HR: 10:20h
AN: H32C-01 INVITED [Abstracts]
TI: Blueprint for Perturbative Solution of Flow and Transport in Strongly Heterogeneous Composite Media
Using Fractal and Variational Multiscale Decomposition
AU: * Neuman, S P
EM: neuman@hwr.arizona.edu
AF: University of Arizona, Department of Hydrology and Water Resources, Tucson, AZ 85721
United States
AB:
This paper offers a blueprint for extending perturbative ensemble moment (and potentially other, e.g. Gaussian or
perturbative Karhunen-Loeve) solutions of stochastic flow and transport problems in composite, randomly heterogeneous porous
media, to cases in which medium heterogeneity is arbitrarily large. The proposed approach is based on a novel idea of
combined fractal and variational multiscale decomposition. It considers log hydraulic conductivity to be a composite random
field with piecewise statistically homogeneous increments characterized by a truncated power variogram, i.e., a truncated
fractal. This consideration is supported either exactly or approximately by a growing amount of observational data. It allows
decomposing the field into two or more mutually uncorrelated components having relatively small variances associated with a
hierarchy of spatial correlation scales. Combining this fractal decomposition with a variational multiscale decomposition
formalism (VMS), motivated by the multiscale nature of turbulence, allows decomposing flow and transport problems into two or
more coupled sets of variational problems each of which is amenable to perturbative solution regardless of how heterogeneous
the original field was. The problems are defined on a hierarchy of computational grids having discretization intervals
proportional to the correlation scales. The proposed approach does not require introducing effective parameters on any scale;
instead, it allows resolving flow and transport ensemble moments fully on multiple scales of spatial resolution. The
approach nevertheless allows subgrid closure (another idea inspired by the literature on turbulence) through subgrid
refinement or the use of effective subgrid parameters. It additionally offers the critically important capability of
conditioning flow and transport analyses on multiscale measurements. Details are presented in the context of steady state
single-phase flow in a composite medium.
DE: 1805 Computational hydrology
DE: 1829 Groundwater hydrology
DE: 1839 Hydrologic scaling
DE: 1869 Stochastic hydrology
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: Fall Meeting 2005