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Plane Potential Flows01:23

Plane Potential Flows

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Updated: May 25, 2026

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
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Numerical simulation of CO generation and migration patterns in goaf based on coupled multi-physics fields.

Mengxuan Ren1, Yongli Liu2, Bingkun Duan1

  • 1School of Safety Engineering, Heilongjiang University of Science and Technology, Harbin, 150022, China.

Scientific Reports
|May 23, 2026
PubMed
Summary

This study uses numerical simulations to predict goaf spontaneous combustion by tracking carbon monoxide (CO) gas. CO accumulates in specific goaf areas, guiding early warning efforts for mine safety.

Keywords:
Coal spontaneous combustionIndicator gasMulti-physics coupled simulationProgrammed heating experimentThermal buoyancy

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Area of Science:

  • Mining Engineering
  • Chemical Engineering
  • Geological Engineering

Background:

  • Goaf spontaneous combustion poses significant risks in mining operations.
  • Early prediction methods are crucial for preventing catastrophic events.

Purpose of the Study:

  • To investigate the generation and migration of indicator gases for early prediction of goaf spontaneous combustion.
  • To develop a numerical model for analyzing gas evolution and temperature changes.

Main Methods:

  • A multi-physics coupling model integrating flow, temperature, and concentration fields was developed.
  • Numerical simulations analyzed the spatio-temporal evolution of temperature and indicator gases.
  • Programmed heating experiments identified carbon monoxide (CO) as a key indicator gas.

Main Results:

  • Carbon monoxide (CO) showed a strong correlation with temperature at early stages.
  • Thermal buoyancy was identified as the primary driver for CO migration and distribution.
  • CO accumulates in upper, deep, and return side regions of the goaf.

Conclusions:

  • Monitoring specific CO accumulation zones in the goaf is essential for early detection.
  • The findings offer theoretical and engineering support for preventing goaf spontaneous combustion.
  • Effective monitoring strategies can be developed based on predicted CO distribution patterns.