bib/netflow.bib}
\addbibresource{bib/petsc.bib}
\addbibresource{bib/hypre.bib}
......@@ -40,12 +41,12 @@
\newpage
\begin{center} \Large{\textbf{Abstract}} \end{center}
\hspace{0.5cm}To study the flow properties of large void-space geometries found in porous media such as f.ex. soil or gravel, \cite{MEYER2021103936} describes and implements routines for the generation \& simulation of flow networks representing the complicated pore-arrangement. Based on a relatively small base network acquired via tomographic scans, the generated flow network is of intermediate size (millions of pores). To extend this procedure to even larger networks (up to 100 millions of pores), parallel computing is employed for both generation of pore-networks as well as solving the flow for said networks. In the latter, we rely on existing MPI-based parallel solvers from the PETSc \cite{petsc-web-page} toolkit. See ~\ref{appendix:install} for installation details.
\hspace{0.5cm}To study the flow properties of large void-space geometries found in porous media such as f.ex. soil or gravel, \cite{MEYER2021103936} describes and implements routines for the generation \& simulation of flow networks in the Python library \emph{netflow} \cite{MEYER2021101592}. Based on a relatively small base network acquired via tomographic scans, the generated flow network is of intermediate size (millions of pores). To extend this procedure to even larger networks (up to 100 millions of pores), parallel computing is employed for both generation of pore-networks as well as solving the flow for said networks. In the latter, we rely on existing MPI-based parallel solvers from the PETSc \cite{petsc-web-page} toolkit. See ~\ref{appendix:install} for installation details.
\vspace{5ex}
\begin{multicols}{2}
\section{Introduction}
\hspace{0.5cm}Porous media are abundant in nature. Various types of soils harbor intricate networks that enable the flow of groundwater and subsequently the transport of important chemical compounds through the soil. To understand these natural phenomena, it is of key interest to study the flow through pore networks. However, this requires sufficiently large void-space geometries taken from porous bodies, which despite advances in scanning technologies, is infeasible. Additionally, in pursuit of simulation efficiency, the 3D images obtained from scans are converted into a simplified representation consisting of spherical pores (nodes), connected by cylindrical throats (edges). To overcome above size limitation, the paper from \cite{MEYER2021103936} outlines a procedure for generating random realizations of much larger flow networks, taking an existing base network, obtained from a scan of a porous medium, as input. Furthermore, this method is particularly useful for generating heterogeneous networks, characterized by an irregular pore distribution. This is done with dendrogram-based clustering of pores in the original network, followed by random rotations of said clusters. When performed repeatedly and arranged in a 3-dimensional grid of perturbed base network copies, the resulting network, carrying over pore statistics such as coordination number \& radius, preserves the irregular pore structure of the original network. The \emph{netflow} package that implements the mentioned algorithm also provides functionality for solving and analyzing the flow through such networks. The former of which is now to be parallelized in a distributed fashion, so that it may support larger networks with up to 100 millions of pores. In a further step, the network generation algorithm shall be complemented by a parallel shared memory approach, specifically for connecting the generated pores in the resulting network. This is done directly in Python using the \verb|multiprocessing| library.
\hspace{0.5cm}Porous media are abundant in nature. Various types of soils harbor intricate networks that enable the flow of groundwater and subsequently the transport of important chemical compounds through the soil. To understand these natural phenomena, it is of key interest to study the flow through pore networks. However, this requires sufficiently large void-space geometries taken from porous bodies, which despite advances in scanning technologies, is infeasible. Additionally, in pursuit of simulation efficiency, the 3D images obtained from scans are converted into a simplified representation consisting of spherical pores (nodes), connected by cylindrical throats (edges). To overcome above size limitation, the paper from \cite{MEYER2021103936} outlines a procedure for generating random realizations of much larger flow networks, taking an existing base network, obtained from a scan of a porous medium, as input. Furthermore, this method is particularly useful for generating heterogeneous networks, characterized by an irregular pore distribution. This is done with dendrogram-based clustering of pores in the original network, followed by random rotations of said clusters. When performed repeatedly and arranged in a 3-dimensional grid of perturbed base network copies, the resulting network, carrying over pore statistics such as coordination number \& radius, preserves the irregular pore structure of the original network. The \emph{netflow} package that implements the mentioned algorithm also provides functionality for solving and analyzing the flow through such networks. The former of which is now to be parallelized in a distributed fashion, so that it may support larger networks with up to 100 millions of pores. In a further step, the network generation algorithm shall be complemented by a parallel shared memory approach, specifically for connecting the generated pores in the resulting network.
