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基于粒子图像测速技术的采煤沉陷研究模型

Research PIV-based model on subsidence caused by coal mining.

作者信息

Zhang Xingsheng, Liu Zihui, Wang Dubo, Dong Jinyu, Wang Xinjian

机构信息

School of Earth Sciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.

出版信息

Sci Rep. 2024 Nov 1;14(1):26269. doi: 10.1038/s41598-024-78137-4.

Abstract

The subsidence caused by coal mining could cause the destruction of roads and houses, and even the failure of infrastructures. Understanding of the mechanism of coal mining subsidence may provide early protecting to infrastructures on coming failure, but dynamic analysis of subsidence due to coal mining is currently needed. In this study we apply particle image velocimetry (PIV) method to reveal strata movement and subsidence according to the prototype and indoor physical model similarity experiment of Henan. Our result shows magnitude of the subsidence of overlying strata during the coal mining at different excavation thickness, that more coal mining thickness may produce more subsidence, and that shallower coal may cause more significant subsidence. Our result suggests that further PIV test combined with field monitoring data may be an effective measure to study subsidence mechanism and pattern helping to predict disaster caused by subsidence.

摘要

煤炭开采引起的地面沉降可能导致道路和房屋毁坏,甚至造成基础设施失效。了解煤炭开采地面沉降的机制或许能为即将失效的基础设施提供早期保护,但目前需要对煤炭开采引起的沉降进行动态分析。在本研究中,我们应用粒子图像测速(PIV)方法,根据河南的原型和室内物理模型相似性试验来揭示地层移动和沉降情况。我们的结果表明了不同开采厚度下煤炭开采过程中覆岩沉降的大小,即开采厚度越大可能产生的沉降越大,且煤层越浅可能导致的沉降越显著。我们的结果表明,进一步结合现场监测数据进行PIV试验可能是研究沉降机制和模式的有效措施,有助于预测沉降引发的灾害。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98aa/11530644/9c6c429640d7/41598_2024_78137_Fig1_HTML.jpg

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