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利用离散裂隙网络模型估算洞穴矿山裂隙岩体中的氡扩散张量。

Estimation of radon diffusivity tensor for fractured rocks in cave mines using a discrete fracture network model.

作者信息

Ajayi K M, Shahbazi K, Tukkaraja P, Katzenstein K

机构信息

South Dakota School of Mines and Technology, SDSMT, Rapid City, SD, 57701, USA.

SDSMT, Rapid City, SD, 57701, USA.

出版信息

J Environ Radioact. 2019 Jan;196:104-112. doi: 10.1016/j.jenvrad.2018.11.003. Epub 2018 Nov 14.

Abstract

This study develops a numerical model for predicting radon effective diffusivity tensor for fractured rocks using a two dimensional discrete fracture network (DFN) model. This is motivated by the limitations of existing techniques in predicting the radon diffusion coefficient for the fractured zones of cave mines. These limitations include access to the fractured zones for the purpose of conducting field studies as well as replication of the degree of fracturing in these zones for laboratory studies. The caving of a rock mass involves the fracturing and breaking of intact and naturally fractured rock, which creates migration pathways for radon gas trapped within uranium-rich rock. Therefore, this study develops a stochastic DFN model with equations derived from radon transport to predict diffusivity. Our simulation results reveal the establishment of a representative elementary volume (REV) for diffusivity tensor; approximately equal principal and cross diffusivity magnitudes for each of the DFN domain; a range of diffusivity with porosity (calculated based on the fractures in the domain); and a significant effect of fracture density on diffusivity tensor. These results are essential in developing proactive measures for mitigation of radon gas in cave mines.

摘要

本研究利用二维离散裂隙网络(DFN)模型开发了一种数值模型,用于预测裂隙岩石中的氡有效扩散率张量。这是受现有技术在预测洞穴矿山裂隙带氡扩散系数方面的局限性所推动。这些局限性包括难以进入裂隙带进行实地研究,以及在实验室研究中难以复制这些区域的裂隙程度。岩体的崩落涉及完整岩石和天然裂隙岩石的破裂,这为困在富铀岩石中的氡气创造了迁移路径。因此,本研究开发了一个随机DFN模型,并从氡输运推导方程来预测扩散率。我们的模拟结果揭示了扩散率张量代表性体积单元(REV)的建立;DFN域中每个区域的主扩散率和横向扩散率大小大致相等;扩散率随孔隙率的变化范围(基于域中的裂隙计算);以及裂隙密度对扩散率张量的显著影响。这些结果对于制定洞穴矿山氡气减排的积极措施至关重要。

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