Groundwater Modeling, Inverse Characterization, and Parallel Computing Kumar Mahinthakumar NC State University

My Background Numerical modeling of groundwater flow and transport – Developed PGREM3D – Parallel Groundwater REMediation model – 3D finite element – GW2D – two dimensional educational models for groundwater flow and transport High Performance Computing – Parallel algorithms, Solvers, Parallel performance analysis Optimization and Inverse modeling – Groundwater source identification – Hydraulic conductivity inversion – Water distribution source identification and leak detection – Population based optimization algorithms (GA, PSO) – Markov Chain Monte Carlo Methods

Groundwater Remediation Modeling using PGREM3D Savannah River Site Investigation 1997

4 Groundwater Source Identification: 3-Source release history reconstruction sampling points Sources C 1 (t), C 2 (t), C 3 (t) are the unknown release histories  flow direction (x1,y1,z1)(x1,y1,z1) (x2,y2,z2)(x2,y2,z2)  C1(t)C1(t) C2(t)C2(t) C3(t)C3(t) 333 m 167 m

5 Plume and Recovered History

6 5-source release history reconstruction

7 RGA-LS results for a 5-source problem

Hydraulic Conductivity Inversion using the Pilot Point Method True K-field Prior Inversion without Regularization Inversion with Regularization

9 Parallel Computing: Multi-level Hybrid GA-LS-FEM framework

Scalability of PSO on ORNL’s Jaguar Supercomputer Jaguar PF: 299,008 AMD Cores Weak Scaling of our PSO Simulation-Optimization framework Showing Over 80% efficiency up to 200,000 cores

WSC Project Tasks Hydrologic Modeling (4.3) – PIHM – Penn-State Integrated Hydrologic Model for groundwater surface water interaction – SWAT-MODFLOW simulations Water Infrastructure Models (4.4) – Groundwater pumping effects (MODFLOW or PGREM3D) – Reservoir model Parallel computing – Ensemble reservoir stream flow calculations