Flow estimation solely from image data through persistent homology analysis

Anna Suzuki, Miyuki Miyazawa, James M. Minto, Takeshi Tsuji, Ippei Obayashi, Yasuaki Hiraoka, Takatoshi Ito

Research output: Contribution to journalArticlepeer-review

Abstract

Abstract: Topological data analysis is an emerging concept of data analysis for characterizing shapes. A state-of-the-art tool in topological data analysis is persistent homology, which is expected to summarize quantified topological and geometric features. Although persistent homology is useful for revealing the topological and geometric information, it is difficult to interpret the parameters of persistent homology themselves and difficult to directly relate the parameters to physical properties. In this study, we focus on connectivity and apertures of flow channels detected from persistent homology analysis. We propose a method to estimate permeability in fracture networks from parameters of persistent homology. Synthetic 3D fracture network patterns and their direct flow simulations are used for the validation. The results suggest that the persistent homology can estimate fluid flow in fracture network based on the image data. This method can easily derive the flow phenomena based on the information of the structure.
Original languageEnglish
Article number17948
Number of pages13
JournalScientific Reports
Volume11
Issue number1
DOIs
Publication statusPublished - 9 Sep 2021

Keywords

  • fluid flow
  • persistent homology
  • fracture networks
  • porous media
  • image analysis
  • OpenFOAM

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