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Very fine discretizations of differential operators often lead to large, sparse matrices A, where the condition number of A is large. Such ill-conditioning has well known effects on both solving linear systems and eigenvalue computations, and, in general, computing solutions with relative accuracy independent of the condition number is highly desirable. This dissertation is divided into two parts. In the first part, we discuss a method of preconditioning, developed by Ye, which allows solutions of Ax=b to be computed accurately. This, in turn, allows for accurate eigenvalue computations. We then use this method to develop discretizations that yield accurate computations of the smallest eigenvalue of the biharmonic operator across several domains. Numerical results from the various schemes are provided to demonstrate the performance of the methods. In the second part we address the role of the condition number of A in the context of multigrid algorithms. Under various assumptions, we use rigorous Fourier analysis on 2- and 3-grid iteration operators to analyze round off errors in floating point arithmetic. For better understanding of general results, we provide detailed bounds for a particular algorithm applied to the 1-dimensional Poisson equation. Numerical results are provided and compared with those obtained by the schemes discussed in part 1.

The primary objective of the presented research is to develop a class of integrated compact silicon diffractive sensors (CSDS) based on in-plane diffraction gratings. This class of sensors uses a silicon-on-insulator (SOI) substrate to limit costs, exploit established fabrication processes, enable integration of supporting electronics, and use the well-understood telecommunications wavelength of 1.55µm. Sensing is achieved by combining constant-diffraction-efficiency and highly-angularly-selective in-plane resonance-domain diffraction gratings. Detection is based on the diffraction efficiency of the highly angularly selective grating. In this research, the design processes for the constant-diffraction-efficiency and the highly angularly selective gratings are detailed. Grating designs are optimized with rigorous coupled-wave analysis (RCWA) and simulated with finite-difference time-domain (FDTD) analysis. Fabrication results are presented for the CSDS gratings. An inductively coupled plasma (ICP) Bosch etch process enables grating fabrication to within one percent of designed values with nearly vertical sidewalls. Experimental results are presented for individual CSDS gratings, the prototype sensor, and a prototype linear sensor array. The results agree well with simulation. The linear sensor array prototype demonstrates the intrinsic splitting mechanism and forms the basis of a 2-D sensor array. Finally, a toluene sensor was functionally demonstrated. The proof-of-concept device includes a polymer immobilization layer and microfluidic delivery of toluene. Toluene concentrations as low as 100ppm are measured, corresponding to a refractive index change of 3x10⁻⁴ RIU.

Large scale nonlinear programming (NLP) has proven to be an effective framework for obtaining profit gains through optimal process design and operations in chemical engineering. While the classical SQP and Interior Point methods have been successfully applied to solve many optimization problems, the focus of both academia and industry on larger and more complicated problems requires further development of numerical algorithms which can provide improved computational efficiency. The primary purpose of this dissertation is to develop effective problem formulations and an advanced numerical algorithms for efficient solution of these challenging problems. As problem sizes increase, there is a need for tailored algorithms that can exploit problem specific structure. Furthermore, computer chip manufacturers are no longer focusing on increased clock-speeds, but rather on hyperthreading and multi-core architectures. Therefore, to see continued performance improvement, we must focus on algorithms that can exploit emerging parallel computing architectures. In this dissertation, we develop an advanced parallel solution strategy for nonlinear programming problems with block-angular structure. The effectiveness of this and modern off-the-shelf tools are demonstrated on a wide range of problem classes. Here, we treat optimal design, optimal operation, dynamic optimization, and parameter estimation. Two case studies (air separation units and heat-integrated columns) are investigated to deal with design under uncertainty with rigorous models. For optimal operation, this dissertation takes cryogenic air separation units as a primary case study and focuses on formulations for handling uncertain product demands, contractual constraints on customer satisfaction levels, and variable power pricing. Multiperiod formulations provide operating plans that consider inventory to meet customer demands and improve profits. In the area of dynamic optimization, optimal reference trajectories are determined for load changes in an air separation process. A multiscenario programming formulation is again used, this time with large-scale discretized dynamic models. Finally, to emphasize a different decomposition approach, we address a problem with significant spatial complexity. Unknown water demands within a large scale city-wide distribution network are estimated. This problem provides a different decomposition mechanism than the multiscenario or multiperiod problems; nevertheless, our parallel approach provides effective speedup.

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A renewed focus to produce college and career ready graduates capable of thriving in science, technology, engineering, and mathematics (STEM) and other career and technical education professions has made access to advanced mathematics and science courses for all students a priority in K-12 education. Previous research on achievement has indicated that Black and Latino students are underrepresented in advanced mathematics and science courses and are lagging behind their peers in academic performance. Some researchers have suggested that these disparities in participation and achievement result from unequal access to educational opportunities. This purpose of this study was to examine student access to advanced mathematics and sciences courses in Virginia public high schools as an indicator of college and career readiness. This study employed secondary data analysis of school level data from the Virginia Department of Education. Regression analyses, simple and multiple, were used to examine access to advanced mathematics (Algebra II and higher) and advanced science (Chemistry and higher) course offerings by school characteristics, including school size, economically disadvantaged percentage, the percentage of minority students, and urbanicity locale. The results of this study indicated that student access to advanced mathematics and science course offerings, excluding and including AP mathematics and science courses, as in indicator of college and career readiness, differed based upon school size, economically disadvantaged percentage, and urbanicity locale. These findings, consistent with national statistics and other research, suggested that students who attend public high schools in the Commonwealth of Virginia do not have equal access to advanced mathematics and science course offerings, including AP mathematics and science courses, when school size, economically disadvantaged, and urbanicity locales are considered. Other findings related to access based on the percentage of minority students were inconsistent with prior research, as there was no significant difference in the number of advanced mathematics and science course offerings, excluding and including AP mathematics and science courses, based on the percentage of Black and Latino students enrolled in Virginia public high schools. / Ed. D.

We develop computer-assisted methods of proof for rigorous computation of the Evans function in order to prove stability of traveling waves. We use the parameterization method, series solutions, and the Newton-Kantorovich Theorem to obtain precise, rigorous error bounds for the numerical solution of the ODE used in the construction of the Evans function. We demonstrate these methods on a scalar reaction-diffusion model and on the Gray-Scott model.