Goal: Model of groundwater contamination under uncertainties in porosity and permeability. Applications: Prediction of availability of pure water resources, ecology and environmental science Modeling of uncertainty: Build a generalized Polynomial Chaos Expansion with coefficients computed on sparse grids. Numerics: We run a highly-parallel multigrid solver, based on ug4, on Shaheen II. Parallelization in spatial and stochastic spaces

1. Goal: Model of groundwater contamination under
uncertainties in porosity and permeability.
Applications: Prediction of availability of pure
water resources, ecology and environmental science
Modeling of uncertainty: Build a generalized
Polynomial Chaos Expansion with coefficients
computed on sparse grids.
Numerics: We run a
highly-parallel multigrid solver,
based on ug4, on Shaheen II.
Parallelization in spatial and stochastic spaces.
Methods and software: build-in MPI based
parallelization, FV spatial discretization, implicit and
explicit in time, Newton for non-linearity, Krylov and
geometric/algebraic multigrids + ILU as a smoother.
Two different scenarios of the salt/pollution propagation.
Under gravity forces salt forms ‘finger’-shaped pattern.
Risk assessment of salt contamina/on of groundwater under
uncertain aquifer proper/es
D. Keyes1, A. Litvinenko2, D. Logashenko1, R. Tempone2, G. WiEum1
1Extreme Computing Research Center, 2Stochastic Numerics group, KAUST
Model (density-driven flow):
Darcy flow,
@t( ⇢c) + r · (⇢cq ⇢Drc) = 0
@t( ⇢) + r · (⇢q) = 0
x 2 ⌦ ⇢ R3
is mass fraction of the salt, p pressure
200 scenarios are computed concurrently.
Every scenario is computed on 32 cores and requires a
mesh with ~8M points and 1500 time steps.
Total number of cores is 200x32=6400.
Future plans: high resolution 3D simulations.
porosity, D permeability,
density, is velocity.
Themeanandthevarianceof
saltconcentration
where c
⇢ = ⇢(c) q = K
µ (rp ⇢g)