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Predicting permeability via statistical learning on higher-order microstructural information
RISE Research Institutes of Sweden, Bioeconomy and Health, Agriculture and Food. Chalmers University of Technology, Sweden; University of Gothenburg, Sweden.ORCID iD: 0000-0002-5956-9934
Princeton University, USA.
Princeton Institute for the Science and Technology of Materials, USA; Princeton University, USA .
2020 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 10, no 1, article id 15239Article in journal (Refereed) Published
Abstract [en]

Quantitative structure-property relationships are crucial for the understanding and prediction of the physical properties of complex materials. For fluid flow in porous materials, characterizing the geometry of the pore microstructure facilitates prediction of permeability, a key property that has been extensively studied in material science, geophysics and chemical engineering. In this work, we study the predictability of different structural descriptors via both linear regressions and neural networks. A large data set of 30,000 virtual, porous microstructures of different types, including both granular and continuous solid phases, is created for this end. We compute permeabilities of these structures using the lattice Boltzmann method, and characterize the pore space geometry using one-point correlation functions (porosity, specific surface), two-point surface-surface, surface-void, and void-void correlation functions, as well as the geodesic tortuosity as an implicit descriptor. Then, we study the prediction of the permeability using different combinations of these descriptors. We obtain significant improvements of performance when compared to a Kozeny-Carman regression with only lowest-order descriptors (porosity and specific surface). We find that combining all three two-point correlation functions and tortuosity provides the best prediction of permeability, with the void-void correlation function being the most informative individual descriptor. Moreover, the combination of porosity, specific surface, and geodesic tortuosity provides very good predictive performance. This shows that higher-order correlation functions are extremely useful for forming a general model for predicting physical properties of complex materials. Additionally, our results suggest that artificial neural networks are superior to the more conventional regression methods for establishing quantitative structure-property relationships. We make the data and code used publicly available to facilitate further development of permeability prediction methods.

Place, publisher, year, edition, pages
NLM (Medline) , 2020. Vol. 10, no 1, article id 15239
Keywords [en]
article, artificial neural network, correlation function, geometry, learning, linear regression analysis, physical model, porosity, prediction, quantitative structure property relation, solid
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-48926DOI: 10.1038/s41598-020-72085-5Scopus ID: 2-s2.0-85091192424OAI: oai:DiVA.org:ri-48926DiVA, id: diva2:1472083
Available from: 2020-09-30 Created: 2020-09-30 Last updated: 2023-05-25Bibliographically approved

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Röding, Magnus

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