Difference between revisions of "Thomas Zauner"

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{{Person
 
{{Person
|name=Zauner, Thomas
+
|image=placeholder.jpg
|status=PhD student
+
|name=Zauner, Thomas,
|phone=67652
+
|title=Dipl.Phys.
|room=202
+
|status=visiting scientist
 
|email=Thomas.Zauner
 
|email=Thomas.Zauner
 
|category=hilfer
 
|category=hilfer
 +
|phone=63932
 +
|room=1.076
 
}}
 
}}
  
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due to the space between individual grains, is permeable for fluids.
 
due to the space between individual grains, is permeable for fluids.
 
Physical transport phenomena of natural porous media,  
 
Physical transport phenomena of natural porous media,  
such as flow rates, trapping behavior and prediction  
+
such as flow rates, trapping behaviour and prediction  
 
of effective transport coefficients, have always been of great practical  
 
of effective transport coefficients, have always been of great practical  
 
interest for the oil and gas industry and has currently drawn  public
 
interest for the oil and gas industry and has currently drawn  public
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The scientific problems I address in my work are:
 
The scientific problems I address in my work are:
  
*''' Developing algorithms that generate realistic  and macroscopic computer models for granular porous media'''. These models are made of several hundred millions of individual grains whose positions, orientations and sizes  must fulfill certain correlations and constraints. The associated large computational demand requires efficient and highly scalable parallelized algorithms.  
+
*''' Developing algorithms that generate realistic  and macroscopic computer models for granular porous media'''. These models are made of several hundred millions of individual grains whose positions, orientations and sizes  must fulfil certain correlations and constraints. The associated large computational demand requires efficient and highly scalable parallelized algorithms.  
  
 
* '''Identifying and evaluating numerical methods that allow accurate and predictive calculations of physical properties of porous media'''. These calculations typically require numerically solving partial differential equations within a chosen discretization scheme and at a specific resolution or order of accuracy.
 
* '''Identifying and evaluating numerical methods that allow accurate and predictive calculations of physical properties of porous media'''. These calculations typically require numerically solving partial differential equations within a chosen discretization scheme and at a specific resolution or order of accuracy.
Line 33: Line 35:
  
 
== Realistic computer models for granular porous media ==
 
== Realistic computer models for granular porous media ==
In the reconstruction procedure a dense packing of spheres is created. The packing must fulfill given constraints such as density, overlap, size distribution and others. The spheres are later substituted by more complex geometrical  
+
In the reconstruction procedure a dense packing of spheres is created using a '''Monte-Carlo''' or '''Discrete-Element-Simulation'''. The packing must fulfil given constraints such as density, overlap, size distribution and others. The spheres are later substituted by more complex geometrical  
objects determined by the shapes of the grains used in the
+
objects determined by the shapes of the grains used. One option is to use polyhedrons as grain shapes. The resulting model is a continuum model.
specific model. One option is to use polyhedrons as grain shapes.
+
The grain centres, sizes and orientations are continuous in space and the grain shapes are analytical defined. An example of such a model for Fontainebleau sandstone is shown in figure 1. The continuum computer model can then be discretized at any desired resolution (figure 2) for further analysis and computer simulations.
The resulting model then is a continuum model in the sense that the
+
[[Image:all.png |thumb|400px|left| Figure 1: Three dimensional rendering [2] of a model for Fontainebleau sandstone. The model contains over 1.2 million quartz grains. Each grain is modelled as a polyhedron. A crosssection is shown in orange. ]]
geometry is defined by  points in the continuum and  polyhedrons with analytical defined shapes, sizes and orientations. An example of such a model for Fountainebleau sandstone is shown in image 1. Such a continuous computer model can then be discretized at any desired resolution, see image 2for further analysis and computer simulations.
+
[[Image:muCT_ani.gif |thumb|300px|right|Figure 2: Two dimensional crosssections of the discretized model.The sidelength of the crosssection is L and the resolution a. The porespace is black and the grains are gray. At the highest resolution  even narrow channels are resolved by more than 20 lattice nodes. This allows high precision flow simulations.]]
[[Image:all.png |thumb|400px|left| Picture 1: Three dimensional rendering of a model for Fontainebleau sandstone. The model contains over 1.2 million quartz grains. Each grain is modeled as a polyhedron. A crosssection is shown in orange.]]
 
