Effects of model resolution on optimal design of subsurface flow and transport problems

Abstract Mathematical modeling of water resources problems is regularly undertaken to aid the management or design of a system. These optimal design problems can require a large number of model simulations with different sets of design variables. In an effort to reduce the computational expense of such design problems, various sources of error are typically introduced into the numerical model simulations, often without explicit consideration of subsequent effects on solution quality. We investigate several sources of such errors, including model formulation, grid resolution, and solver error for a set of community test problems, which have received attention in the literature as a vehicle to evaluate and evolve optimization methods. Results show that common shortcuts used to reduce computational effort can lead to inaccurate solutions that differ quantitatively and qualitatively from optimal solutions found using highly resolved methods. In addition to highlighting what we believe is a common issue, we present improved solutions to the problems considered, identify alternative model and optimization formulations that give equivalent accuracy with reduced computational effort, and provide guidance for improving generally the reliability of optimal design solutions to water resources problems.

[1]  E. Somersalo,et al.  Statistical inverse problems: discretization, model reduction and inverse crimes , 2007 .

[2]  A. Lehikoinen,et al.  Dynamic inversion for hydrological process monitoring with electrical resistance tomography under model uncertainties , 2010 .

[3]  V. E. Henson,et al.  BoomerAMG: a parallel algebraic multigrid solver and preconditioner , 2002 .

[4]  Thomas Hemker,et al.  Framework for Particle Swarm Optimization with Surrogate Functions , 2009 .

[5]  Randall T. Hanson,et al.  User guide for the drawdown-limited, multi-node well (MNW) package for the U.S. Geological Survey's modular three-dimensional finite-difference ground-water flow model, versions MODFLOW-96 and MODFLOW-2000 , 2002 .

[6]  Oskar von Stryk,et al.  A mixed-integer simulation-based optimization approach with surrogate functions in water resources management , 2008 .

[7]  D. W. Peaceman Interpretation of well-block pressures in numerical reservoir simulation with nonsquare grid blocks and anisotropic permeability , 1983 .

[8]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[9]  D. McKinney,et al.  Approximate Mixed‐Integer Nonlinear Programming Methods for Optimal Aquifer Remediation Design , 1995 .

[10]  Lothar Thiele,et al.  Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization , 2003 .

[11]  David P Ahlfeld,et al.  Impact of Simulation Model Solver Performance on Ground Water Management Problems , 2008, Ground water.

[12]  Christopher E. Kees,et al.  Adaptive split-operator methods for modeling transport phenomena in porous medium systems , 2011 .

[13]  Daniel P. Sheer,et al.  Dysfunctional Water Management: Causes and Solutions , 2010 .

[14]  Domenico A Baú,et al.  Optimal design of pump-and-treat systems under uncertain hydraulic conductivity and plume distribution. , 2008, Journal of contaminant hydrology.

[15]  Charles Audet,et al.  Globalization strategies for Mesh Adaptive Direct Search , 2008, Comput. Optim. Appl..

[16]  Arlen W. Harbaugh,et al.  User's documentation for MODFLOW-96, an update to the U.S. Geological Survey modular finite-difference ground-water flow model , 1996 .

[17]  K. R. Fowler,et al.  Approaching the Groundwater Remediation Problem Using Multifidelity Optimization. , 2006 .

[18]  Cass T. Miller,et al.  Solution of a Well-Field Design Problem with Implicit Filtering , 2004 .

[19]  Avi Ostfeld,et al.  State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management , 2010 .

[20]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[21]  Patrick M. Reed,et al.  Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design , 2005 .

[22]  A. Mantoglou Pumping management of coastal aquifers using analytical models of saltwater intrusion , 2003 .

[23]  Arlen W. Harbaugh,et al.  MODFLOW-2000, The U.S. Geological Survey Modular Ground-Water Model - User Guide to Modularization Concepts and the Ground-Water Flow Process , 2000 .

[24]  A. Mayer,et al.  Analysis of the impact of layered soil heterogeneity on optimal policies for groundwater, remediation , 2004 .

[25]  Christopher E. Kees,et al.  A hydraulic capture application for optimal remediation design , 2004 .

[26]  Charles Audet,et al.  Comparison of derivative-free optimization methods for groundwater supply and hydraulic capture community problems , 2008 .

[27]  James R. Craig,et al.  Pump-and-treat optimization using analytic element method flow models , 2006 .

[28]  David P. Ahlfeld,et al.  The impact of numerical precision on the solution of confined and unconfined optimal hydraulic control problems , 1996 .

[29]  A. Cheng,et al.  Pumping optimization in saltwater‐intruded coastal aquifers , 2000 .

[30]  Domenico Baù,et al.  Stochastic management of pump-and-treat strategies using surrogate functions , 2006 .

[31]  J. Bear Dynamics of Fluids in Porous Media , 1975 .

[32]  Wolfgang Bangerth,et al.  Adaptive finite element methods for the solution of inverse problems in optical tomography , 2008 .

[33]  Boris Vexler,et al.  Adaptive Finite Elements for Elliptic Optimization Problems with Control Constraints , 2008, SIAM J. Control. Optim..

[34]  K. Deb An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[35]  E. Haber,et al.  Grid refinement and scaling for distributed parameter estimation problems , 2001 .

[36]  Barbara S. Minsker,et al.  Which Groundwater Remediation Objective is Better: A Realistic One or a Simple One? , 2005 .

[37]  Kalyanmoy Deb,et al.  Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence , 2001, EMO.

[38]  Jari P. Kaipio,et al.  Importance sampling approach for the nonstationary approximation error method , 2010 .

[39]  O. V. Stryk,et al.  DERIVATIVE-FREE OPTIMIZATION METHODS FOR HANDLING FIXED COSTS IN OPTIMAL GROUNDWATER REMEDIATION DESIGN , 2006 .

[40]  Cass T. Miller,et al.  Optimal design for problems involving flow and transport phenomena in saturated subsurface systems , 2002 .

[41]  Stefan M. Wild,et al.  Benchmarking Derivative-Free Optimization Algorithms , 2009, SIAM J. Optim..

[42]  Tamara G. Kolda,et al.  Asynchronous Parallel Pattern Search for Nonlinear Optimization , 2001, SIAM J. Sci. Comput..

[43]  Michal Kuraz,et al.  Solving the Nonstationary Richards Equation With Adaptive hp-FEM , 2011 .

[44]  Barbara S. Minsker,et al.  Effects of Local Search Algorithms on Groundwater Remediation Optimization Using a Self-Adaptive Hybrid Genetic Algorithm , 2006 .

[45]  D. Ahlfeld,et al.  Advective control of groundwater contaminant plumes: Model development and comparison to hydraulic control , 1999 .

[46]  Domenico Baù,et al.  Data-worth analysis for multiobjective optimal design of pump-and-treat remediation systems , 2007 .

[47]  T. Walski,et al.  Self-Adaptive Penalty Approach Compared with Other Constraint-Handling Techniques for Pipeline Optimization , 2005 .