\section{Parallel Flow Solver}
\subsection{PETSc Interface}
......@@ -66,6 +67,13 @@
\label{fig:balance}
\end{figure}
\begin{multicols}{2}
\section{Parallel Network Generation Algorithm}
\hspace{0.5cm} The existing serial dendrogram-based network generation algorithm, as presented in \cite{MEYER2021101592}, is now modified. Concretely, to allow connecting all pores that populate the larger, generated network domain in parallel, we rely on a shared memory approach via the \verb|multiprocessing| Python module. To accomplish this, we first shifted our attention to \emph{cell lists}, which offer a direct application of the already existing \textbf{maximal throat length} parameter $L_{\mathrm{m}}$ as a suitable \textbf{cell size}.
\subsection{Cell-Lists}
\hspace{0.5cm} To speed up neighbor search for the \emph{stationary} pores, we have opted to use the well-known cell lists data structure instead of the triangulation based approach outlined in \cite{MEYER2021103936}. This choice was supported by the suitable properties of cell lists for our purpose of generating a network of similar pore-arrangement. It is also favorable over the triangulation method as the cell lists are only initialized once, and pores that have already been processed by the algorithm can be removed efficiently. Primarily however, it allows a straight-forward application of parallel computing, by distributing the work needed to find the neighbors of individual pores evenly.
\end{multicols}
\newpage
\appendix
......
visualize/visualize_cells.py 0 → 100644
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import sys
sys.path.append('../')
from netflow import *
import open3d as o3d
import numpy as np
from typing import List
def coord_transform(pos: List[float], lb: float, ub: float):
transform = lambda x: 10. / (ub - lb) * (2. * x - ub - lb)
return np.array(list(map(transform, pos)))
def is_inside(cellList, triplet: List[int]):
return all([(1 <= triplet[i] and triplet[i] < cellList.nCells[i] - 1) \
for i in range(len(triplet))])
def main():
dendro = netflow.load_network_from("network/dendro.h5")
targetsize = [1, 1, 1]
d = len(targetsize)
# size of new network
L = list(map(lambda lb,ub,i: (ub-lb)*i, dendro.lb, dendro.ub, targetsize))
# Domain size including periodic buffer layers on all sides
trueDomainSize = [L[i] + 2 * dendro.Lmax for i in range(d)]
# Initialize cell-list; Place each pore in respective cell-set
cellList = netflow.CellList(dendro.pores, trueDomainSize, dendro.Lmax, dendro, len(dendro.pores))
# Setup GUI
vis = o3d.visualization.VisualizerWithKeyCallback()
vis.create_window()
# Add default primitives
coordinate_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(
size=0.1, origin=[0, 0, 0]
)
vis.add_geometry(coordinate_frame) # coordinate frame
aabb = o3d.geometry.AxisAlignedBoundingBox(
min_bound=-10 * np.ones(3), max_bound=10 * np.ones(3)
)
aabb.color = [0.7, 0.7, 0.7]
vis.add_geometry(aabb) # bounding box
# Colors
colors = [
np.array([0.8, 0.0, 0.0]),
np.array([0.0, 0.8, 0.0]),
np.array([0.0, 0.0, 0.8]),
np.array([0.8, 0.8, 0.0]),
np.array([0.0, 0.8, 0.8]),
np.array([0.8, 0.0, 0.8]),
np.array([0.5, 0.3, 0.2]),
np.array([0.3, 0.1, 0.5])
]
color_dict = {}
running_idx = 0
n_zero_radius = 0
for i, pore in enumerate(dendro.pores):
radius = .6e4 * pore.r
if pore.r < 1e-6:
n_zero_radius += 1
continue
# Add sphere
sphere = o3d.geometry.TriangleMesh.create_sphere(radius=radius, resolution=10)
"""
if pore.label == netflow.LABELS[0]:
sphere.paint_uniform_color(black)
elif pore.label[:2] == 'in':
sphere.paint_uniform_color(red)
elif pore.label[:3] == 'out':
sphere.paint_uniform_color(blue)
else:
sphere.paint_uniform_color(magenta)
"""
cellIdxTrip = cellList.pore_to_triplet(pore)
if (is_inside(cellList, cellIdxTrip)):
cellIdx = cellList.flatten(*cellIdxTrip)
if cellIdx in color_dict:
idx = color_dict[cellIdx]
else:
idx = running_idx
color_dict[cellIdx] = idx
running_idx += 1
color = colors[idx]
else:
color = np.array([0.0, 0.0, 0.0])
sphere.paint_uniform_color(color)
pos = coord_transform(pore.pos, dendro.lb[0], dendro.ub[0])
sphere.translate(pos)
vis.add_geometry(sphere)
print("Did not render %d/%d pores because of too small radius" % (n_zero_radius, len(dendro.pores)))
while True:
if not vis.poll_events():
break
vis.update_renderer()
if __name__ == '__main__':
main()
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