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主办单位:煤炭科学研究总院有限公司、中国煤炭学会学术期刊工作委员会
煤层采动底板变形破坏过程多参数精细感知方法研究
  • Title

    Multi-parameter fine sensing method of deformation and failure process of coal seam mining floor

  • 作者

    姚多喜刘畅

  • Author

    YAO Duoxi;LIU Chang

  • 单位

    安徽理工大学地球与环境学院

  • Organization
    School of Earth and Environment, Anhui University of Science and Technology
  • 摘要

    煤层采动过程中,通过测试参数感知底板变形破坏过程是采场地质保障的有效方式。利用有限差分法构建煤层采动底板破坏数值模型,获取塑性区的分布和演化特征,结果显示底板破坏深度为21 m。在煤层底板钻孔中植入电缆和分布式光纤,通过对电极电流值和光纤应变的连续采集,获得采动效应下的特征参数图谱,进一步分析底板的变形破坏程度。结果表明:电极电流的初始值在40 mA以上。随着工作面的推进电流值轻微升高,当工作面推进到孔口附近时,电流降到1 mA以下,岩层破坏;光纤测试方面,随着工作面的推进光纤应变不断增大,当工作面靠近孔口时,光纤应变峰值为8.589×10−3,之后岩层破裂,能量释放,光纤应变回弹。电极电流和光纤应变参数图谱显示底板的变形破坏过程分为4个阶段,分别为无影响阶段、微影响阶段、显著影响阶段和破坏阶段。监测数据对底板变形破坏过程起到了良好表征作用,但存在一定差异,具体表现在超前应力和破裂伊始的感知上,电极电流的响应要略早于光纤应变。电极电流的结果显示底板破坏深度为20.8 m,光纤应变的结果显示底板破坏深度为21.0 m。构建电极电流值和应变值的核密度图,对于底板浅部的监测点,数据点离散程度较大;而埋深较大的监测点受动效应影响较小,回采过程中数据点分布较为集中,离散程度较小。通过多测试参数联合感知,实现煤层采动底板变形破坏过程的精细表征和评价。

  • Abstract

    Sensing the floor deformation and damage process by testing parameters during coal seam mining is an effective way to secure the geology of the quarry. A numerical model of coal seam mining floor failure was constructed by using the finite difference method to obtain the distribution and evolution characteristics of the plastic zone, which showed a floor failure depth of 21 m. Electric Cables and distributed optical fibers were implanted in the coal floor borehole, and the characteristic parameter profiles under the mining effect were obtained through the continuous acquisition of electrode current values and optical fiber strain values to further analyze the deformation and failure. The result showed that the initial value of electrode current was above 40 mA, and the current value increases slightly as the working face advances, and when the working face advances near the borehole, the current value dropped to less than 1 mA and the rock formation was failure; as for the fiber optic test, the fiber optic strain value increased continuously as the working face advances, and the peak fiber optic strain was 8.589×10−3 when the working face was near the monitoring borehole, after which the rock formation ruptured and the energy was released, and the fiber optic The strain value bounced back after the rock rupture and energy release. The mapping of electrode current and fiber optic strain parameters showed that the deformation and failure process of the floor was divided into four stages, namely, no impact stage, micro impact stage, significant impact stage, and failure stage. The monitoring data provided a good characterization of the deformation and failure process of the floor, but there were some differences, specifically in the perception of the overstress and the beginning of rupture, with the response of the electrode current slightly earlier than the fiber optic strain. The results of the electrode current showed that the floor failure depth was 20.8 m, and the results of the fiber optic strain showed that the floor failure depth was 21 m. The kernel density maps of electrode current values and strain values were constructed. For the monitoring points in the shallow part of the floor, the data points were more discrete; while the monitoring points in the deeper burial were less affected by the dynamic effect, and the data points were more concentrated and less discrete during the recovery process. Through the joint sensing of multiple test parameters, we can realize the fine characterization and evaluation of the deformation and failure process of the coal seam mining floor.

  • 关键词

    煤层采动底板破坏多参数表征精细化探测光纤监测

  • KeyWords

    coal seam mining;coal floor failure;multi-parameter characterization;refined detection;fiber optic monitoring

  • 基金项目(Foundation)
    国家自然科学基金资助项目(41877268)
  • DOI
  • 引用格式
    姚多喜,刘 畅. 煤层采动底板变形破坏过程多参数精细感知方法研究[J]. 煤炭科学技术,2023,51(7):44−52
  • Citation
    YAO Duoxi,LIU Chang. Multi-parameter fine sensing method of deformation and failure process of coal seam mining floor[J]. Coal Science and Technology,2023,51(7):44−52
  • 相关专题
  • 图表
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    • 底板采动数值模型

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