[[Image:muCT_ani.gif |thumb|300px|right|Picture 2: Two dimensional crosssections of the discretized model.The sidelength of the crosssection is L and the resolution a. The porespace is black and the grains are gray. A the highest resolution  narrow channel are solved by more than 20 nodes. ]]
 
 
 
<br>
 
 
 
 
 
  
 
== Flow Simulations  ==
 
== Flow Simulations  ==
A porous mediums '''permeability''' is an example of a  
+
The  '''permeability''' of a porous medium is an example of a  
 
physical transport parameter. It describes the mediums ability to  
 
physical transport parameter. It describes the mediums ability to  
 
transmit fluid flow through it.  Using '''Darcy's law''' the permeability
 
transmit fluid flow through it.  Using '''Darcy's law''' the permeability
of a porous medium can be calculated from the velocity and  
+
of a porous medium is calculated from the velocity and  
corresponding pressure field on the porescale. These fields  
+
corresponding pressure field on the porescale [1,3]. These fields  
are solutions of hydrodynamic partial differential  
+
are solutions of porescale hydrodynamic partial differential  
equations describing fluid flow on the porescale. Many different
+
equations. Different numerical methods that  solve different hydrodynamic
numerical methods that  solve different hydrodynamic equations
+
equations can be used to obtain the velocity and pressure field.
can be used to obtain the velocity and pressure field.
+
Possibilities are '''Navier-Stokes simulations''' and '''Lattice-Boltzmann simulations'''. Our Lattice-Boltzmann implementation, for example, numerically solves the Boltzmann equation and is run until a stationary solution has been reached. From this stationary solution the hydrodynamic fields are calculated.  
Possibilities are '''Navier-Stokes simulations''' and '''Lattice-Boltzmann simulations'''. Our Lattice-Boltzmann implementation, for example, numerically solves the Boltzmann equation and is ran until a stationary solution has been reached. From this stationary solution the hydrodynamic fields can be calculated.  
 
 
Simulations in stochastic geometries often require very large system sizes to obtain representative solutions. In our investigations, typical sizes of the  discretized system are in the range 1024³-4096³ voxel.
 
Simulations in stochastic geometries often require very large system sizes to obtain representative solutions. In our investigations, typical sizes of the  discretized system are in the range 1024³-4096³ voxel.
  
[[Image:poreflow1.jpg|thumb|550px|center| Very small subvolume (128³ voxel) of a sandstone at a resolution 5µm. The porespace-matrix interface is shown as a mesh. The velocity  magnitude in lattice units, as calculated by a Lattice-Boltzmann simulation, is shown within the porespace. Areas of slow flow  are blue, fast flow is shown in red. An empty porespace indicates areas where the fluid is at rest. It can be seen that only a small portion of the porespace contributes to the mass transport. [[Media:poremovie.avi]] ]]
+
[[Image:poreflow1.jpg|thumb|550px|center| Very small subvolume (128³ voxel) of a sandstone at a resolution of 5µm. The porespace-matrix interface is shown as a mesh. The velocity  magnitude in lattice units, as calculated by a Lattice-Boltzmann simulation, is shown within the porespace. Areas of slow flow  are blue, fast flow is shown in red. Empty porespace indicates areas where the fluid is at rest. Only a small portion of the porespace contributes to the mass transport. [[Media:poremovie.avi]] ]]
 
 
  
 
== Publications ==
 
== Publications ==
  
 
[1]  A. Narvaez, T. Zauner, F. Raischel, R. Hilfer, J. Harting, “Quantitative analysis of numerical estimates for the permeability  of porous media from lattice-Boltzmann simulations”, Journal of Statistical Mechanics, P11026, 2010, [http://www.icp.uni-stuttgart.de/~zauner/publications/LB_JStat08.pdf Preprint]
 
[1]  A. Narvaez, T. Zauner, F. Raischel, R. Hilfer, J. Harting, “Quantitative analysis of numerical estimates for the permeability  of porous media from lattice-Boltzmann simulations”, Journal of Statistical Mechanics, P11026, 2010, [http://www.icp.uni-stuttgart.de/~zauner/publications/LB_JStat08.pdf Preprint]
 
  
 
[2] S. Grottel, G. Reina, T. Zauner, R. Hilfer, T. Ertl, "Particle-based Rendering for Porous Media", SIGRAD, 2010, [http://www.icp.uni-stuttgart.de/~zauner/publications/sigrad_2010.pdf Preprint]
 
[2] S. Grottel, G. Reina, T. Zauner, R. Hilfer, T. Ertl, "Particle-based Rendering for Porous Media", SIGRAD, 2010, [http://www.icp.uni-stuttgart.de/~zauner/publications/sigrad_2010.pdf Preprint]
  
 
[3] J. Harting, T. Zauner, R. Weeber, and R. Hilfer
 
[3] J. Harting, T. Zauner, R. Weeber, and R. Hilfer
Numerical modeling of fluid flow in porous media and in driven colloidal suspensions
+
"Numerical modeling of fluid flow in porous media and in driven colloidal suspensions" in High Performance Computing in Science and Engineering '08, edited by W. Nagel, D. Kröner, M. Resch, Springer ,2008. [http://www.icp.uni-stuttgart.de/~zauner/publications/HPC08.pdf Preprint]
in High Performance Computing in Science and Engineering '08, edited by W. Nagel, D. Kröner, M. Resch, Springer ,2008. [http://www.icp.uni-stuttgart.de/~zauner/publications/HPC08.pdf Preprint]
+
 
