Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
McAnally, H. William
Committee Member
Kelley, C. Tim
Committee Member
Martin, L. James
Committee Member
Berger, C. Rutherford
Committee Member
Howington, E. Stacy
Date of Degree
5-1-2010
Document Type
Dissertation - Open Access
Major
Civil Engineering
Degree Name
Doctor of Philosophy (Ph.D)
College
James Worth Bagley College of Engineering
Department
Department of Civil and Environmental Engineering
Abstract
A suite of tools to reduce the computational effort in groundwater modeling validation and testing has been developed. The work herein explores reduction of computational effort via smart adaptivemeshing, optimization techniques, which require fewer model calls, and the development of surrogate models. Adaptive meshing reduces the computational domain by allowing for mesh refinement in areas of interest determined dynamically by the model through error indicators instead of requiring a priori knowledge or a posteriori determination and rebuilding of the computational domain. As the areas of interest change with the physics, the refinement is removed to lower computational time by using unrefinement. The computational time for dynamic mesh adaption versus uniform refinement is orders of magnitudes smaller. Further reduction in computational time may be required especially when using parameter estimation techniques that require on the order of 2n computations, where n is the number of parameters being estimated. A demonstration of the usefulness of parameter estimation techniques is given, followed by a discussion of methods to further reduce computational time. It may also be necessary to look at reduced physics-type methods to further reduce computational time for the physics-based model. Surrogate models, such as proper orthogonal decomposition (POD), greatly reduce the computational time while maintaining the most important aspects of the physics being solved. The idea here is to run the full model, create the PODs basis, then use this basis to run parameter estimation. Once a better fit has been determined, the full model is run again to capture the full-physics results. The technique is repeated as necessary to capture the “best” parameters to numerically represent the observed behavior.
URI
https://hdl.handle.net/11668/15024
Recommended Citation
Pettway, Jacqueline, "Development of an integrated suite of methods to reduce computational effort in groundwater modeling validation and testing" (2010). Theses and Dissertations. 1444.
https://scholarsjunction.msstate.edu/td/1444