Propagation of Uncertainties in Density-Driven Flow
For researchers modeling subsurface contamination, this provides a parallelized uncertainty quantification approach, but the contribution is incremental as it applies existing gPC and multigrid methods to a specific problem.
This work presents a parallel method for quantifying uncertainty propagation in density-driven subsurface flow, using a generalized polynomial chaos expansion surrogate model. The method achieves low-cost uncertainty quantification for permeability and porosity in an Elder-like benchmark problem.
Accurate modeling of contamination in subsurface flow and water aquifers is crucial for agriculture and environmental protection. Here, we demonstrate a parallel method to quantify the propagation of the uncertainty in the dispersal of pollution in subsurface flow. Specifically, we consider the density-driven flow and estimate how uncertainty from permeability and porosity propagates to the solution. We take an Elder-like problem as a numerical benchmark and we use random fields to model the limited knowledge on the porosity and permeability. We construct a low-cost generalized polynomial chaos expansion (gPC) surrogate model, where the gPC coefficients are computed by projection on sparse and full tensor grids. We parallelize both the numerical solver for the deterministic problem based on the multigrid method, and the quadrature over the parametric space