 +
[4] R. Hilfer and T. Zauner
 +
"High precision synthetic computed tomography
 +
of reconstructed porous media" in Physical Review E, vol.84, p. 062301 (2011)

Latest revision as of 14:24, 30 October 2015

Placeholder.jpg
Dipl.Phys. Thomas Zauner
visiting scientist
Office:1.076
Phone:+49 711 685-63932
Fax:+49 711 685-63658
Email:Thomas.Zauner _at_ icp.uni-stuttgart.de
Address:Dipl.Phys. Thomas Zauner
Institute for Computational Physics
Universität Stuttgart
Allmandring 3
70569 Stuttgart
Germany



Research Overview

Sandstone is a very common natural porous medium. ©wikipedia

The general field of my research is the computational investigations of natural porous media. A typical example of such a natural porous medium is sandstone. It consists of very small consolidated grains that form a solid backbone but, due to the space between individual grains, is permeable for fluids. Physical transport phenomena of natural porous media, such as flow rates, trapping behaviour and prediction of effective transport coefficients, have always been of great practical interest for the oil and gas industry and has currently drawn public attention in the context of CO2 sequestration. Many industrial applications use porous materials. Examples are fuel cells, filtration processes, catalyst in chemical reactions or building materials with special physical properties.


The scientific problems I address in my work are:

  • Developing algorithms that generate realistic and macroscopic computer models for granular porous media. These models are made of several hundred millions of individual grains whose positions, orientations and sizes must fulfil certain correlations and constraints. The associated large computational demand requires efficient and highly scalable parallelized algorithms.
  • Identifying and evaluating numerical methods that allow accurate and predictive calculations of physical properties of porous media. These calculations typically require numerically solving partial differential equations within a chosen discretization scheme and at a specific resolution or order of accuracy.


Realistic computer models for granular porous media

In the reconstruction procedure a dense packing of spheres is created using a Monte-Carlo or Discrete-Element-Simulation. The packing must fulfil given constraints such as density, overlap, size distribution and others. The spheres are later substituted by more complex geometrical objects determined by the shapes of the grains used. One option is to use polyhedrons as grain shapes. The resulting model is a continuum model. The grain centres, sizes and orientations are continuous in space and the grain shapes are analytical defined. An example of such a model for Fontainebleau sandstone is shown in figure 1. The continuum computer model can then be discretized at any desired resolution (figure 2) for further analysis and computer simulations.

Figure 1: Three dimensional rendering [2] of a model for Fontainebleau sandstone. The model contains over 1.2 million quartz grains. Each grain is modelled as a polyhedron. A crosssection is shown in orange.
Figure 2: Two dimensional crosssections of the discretized model.The sidelength of the crosssection is L and the resolution a. The porespace is black and the grains are gray. At the highest resolution even narrow channels are resolved by more than 20 lattice nodes. This allows high precision flow simulations.

Flow Simulations

The permeability of a porous medium is an example of a physical transport parameter. It describes the mediums ability to transmit fluid flow through it. Using Darcy's law the permeability of a porous medium is calculated from the velocity and corresponding pressure field on the porescale [1,3]. These fields are solutions of porescale hydrodynamic partial differential equations. Different numerical methods that solve different hydrodynamic equations can be used to obtain the velocity and pressure field. Possibilities are Navier-Stokes simulations and Lattice-Boltzmann simulations. Our Lattice-Boltzmann implementation, for example, numerically solves the Boltzmann equation and is run until a stationary solution has been reached. From this stationary solution the hydrodynamic fields are calculated. Simulations in stochastic geometries often require very large system sizes to obtain representative solutions. In our investigations, typical sizes of the discretized system are in the range 1024³-4096³ voxel.

Very small subvolume (128³ voxel) of a sandstone at a resolution of 5µm. The porespace-matrix interface is shown as a mesh. The velocity magnitude in lattice units, as calculated by a Lattice-Boltzmann simulation, is shown within the porespace. Areas of slow flow are blue, fast flow is shown in red. Empty porespace indicates areas where the fluid is at rest. Only a small portion of the porespace contributes to the mass transport. Media:poremovie.avi

Publications

[1] A. Narvaez, T. Zauner, F. Raischel, R. Hilfer, J. Harting, “Quantitative analysis of numerical estimates for the permeability of porous media from lattice-Boltzmann simulations”, Journal of Statistical Mechanics, P11026, 2010, Preprint

[2] S. Grottel, G. Reina, T. Zauner, R. Hilfer, T. Ertl, "Particle-based Rendering for Porous Media", SIGRAD, 2010, Preprint

[3] J. Harting, T. Zauner, R. Weeber, and R. Hilfer "Numerical modeling of fluid flow in porous media and in driven colloidal suspensions" in High Performance Computing in Science and Engineering '08, edited by W. Nagel, D. Kröner, M. Resch, Springer ,2008. Preprint

[4] R. Hilfer and T. Zauner "High precision synthetic computed tomography of reconstructed porous media" in Physical Review E, vol.84, p. 062301 (2011)