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首页> 外文期刊>Journal of Hydrology >Using microscope observations of thin sections to estimate soil permeability with the Kozeny-Carman equation
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Using microscope observations of thin sections to estimate soil permeability with the Kozeny-Carman equation

机译:使用显微镜观察薄切片,用Kozeny-Carman方程估算土壤渗透率

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In this study we used the Kozeny-Carman (K-C) equation as a semi-physical model for estimating the soil permeability using data derived from microscope observations. Specific surface areas and porosities were obtained from two-point correlation functions derived from scanning electron microscope images of thin sections using a magnification of 50 and a resolution of 1.88 mum pixel(-1). Permeabilities were predicted using two published ('Ahuja' and 'Berryman') and one generalized variant of the K-C equation. The latter model was similar to the Berryman variant, but used a free parameter C rather than a porosity dependent formation factor. All K-C model variants were optimized on measured permeabilities. The Ahuja and Berryman KC models performed relatively poorly with R-2 values of 0.36 and 0.57. respectively, while the generalized model attained R-2 values of 0.91. The parameter C was strongly related to texture and, to a lesser extent, particle density. The general model still required measured surface area and porosity. However, we showed that it was possible to estimate these parameters from texture resulting in an R-2 of 0.87. A fully empirical model that did not assume K-C concepts performed slightly worse (R-2 = 0.84). The results indicate that after developing the model using microscope information, only macroscopic data are necessary to predict permeability of soils in a semi-physical manner with the K-C equation. (C) 2001 Elsevier Science BN. All rights reserved. [References: 34]
机译:在这项研究中,我们使用Kozeny-Carman(K-C)方程作为半物理模型,使用从显微镜观察获得的数据估算土壤的渗透性。比表面积和孔隙率是从两部分相关函数获得的,该函数是从薄截面的扫描电子显微镜图像获得的,放大倍数为50,分辨率为1.88 mum pixel(-1)。使用两个已公开的(“ Ahuja”和“ Berryman”)和一个K-C方程的广义变体来预测渗透率。后一种模型与Berryman变体相似,但是使用了自由参数C而不是孔隙度相关的形成因子。所有K-C模型变体都根据测得的渗透率进行了优化。 Ahuja和Berryman KC模型的R-2值分别为0.36和0.57,表现相对较差。广义模型的R-2值为0.91。参数C与质地密切相关,在较小程度上与颗粒密度相关。通用模型仍然需要测量表面积和孔隙率。但是,我们表明可以从纹理估计这些参数,从而使R-2为0.87。没有假设K-C概念的完全经验模型的表现稍差一些(R-2 = 0.84)。结果表明,使用显微镜信息开发模型后,仅需宏观数据即可使用K-C方程以半物理方式预测土壤的渗透性。 (C)2001 Elsevier Science BN。版权所有。 [参考:34]

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