Analysis of subsurface contaminant migration and remediation using high performance computing |
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| Institution: | 1. Geosciences and Environmental Technologies Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA;2. Center for Applied Scientific Computation, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA;1. Institute of Environmental Engineering Polish Academy of Sciences, 34 M. Sk?odowskiej-Curie St., 41-819, Zabrze, Poland;2. Department of Ecology and Silviculture, Faculty of Forestry, University of Agriculture, Al. 29 Listopada 46, 31-425, Cracow, Poland;1. Department of Energy, Politecnico di Torino, Corso Duca degli Abruzzi, 24 - 10129 Torino, Italy;2. Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Saluggia Research Centre, Strada per Crescentino, 41 - 13040 Saluggia (VC), Italy;1. Université de Toulouse; INPT, UPS; EcoLab (Laboratoire écologie fonctionnelle et environnement), Ecole Nationale Supérieure Agronomique de Toulouse (ENSAT), Castanet Tolosan, France;2. CNRS; EcoLab (Laboratoire écologie fonctionnelle et environnement), Castanet Tolosan, France;1. State Key Laboratory of Phytochemistry and Plant Resources in West China (CUHK), Institute of Chinese Medicine, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China;2. School of Life Sciences, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China;1. State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, Hubei, PR China;2. School of Environmental Studies, China University of Geosciences, Wuhan 430074, Hubei, PR China |
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| Abstract: | Highly resolved simulations of groundwater flow, chemical migration and contaminant recovery processes are used to test the applicability of stochastic models of flow and transport in a typical field setting. A simulation domain encompassing a portion of the upper saturated aquifer materials beneath the Lawrence Livermore National Laboratory was developed to hierarchically represent known hydrostratigraphic units and more detailed stochastic representations of geologic heterogeneity within them. Within each unit, Gaussian random field models were used to represent hydraulic conductivity variation, as parameterized from well test data and geologic interpretation of spatial variability. Groundwater flow, transport and remedial extraction of two hypothetical contaminants were made in six different statistical realizations of the system. The effective flow and transport behavior observed in the simulations compared reasonably with the predictions of stochastic theories based upon the Gaussian models, even though more exacting comparisons were prevented by inherent nonidealities of the geologic model and flow system. More importantly, however, biases and limitations in the hydraulic data appear to have reduced the applicability of the Gaussian representations and clouded the utility of the simulations and effective behavior based upon them. This suggests a need for better and unbiased methods for delineating the spatial distribution and structure of geologic materials and hydraulic properties in field systems. High performance computing can be of critical importance in these endeavors, especially with respect to resolving transport processes within highly variable media.©1998 Elsevier Science Limited. All rights reserved |
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