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Predicting permeability via statistical learning on higher-order microstructural information.

Magnus Röding1,2, Zheng Ma3, Salvatore Torquato4

  • 1RISE Research Institutes of Sweden, 41276, Göteborg, Sweden. magnus.roding@ri.se.

Scientific Reports
|September 18, 2020
PubMed
Summary
This summary is machine-generated.

Predicting fluid flow permeability in porous materials is enhanced by using advanced structural descriptors. Combining multiple correlation functions and tortuosity, especially void-void correlation, significantly improves prediction accuracy over simpler models.

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Area of Science:

  • Material Science
  • Geophysics
  • Chemical Engineering

Background:

  • Quantitative structure-property relationships are vital for predicting material properties.
  • Permeability prediction in porous materials is crucial for various scientific and engineering fields.
  • Characterizing pore microstructure geometry is key to understanding fluid flow.

Purpose of the Study:

  • To evaluate the predictability of different structural descriptors for fluid flow permeability.
  • To compare linear regressions and neural networks for quantitative structure-property relationships.
  • To identify the most informative descriptors for permeability prediction.

Main Methods:

  • Generated 30,000 virtual porous microstructures (granular and continuous).
  • Computed permeability using the lattice Boltzmann method.
  • Characterized pore geometry using one-point and two-point correlation functions, and geodesic tortuosity.

Main Results:

  • Higher-order correlation functions and tortuosity significantly improved permeability prediction compared to Kozeny-Carman regression.
  • The combination of all three two-point correlation functions and tortuosity yielded the best predictive performance.
  • The void-void correlation function was the most informative single descriptor; artificial neural networks outperformed linear regressions.

Conclusions:

  • Higher-order correlation functions are essential for general models predicting physical properties of complex materials.
  • Advanced structural descriptors and machine learning models enhance permeability prediction accuracy.
  • Publicly releasing data and code will foster further research in permeability prediction methods.