Type of Document	Thesis
Author	Miller, Geoffery Larch
URN	etd-01252011-135335
Title	Parametric Uncertainty Analysis of Uranium Transport Surface Complexation Models
Degree	Master of Science
Department	Scientific Computing, Department of
Advisory Committee	Advisor Name Title Ming Ye Committee Chair Robert van Engelen Committee Member Tomasz Plewa Committee Member
Keywords	Groundwater contamination Hydrology
Date of Defense	2010-11-18
Availability	unrestricted
Abstract Parametric uncertainty analysis of surface complexation modeling (SCM) has been studied using linear and nonlinear analysis. A computational SCM model was developed by Kohler et al. (1996) to simulate the breakthrough of Uranium(VI) in a column of quartz. Calibration of parameters which describe the reactions involved during reactive-transport simulation has been found to fit experimental data well. Further uncertainty analysis has been conducted which determines the predictive capability of these models. It was concluded that nonlinear analysis results in a more accurate prediction interval coverage than linear analysis. An assumption made by both linear and nonlinear analysis is that the parameters follow a normal distribution. In a preliminary study, when using Monte Carlo sampling a uniform distribution among a known feasible parameter range, the model exhibits no predictive capability. Due to high parameter sensitivity, few realizations exhibit accuracy to the known data. This results in a high confidence of the calibrated parameters, but poor understanding of the parametric distributions. This study first calibrates these parameters using a global optimization technique, multi-start quasi-newton BFGS method. Second, a Morris method (MOAT) analysis is used to screen parametric sensitivity. It is seen from MOAT that all parameters exhibit nonlinear effects on the simulation. To achieve an approximation of the simulated behavior of SCM parameters without the assumption of a normal distribution, this study employs the use of a Covariance-Adaptive Monte Carlo Markov chain algorithm. It is seen from posterior distributions generated from accepted parameter sets that the parameters do not necessarily follow a normal distribution. Likelihood surfaces confirm the calibration of the models, but shows that responses to parameters are complex. This complex surface is due to a nonlinear model and high correlations between parameters. The posterior parameter distributions are then used to find prediction intervals about an experiment not used to calibrate the model. The predictive capability of Adaptive MCMC is found to be better than that of linear and non-linear analysis, showing a better understanding of parametric uncertainty than previous study.
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