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Medicago sativa (Alfalfa) is an important agricultural plant for animal forage and nitrogen fixation, and has potential value in ligno-cellulosic energy production. In the quest to understand the plant, I generated a draft genome sequence of M. sativa via two reduced representation sequencing approaches: methylation-dependent filtration, and high CoT filtration. Libraries created from each approach were sequenced on an Illumina next-generation sequencing platform yielding approximately 2.5Gb of raw data. A combination of reference-based genome assembly approaches using the closely related species, Medicago truncatula as a reference, and de novo genome assembly approaches were performed to assemble the draft genome. The reference-based assembly generated 312,011 contigs with weighted median contig length (N50) of 247 bases, whereas de novo assembly produced 547,304 contigs with N50 of 275 bases. The creation of the M. sativa draft genome is vital for downstream functional analyses such as genome wide gene mining and gene expression profiling.

Medicago sativa (Alfalfa) is an important agricultural plant for animal forage and nitrogen fixation, and has potential value in ligno-cellulosic energy production. In the quest to understand the plant, I generated a draft genome sequence of M. sativa via two reduced representation sequencing approaches: methylation-dependent filtration, and high CoT filtration. Libraries created from each approach were sequenced on an Illumina next-generation sequencing platform yielding approximately 2.5Gb of raw data. A combination of reference-based genome assembly approaches using the closely related species, Medicago truncatula as a reference, and de novo genome assembly approaches were performed to assemble the draft genome. The reference-based assembly generated 312,011 contigs with weighted median contig length (N50) of 247 bases, whereas de novo assembly produced 547,304 contigs with N50 of 275 bases. The creation of the M. sativa draft genome is vital for downstream functional analyses such as genome wide gene mining and gene expression profiling.

Reservoir simulation is an important tool for decision making and field development management. It enables reservoir engineers to predict reservoir production performance, update an existing model to reproduce monitoring data, assess alternative field development scenarios and design robust production optimization strategies by taking into account the existing uncertainties. A big obstacle in automating model calibration and production optimization approaches is the massive computation required to predict the response of real reservoirs under proposed changes in the model inputs. To speed up reservoir response predictions without compromising accuracy, fast surrogate models have been proposed. These models are either derived by preserving the physics of the involved processes (e.g. mass balance equations) to provide reliable long-range predictions or are developed based solely on statistical relations, in which case they can only provide short-range predictions due to the absence of the physical processes that govern the long-term behavior of the reservoir. We present an alternative solution that combines the advantages of both statistics-based and physics-based methods by deriving the flow predictions in complex two-dimensional models from one-dimensional flow network models. The existing injection/production wells in the original model form the nodes or vertices of the flow network. Each pair of wells (nodes) in the flow network is connected using a one-dimensional numerical simulation model; hence, the entire reservoir is reduced to a connected network of one-dimensional simulation models where the coupling between the individual one-dimensional models is enforced at the nodes where network edges intersect. The proposed flow network model provides a useful and fast tool for characterizing inter-well connectivity, estimating drainage volume between each pair of wells, and predicting reservoir production over an extended period of time for optimization purposes. We estimate the parameters of the flow network model using a robust training approach to ensure that the flow network model reproduces the response of the original full model under a wide range of development strategies. This step helps preserve the flow network model's predictive power during the production optimization when development strategies can change at different iterations. The robust networks training and the subsequent production optimization iterations are computationally efficient as they are performed with the faster flow network model. We demonstrate the effectiveness and applicability of our proposed flow network modeling approach to rapid production optimization using two-phase waterflooding simulations in synthetic and benchmark models.

Many reaction mechanisms have been developed over the past few decades to predict flame characteristics. A detailed reaction mechanism can predict flame characteristics well, but at a high computational cost. The reason for reducing reaction mechanisms is to reduce the computational time needed to simulate a problem. The focus of this work is on the validity of reduced methane-air combustion mechanisms, particularly pertaining to satisfying the entropy inequality. While much of this work involves a two-step reaction mechanism developed by Dr. Charles Westbrook and Dr. Frederick Dryer, some consideration is given to the four-step and three-step mechanisms of Dr. Norbert Peters. These mechanisms are used to simulate the Flame A experiment from Sandia National Laboratories. The two-step mechanism of Westbrook and Dryer is found to generate results that violate the entropy inequality. Modifications are made to the two-step mechanism simulation in an effort to reduce these violations. Two new mechanisms, Mech 1 and Mech 2, are developed from the original two-step reaction mechanism by modifying the empirical data constants in the Arrhenius reaction form. The reaction exponents are set to the stoichiometric coefficients of the reaction, and the concentrations computed from a one-dimensional flame simulation are matched by changing the Arrhenius parameters. The new mechanisms match experimental data more closely than the original two-step mechanism and result in a significant reduction in entropy inequality violations. The solution from Mech 1 had only 9 cells that violated the entropy inequality, while the original two-step mechanism of Westbrook and Dryer had 22,016 cells that violated the entropy inequality. The solution from Mech 2 did not have entropy inequality violations. The method used herein for developing the new mechanisms can be applied to more complex reaction mechanisms.

Thesis (Ph.D.)--Tufts University, 2001. / Adviser: Chris Rogers. Submitted to the Dept. of Mechanical Engineering. Includes bibliographical references (leaves 140-146). Access restricted to members of the Tufts University community. Also available via the World Wide Web;

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: fringe projection; condensation; reduced gravity; optical measurement. Includes bibliographical references. (p.125-126)

Thesis (Ph. D.)--Drexel University, 2005. / Includes abstract and vita. Includes bibliographical references (leaves 132-137).

abstract: A least total area of triangle method was proposed by Teissier (1948) for fitting a straight line to data from a pair of variables without treating either variable as the dependent variable while allowing each of the variables to have measurement errors. This method is commonly called Reduced Major Axis (RMA) regression and is often used instead of Ordinary Least Squares (OLS) regression. Results for confidence intervals, hypothesis testing and asymptotic distributions of coefficient estimates in the bivariate case are reviewed. A generalization of RMA to more than two variables for fitting a plane to data is obtained by minimizing the sum of a function of the volumes obtained by drawing, from each data point, lines parallel to each coordinate axis to the fitted plane (Draper and Yang 1997; Goodman and Tofallis 2003). Generalized RMA results for the multivariate case obtained by Draper and Yang (1997) are reviewed and some investigations of multivariate RMA are given. A linear model is proposed that does not specify a dependent variable and allows for errors in the measurement of each variable. Coefficients in the model are estimated by minimization of the function of the volumes previously mentioned. Methods for obtaining coefficient estimates are discussed and simulations are used to investigate the distribution of coefficient estimates. The effects of sample size, sampling error and correlation among variables on the estimates are studied. Bootstrap methods are used to obtain confidence intervals for model coefficients. Residual analysis is considered for assessing model assumptions. Outlier and influential case diagnostics are developed and a forward selection method is proposed for subset selection of model variables. A real data example is provided that uses the methods developed. Topics for further research are discussed. / Dissertation/Thesis / Ph.D. Statistics 2012