The ongoing European Refugee Crisis has been one of the most popular trending topics on Twitter for the past 8 months. This paper applies topic modeling on bulks of tweets to discover the hidden patterns within these social media discussions. In particular, we perform topic analysis through solving Non-negative Matrix Factorization (NMF) as an Inexact Alternating Least Squares problem. We accelerate the computation using techniques including tweet sampling and augmented NMF, compare NMF results with different ranks and visualize the outputs through topic representation and frequency plots. We observe that supportive sentiments maintained a strong presence while negative sentiments such as safety concerns have emerged over time.

During intense rain events a stormwater system can fill rapidly and undergo a transition from open channel flow to pressurized flow. This transition can create large discrete pockets of trapped air in the system. These pockets are pressurized in the horizontal reaches of the system and then are released through vertical vents. In extreme cases, the transition and release of air pockets can create a geyser feature. The current models are inadequate for simulating mixed flows with complicated air-water interactions, such as geysers. Additionally, the simulation of air escaping in the vertical dropshaft is greatly simplified, or completely ignored, in the existing models. In this work a two-phase numerical model solving the Navier-Stokes equations is developed to investigate the key factors that form geysers. A projection method is used to solve the Navier-Stokes Equation. An advanced two-phase flow model, Volume of Fluid (VOF), is implemented in the Navier-Stokes solver to capture and advance the interface. This model has been validated with standard two-phase flow test problems that involve significant interface topology changes, air entrainment and violent free surface motion. The results demonstrate the capability of handling complicated two-phase interactions. The numerical results are compared with experimental data and theoretical solutions. The comparisons consistently show satisfactory performance of the model. The model is applied to a real stormwater system and accurately simulates the pressurization process in a horizontal channel. The two-phase model is applied to simulate air pockets rising and release motion in a vertical riser. The numerical model demonstrates the dominant factors that contribute to geyser formation, including air pocket size, pressurization of main pipe and surcharged state in the vertical riser. It captures the key dynamics of two-phase flow in the vertical riser, consistent with experimental results, suggesting that the code has an excellent potential of extending its use to practical applications.

Parameter estimation techniques have been successfully and extensively applied to deterministic models based on ordinary differential equations but are in early development for stochastic models. In this thesis, we first investigate using parameter estimation techniques for a deterministic model to approximate parameters in a corresponding stochastic model. The basis behind this approach lies in the Kurtz limit theorem which implies that for large populations, the realizations of the stochastic model converge to the deterministic model. We show for two example models that this approach often fails to estimate parameters well when the population size is small. We then develop a new method, the MCR method, which is unique to stochastic models and provides significantly better estimates and smaller confidence intervals for parameter values. Initial analysis of the new MCR method indicates that this method might be a viable method for parameter estimation for continuous time Markov chain models.

Problems involving the minimization of functionals date back to antiquity. The mathematics of the calculus of variations has provided a framework for the analytical solution of a limited class of such problems. This paper describes a numerical approximation technique for obtaining machine solutions to minimal path problems. It is shown that this technique is applicable not only to the common case of finding geodesics on parameterized surfaces in R3, but also to the general case of finding minimal functionals on hypersurfaces in Rn associated with an arbitrary metric.

Molecular Dynamics (MD) is the numerical simulation of a large system of interacting molecules, and one of the key components of a MD simulation is the numerical estimation of the solutions to a system of nonlinear differential equations. Such systems are very sensitive to discretization and round-off error, and correspondingly, standard techniques such as Runge-Kutta methods can lead to poor results. However, MD systems are conservative, which means that we can use Hamiltonian mechanics and symplectic transformations (also known as canonical transformations) in analyzing and approximating solutions. This is standard in MD applications, leading to numerical techniques known as symplectic integrators, and often, these techniques are developed for well-understood Hamiltonian systems such as Hill’s lunar equation. In this presentation, we explore how well symplectic techniques developed for well-understood systems (specifically, Hill’s Lunar equation) address discretization errors in MD systems which fail for one or more reasons.

The task of choosing a preconditioner M to use when solving a linear system Ax=b with iterative methods is often tedious and most methods remain ad-hoc. This thesis presents a randomized algorithm to make this chore less painful through use of randomized algorithms for estimating traces. In particular, we show that the preconditioner stability || I - M-1A ||F, known to forecast preconditioner quality, can be computed in the time it takes to run a constant number of iterations of conjugate gradients through use of sketching methods. This is in spite of folklore which suggests the quantity is impractical to compute, and a proof we give that ensures the quantity could not possibly be approximated in a useful amount of time by a deterministic algorithm. Using our estimator, we provide a method which can provably select a quality preconditioner among n candidates using floating operations commensurate with running about n log(n) steps of the conjugate gradients algorithm. In the absence of such a preconditioner among the candidates, our method can advise the practitioner to use no preconditioner at all. The algorithm is extremely easy to implement and trivially parallelizable, and along the way we provide theoretical improvements to the literature on trace estimation. In empirical experiments, we show the selection method can be quite helpful. For example, it allows us to create to the best of our knowledge the first preconditioning method for kernel regression which never uses more iterations over the non-preconditioned analog in standard settings.

