Theses and Dissertations

Issuing Body

Mississippi State University


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


Document Type

Dissertation - Open Access


Civil Engineering

Degree Name

Doctor of Philosophy (Ph.D)


James Worth Bagley College of Engineering


Department of Civil and Environmental Engineering


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.