Stress-Electromagnetic Radiation (EMR) Numerical Model and EMR Evolution Law of Composite Coal-Rock under Load

荷载作用下复合煤岩应力-电磁辐射(EMR)数值模型及EMR演化规律

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Abstract

There is a close relationship between the electromagnetic radiation (EMR) evolution and the stress state during loading of composite coal-rock. In this research, the coal-rock EMR generation mechanism was studied and the stress-EMR numerical model was established. Finite element simulation and experiments were then used to verify their correctness, and EMR characteristics, evolution law, and the corresponding relationship between EMR and coal-rock state were studied in depth. The results show that the deformation cycle of "load compression-deformation release-load compression" occurs at coal-rock internal fractures, which together with friction make the formation of coal-rock alternating weak current sources, resulting in the EMR. In addition, the fracture structure is similar to capacitors with time-varying electric quantity and plate spacing. When the fracture is loaded, it will generate approximately sinusoidal EMR pulses whose amplitude is positively correlated with the degree of coal-rock damage. EMR will be exponentially attenuated and distorted at the medium junction when propagating, which does not affect signal characteristics. Meanwhile, EMR quality within 1.0-2.5 mm outside coal-rock is high, whose change is almost synchronous with source. EMR evolution has stages during loading, whose characteristics are different in each stress stage: In compaction and elastic stages, EMR remains stable for most of the time except for the abrupt change of 1-3 mV/m at the junction. In the yield, coal-rock transitions from elastic to plastic, and both EMR and stress increase rapidly as fracture expands. In the fracture stage, EMR maintains high and produces a peak that is synchronous with the stress. After fracture, they drop and recover to stability. The research results will help improve the basic theory of coal and rock dynamic disasters and provide support for its prediction with multi-information fusion, which will help reduce the adverse impact of coal mine disasters on people's lives and property.

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