For this project, we use a modified version of a previously developed mathematical model, which describes the relationships among matrix metalloproteinases (MMPs), their tissue inhibitors (TIMPs), and extracellular matrix (ECM). Our ultimate goal is to quantify and understand differences in parameter estimates between patients in order to predict future responses and individualize treatment for each patient. By analyzing parameter confidence intervals and confidence and prediction intervals for the state variables, we develop a parameter space reduction algorithm that results in better future response predictions for each individual patient. Moreover, use of another subset selection method, namely Structured Covariance Analysis, that considers identifiability of parameters, has been included in this work. Furthermore, to estimate parameters more efficiently and accurately, the standard error (SE- )optimal design method is employed, which calculates optimal observation times for clinical data to be collected. Finally, by combining different parameter subset selection methods and an optimal design problem, different cases for both finding optimal time points and intervals have been investigated.

This dissertation presents techniques for high-order simulation of electromagnetic fields, particularly for problems involving ships with ferromagnetic hulls and active corrosion-protection systems. A set of numerically constrained hexahedral basis functions for volume integral equation discretization is presented in a method-of-moments context. Test simulations demonstrate the accuracy achievable with these functions as well as the improvement brought about in system conditioning when compared to other basis sets. A general method for converting between a locally-corrected Nyström discretization of an integral equation and a method-of-moments discretization is presented next. Several problems involving conducting and magnetic-conducting materials are solved to verify the accuracy of the method and to illustrate both the reduction in number of unknowns and the effect of the numerically constrained bases on the conditioning of the converted matrix. Finally, a surface integral equation derived from Laplace’s equation is discretized using the locally-corrected Nyström method in order to calculate the electric fields created by impressed-current corrosion protection systems. An iterative technique is presented for handling nonlinear boundary conditions. In addition we examine different approaches for calculating the magnetic field radiated by the corrosion protection system. Numerical tests show the accuracy achievable by higher-order discretizations, validate the iterative technique presented. Various methods for magnetic field calculation are also applied to basic test cases.

This dissertation investigates unexplained vapor intrusion field data sets that have been observed at hazardous waste sites, including: 1) non-linear soil gas concentration trends between the VOC source (i.e. contaminated groundwater plume) and the ground surface; and, 2) alternative pathways that serve as entry points for vapors to infiltrate into buildings and serve to increase VOC exposure risks as compared to the classic vapor intrusion model, which primarily considered foundation cracks as the route for vapor entry. The overall hypothesis of this research is that theoretical knowledge of fate and transport processes can be systematically applied to vapor intrusion field data using a multiple lines of evidence approach to improve the science-based understanding of how and when vapor intrusion exposure risks will pose increased exposure risk; and, ultimately this knowledge can be used to develop policies that reduce exposure risks. The first objective of this research involved numerical modeling, field sampling and laboratory tests to investigate which factors influence soil gas transport within the subsurface. Combining results of all of these studies provide improved understanding of which factors influence VOC fate and transport within the subsurface. Importantly, the results demonstrate a non-linear trend between the VOC source concentration in the subsurface and the ground surface concentration at the study site, which disagrees with many vapor intrusion conceptual models. Ultimately, the source concentration may not be a good predictor of shallow soil gas concentrations. Laboratory tests described the effect of soil characteristics such as the soil water content on VOC vapor diffusion. The numerical model was able to explain specific conditions that could not be described by the field and laboratory data alone. A paper was published that summarizes the major outcomes from this objective (Pennell et al, 2016). The second objective of this research investigated preferential pathways for VOC vapor migration into buildings. Sewer systems can act as important pathways for vapor intrusion. The research objective is to evaluate conditions that increase the potential for inhalation exposure risks via vapor intrusion thorough sewer systems into indoor spaces. A field study was conducted in California over a 4-year period to investigate the spatial and temporal variability of alternative pathways (e.g. aging infrastructure piping systems) within the context of vapor intrusion exposure risks. A paper was published that summarizes the major outcomes from the field study (Roghani et al. 2018). The final research objective involved the development of a numerical model to describe VOC fate and transport within a sewer system. The numerical model predicts VOC mass transport. The model results were compared to the field data and provides insight about the role preferential pathways play in increasing VOC exposure risks.

Dropped object are defined as any object that fall under its own weight from a previously static position or fell due to an applied force from equipment or a moving object. It is among the top ten causes of injuries and fatality in oil and gas industry. To solve this problem, several in-house tools and guidelines is developed over time to assess the risk of dropped objects on the sub-sea structures. This thesis focuses on compiling and comparing those methods in hope to improve the recommended practices available in the market. A simple modification is done on the in-house tools to better predict the landing point distribution of the dropped cylindrical objects on the seabed by imposing the random three-dimensional rotation around the water depth axis. This tool is then used to compare the result of annual hit frequency using the recommended practice and further compared with the available experimental data.