Tortuosity and Microstructure Effects in Porous Media: Classical Theories, Empirical Data and Modern Methods

ISBN-13: 9783031304798 / Angielski

Lorenz Holzer; Philip Marmet; Mathias Fingerle

Opis

This open access book presents a thorough look at tortuosity and microstructure effects in porous materials. The book delivers a comprehensive review of the subject, summarizing all key results in the field with respect to the underlying theories, empirical data available in the literature, modern methodologies and calculation approaches, and quantitative relationships between microscopic and macroscopic properties. It thoroughly discusses up to 20 different types of tortuosity and introduces a new classification scheme and nomenclature based on direct geometric tortuosities, indirect physics-based tortuosities, and mixed tortuosities (geometric and physics-based). The book also covers recent progress in 3D imaging and image modeling for studying novel aspects of tortuosity and associated transport properties in materials, while providing a comprehensive list of available software packages for practitioners in the community.This book is a must-read for researchers and students in materials science and engineering interested in a deeper understanding of microstructure–property relationships in porous materials. For energy materials in particular, such as lithium-ion batteries, tortuosity is a key microstructural parameter that can greatly impact long-term material performance. Thus, the information laid out in this book will also greatly benefit researchers interested in computational modeling and design of next-generation materials, especially those for sustainability and energy applications.

Spis treści

Abstract

1. Introduction

2. Review of theories and a classification of tortuosity types

2.1 Hydraulic tortuosity

2.1.1 Classical Carman-Kozeny theory

2.1.2 From classical Carman-Kozeny theory to modern characterization of microstructure effects

2.2 Electrical tortuosity

2.3 Diffusional tortuosity

2.4 Direct geometric tortuosity

2.4.1 Skeleton and medial axis tortuosity

2.4.2 Path tracking method tortuosity

2.4.3 Geodesic tortuosity

2.4.4 Fast marching method (FMM) tortuosity

2.4.5 Percolation path tortuosity

2.4.6 Pore centroid tortuosity

2.5 Tortuosity types: classification scheme and nomenclature

2.5.1 Classification scheme

2.5.2 Nomenclature

3. Review of empirical data from literature - tortuosity-porosity relationships

3.1 Empirical data for different types of materials and microstructures

3.2 Empirical data for different types of tortuosity

3.3 Direct comparison of tortuosity types based on selected datasets

3.4 Relative order of tortuosity types

3.5 Tortuosity - porosity relationships in literature

4. Methodologies, workflows and calculation approaches

4.1 3D-imaging / tomography

4.1.1 Overview of 3D imaging methods

4.1.2 X-ray computed tomography (XCT)

4.1.3 FIB-SEM tomography and serial sectioning

4.1.4 Electron Tomography (ET)

4.1.5 Atom Probe Tomography (APT)

4.1.6 Correlative Tomography

4.2 Available software (SW) packages for 3D image processing and modeling

4.2.1 Methodological modules

4.2.2 Different types of SW packages

4.3 From tomography raw data to segmented 3D microstructure model (image processing I)

4.4 Calculation approaches for tortuosity

4.4.1 Calculation approaches and SW for direct geometric tortuosities

4.4.2 Calculation approaches and SW for indirect physics-based tortuosities

4.4.3 Calculation approaches for mixed tortuosities

4.5 Pore scale modeling for tortuosity characterization: examples from literature

4.6 Stochastic microstructure modeling

4.6.1 Stochastic modeling for Digital Materials Design (DMD) of electrochemical devices

4.6.2 Stochastic modeling for Digital Rock Physics (DRP) and Virtual Materials Testing (VMT) of porous media

5. Towards a quantitative understanding of microstructure - property relationships

5.1 Quantitative micro-macro relationships for prediction of conduction and diffusion

5.2 Quantitative micro-macro relationships for prediction of permeability

5.2.1 Kozeny, 1927: Bundle of tubes model

5.2.2 Carman, 1937: Sphere packing model

5.2.3 Katz and Thompson, 1986: Experimental characterization of L_char and M_exp

5.2.4 Holzer et al., 2016: Modern 3D analysis for characterization of L_char  and M_pred

5.2.5 Neumann et al., 2020: Determination of L_char  and M_pred using stochastic geometry and virtual materials testing

6. Summary and Conclusions

References

Appendix

O autorze

Lorenz Holzer is a senior scientist at the Zurich University of Applied Sciences (ZHAW), Switzerland, and head of the research group Microstructure Analysis and Property prediction (MAP). His research focuses on digital materials design (DMD/ICME), which combines methods of imaging/tomography, 3D image analysis, microstructure modeling and multiphysics simulations. These methods serve as a basis for optimization of engineered porous and composite materials (e.g., electrodes of fuel cells and batteries). Lorenz Holzer has published more than 150 scientific papers. He has organized several international conferences on the topics of 3D imaging and microstructure analysis. For his pioneering work in FIB-SEM tomography he received the Purdy award (American Ceramic Society) in 2007.

 

Philip Marmet obtained his Master of Science in Engineering in Industrial Technologies from Bern University of Applied Sciences in 2013 and his Master of Science in Physics from University of Fribourg (Switzerland) in 2016. He is a specialist in modelling and simulation of industrial systems, with strong experience in applied research with partners form academia and private sector. During his PhD thesis (2019 to 2023, Uni Fribourg and ZHAW), Philip Marmet was developing new models for digital materials design of Solid Oxide Fuell Cell electrodes. Since 2023, Philip Marmet is employed as a research associate at the Institute of Computational Physics (ICP), Zurich University of Applied Sciences (ZHAW), Switzerland.

 

Mathias Fingerle received his PhD in experimental physics from TU Kaiserslautern in 2014. He has conducted academic teaching and research in the field of energy materials and led the group Electrochemical Interfaces at the Institute of Surface Science at TU Darmstadt. Presently, as Head of Consulting & Projects at Math2Market GmbH, he is coordinating scientific consulting and application engineering for digital material design mainly in the fields of batteries, fuel cells, digital rock physics, air and oil filtration and composites.

 

Andreas Wiegmann is CEO and founder of Math2Market, an engineering software company that specializes in digital mesoscale materials characterization and materials design. He has a PhD in mathematics from the University of Washington in Seattle and then worked at the University of California at Berkeley, LBNL and Fraunhofer ITWM. He specializes in numerics of interfaces between high contrast materials, stochastic 3D material models, and the analysis of 3D scans and published more than 150 articles in areas ranging from machine learning to computational physics, textile engineering, fuel cell modelling, battery material research, petroleum engineering and structural topology optimization.

 

Matthias Neumann is Post-doc at the Institute of Stochastics of Ulm University (UU), where he received his PhD in 2020. He was awarded with the PhD price of UU and received start-up funding by the Graduate & Professional Training Center Ulm. Since 2021, he is member of the Cluster of Excellence POLiS (Post Lithium Storage). The focus of his research is on microstructure characterization by statistical image analysis and machine learning, the development of stochastic models for generating digital twins, and the quantification of microstructure-property relationships. He published 33 research papers in peer-reviewed international journals and 3 contributions in conference proceedings.

Volker Schmidt is Professor at the Institute of Stochastics of Ulm University. He received his PhD from the Technical University Bergakademie Freiberg. His research activities are focused on spatial stochastic modeling of highly resolved image data in 2D, 3D and 4D, using methods of statistical image processing, machine learning and stochastic geometry. Prof. Schmidt is co-author of three books and more than two hundred research papers in peer-reviewed international journals. He supervised more than one hundred Bachelor and Master students, and 23 PhD students who have completed their dissertation theses so far. Currently, 11 PhD students are preparing their PhD theses under his supervision.