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Research Progress on Micro/Nanopore Flow Behavior.

Jinbo Yu1,2,3, Meng Du1,2,3, Yapu Zhang2,3

  • 1University of Chinese Academy of Sciences, Beijing 100049, China.

Molecules (Basel, Switzerland)
|May 7, 2025
PubMed
Summary
This summary is machine-generated.

Fluid flow in micro/nanoporous media is complex. Advanced methods reveal unique behaviors crucial for energy applications, though challenges in modeling and replication persist.

Keywords:
artificial intelligenceconfined flow behaviorexperimental researchlattice Boltzmann methodmicroseepagemolecular dynamics simulationnanoporous mediapore-scale modeling

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Area of Science:

  • Geosciences and Energy
  • Physics and Chemistry of Materials

Background:

  • Fluid flow in porous media deviates from continuum theory at micro/nanoscales due to surface forces.
  • Understanding these phenomena is vital for unconventional reservoir recovery and energy technologies.

Purpose of the Study:

  • To review and comparatively analyze experimental, theoretical, and numerical advancements in micro/nanopore fluid flow.
  • To identify key phenomena, persistent challenges, and future research directions in the field.

Main Methods:

  • Review of experimental techniques (micro/nanofluidic devices, high-resolution imaging).
  • Analysis of theoretical models and advanced numerical simulations (MD, LBM, hybrid frameworks).
  • Integration of artificial intelligence (AI) for data interpretation and multiscale modeling.

Main Results:

  • Direct observation of confined flow phenomena (slip flow, phase transitions, non-Darcy behavior).
  • Clarification of scale-dependent flow regimes and complex fluid-solid interactions.
  • AI accelerates data interpretation and enhances predictive modeling capabilities.

Conclusions:

  • Significant progress in understanding micro/nanopore flow, but challenges remain in replicating reservoir conditions and bridging scales.
  • Further research is needed to fully characterize multiphase dynamics and improve multiscale modeling.
  • The review consolidates current knowledge and highlights critical gaps for future research.