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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks Volume 18
Editor
Cihan H. Dagli
Cihan H. Dagli
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ISBN-10:
0791802823
ISBN:
9780791802823
No. of Pages:
700
Publisher:
ASME Press
Publication date:
2008

The heterogeneous nature of porous media presents serious challenges in accurately predicting solute transport through earth materials. Spatial analysis techniques such as geostatistics provide a quantitative tool to evaluate soil pore heterogeneity at a variety of scales. Since the CT method can provide spatially detailed data for solute transport parameters such as pore-water velocity and dispersivity, these detailed data may be evaluated using geostatistical analysis tools. The objective of this study was to determine the macropore-scale spatial semivariograms of CT-measured porosity, pore-water velocity, and dispersivity in cores. CT-measured breakthrough curve experiments were conducted in columns of glass beads (1.4 to 8.0 mm diam.). The exponential semivariogram model provided the best fit for the semivariograms of CT-measured porosity, pore-water velocity, and dispersivity for the cores. These three solute transport parameters were found to be autocorrelated with varying ranges of autocorrelation. Range of spatial autocorrelation for porosity, pore-water velocity and dispersivity was found to be a linear function of bead diameter (r values > 0.89). Ranges were found to be approximately 55% of the bead diameter size. This study illustrates the ability to utilize spatial semivariograms for quantifying the autocorrelation of CT-measured transport properties, information which is very useful in transport models.

Abstract
Introduction
Materials and Methods
Results and Discussion
Summary
References
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