

@mastersthesis {7192,
	title = {Evaluation of uncertainties in {MOFAT} for {Tier} 3 {RBCA}},
	year = {2006},
	note = {NR19621},
	school = {Memorial University of Newfoundland (Canada)},
	type = {phdPh.{D}.},
	address = {Ann Arbor},
	abstract = {The description and characterization of parametric uncertainty in the modeling process is a required component of risk assessment and is of paramount importance for Tier 3 Risk Based Corrective Action (RBCA) of petroleum-contaminated sites. The parametric uncertainty associated with the modeling process is propagated through the risk assessment computations and failure to characterize this uncertainty in multi-phase multicomponent flow and transport modeling outputs results in an inaccurate risk assessment. The primary aim of this study was to improve quantification of parametric uncertainties by developing a comprehensive framework for the uncertainty and sensitivity analysis of a fate and transport model that has wide spread application in Tier 3 RBCA of petroleum contaminated sites. The fate and transport model selected for this study was the U.S. EPA two-dimensional multi-phase multi-component fate and transport model{\textendash}-MOFAT. It is one of the few finite element models available from the U.S. EPA to model multi-phase and multi-component fate and transport. MOFAT was selected after screening Tier 3 RBCA numerical models against four criteria that required the selected model to be a finite element multi-phase multi-component fate and transport model; a public domain code with detailed documentation; peer reviewed and validated; and identified as a Tier 3 RBCA code by the American Society of Testing and Materials. The specific objectives of his research were: (1) to conduct a bench mark parametric uncertainty analysis of MOFAT using Random Sampling based Monte Carlo and to evaluate the applicability and performance of various uncertainty analysis techniques to MOFAT; (2) to develop the computational tools required to undertake an uncertainty analysis of MOFAT; (3) to quantify the uncertainty in estimates of exposure due to variability in input parameters; (4) to evaluate the applicability and performance of various sensitivity analysis techniques to MOFAT; (5) to identify sensitive parameters and issues that need to be addressed when using MOFAT; (6) to use the information gained from the previous five objectives to develop a framework for uncertainty and sensitivity analysis of MOFAT and; (7) to identify areas of additional research so that uncertainty in MOFAT estimates can be better understood and quantified. To ensure that the objectives of this research study were met, a comprehensive parametric uncertainty analysis study was formulated and implemented. The uncertainty analysis techniques evaluated were Random Sampling based Monte Carlo (RS-MC), Monte Carlo with Latin Hypercube sampling (LHS-MC), Monte Carlo using a Response Surface Methodology (RSM-MC) based replacement model and Monte Carlo using a Neural Network (NN-MC) based replacement model. LHS-MC was able to accurately replicate the cumulative distribution function ( cdf ) generated by RS-MC with greater efficiency. RSM-MC and NN-MC were not able to accurately approximate MOFAT outputs for uncertainty analysis. A set of computational tools required to undertake an uncertainty analysis of MOFAT were also developed as a part of this study. A Monte Carlo version of MOFAT called MC-MOFAT was also developed by modifying the original FORTRAN code for MOFAT. A pre-processing FORTRAN tool was developed to allow automated generation of MOFAT input files from text files listing all MC or LHS samples. Similarly a Visual Basic based post-processing tool was developed to automatically extract the results for a specified node and time step from MC-MOFAT output files. An extensive sensitivity analysis of MOFAT was also accomplished. Sensitive parameters were identified through the application of sampling based statistical measures on the set of LHS-MC inputs and outputs. Scatter plots of each of the twelve MOFAT outputs versus the probabilistic input parameters indicated that all the outputs displayed a monotonic dependence on the input parameters; saturated conductivity, Ksw , and the van Genuchten retention parameters, α, and n . A slight dependence between all the outputs and the input parameter, apparent irreducible water saturation, Sm was also apparent. Dependencies between input parameters and MOFAT outputs were also evaluated using four different measures of correlation (Pearson{\textquoteright}s r, Spearman{\textquoteright}s ρ, Partial Correlation Coefficient and Partial Rank Correlation Coefficient). While all the tests do not always concur, in general all the outputs are correlated with Ksw , α, n and to a lesser degree Sm . The variation in correlations as detected by the four different tests is a function of the different testing powers of the tests, complexity of MOFAT and the correlations between the probabilistic input parameters (where Ksw is positively correlated with Sm , α and n. ) Interactions between the input parameters were identified using RSM models. For all phases of Benzene, Toluene, Ethyl benzene, and Xylene, interactions were identified between Ksw and [straight phi] and between [straight phi] and α. For the gas phases of Benzene and Toluene and all phases of Ethyl-benzene and Xylene an additional interaction was identified between K sw and α. To assign priorities to the input variables for reducing uncertainties, MOFAT input variables were ranked on the relative size of the absolute value of the Partial Rank Correlation Coefficients (PRCC). They were also grouped into three categories of decreasing significance based on critical values of the PRCC analysis. Using a new approach developed in this study the input variables were first grouped into four groups on the basis of the critical values of the PRCC and then within each group they were relatively ranked based on the relative size of absolute value of the PRCC. The higher ranked inputs within the lower critical value groups will have the most influence on the calibration and predictions and should be the focus of field investigation and calibration efforts. Drawing on the results of the uncertainty and sensitivity analysis, a comprehensive six-step framework for uncertainty and sensitivity analysis of MOFAT was developed. This study has provided the community of MOFAT users with a comprehensive framework for uncertainty and sensitivity analysis of MOFAT. This framework not only lays out a methodology for uncertainty and sensitivity analysis of MOFAT but it also provides the tools required to conduct such an analysis. Within the context of Tier 3 RBCA the framework and supporting tools developed will make it possible for MOFAT users to conduct comprehensive uncertainty and sensitivity analysis of their simulation scenarios. The framework is also crucial for undertaking any further uncertainty or sensitivity research on MOFAT. Additional areas of research identified in this study to address uncertainty in Tier 3 RBCA fate and transport modeling were: improving the input covariance matrix to better account for input parameter correlations; incorporating component properties as probabilistic inputs in the uncertainty analysis; evaluating the applicability of SRSM-ADIFOR as an uncertainty analysis technique for MOFAT; further study of interactions between [straight phi] and α and between Ksw and α; extending the uncertainty analysis in this study to other Tier 3 RBCA Models; and studying the effect of scaling up of input parameters in the context of uncertainty analysis of MOFAT.* *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Internet browser; Microsoft Office..},
	keywords = {0543:Civil engineering, 0775:Environmental engineering, Applied sciences, Civil engineering, Environmental engineering, MOFAT, Petroleum contamination, Risk-based corrective action, Tier 3 RBCA, Uncertainties},
	url = {http://search.proquest.com/docview/304955593?accountid=14270},
	author = {Khan, Amir Ali}
}
