Coupling of Chemical Kinetics with Computational Fluid Dynamics in a Three-Dimensional Engine Model

The role of computer modeling has grown recently to integrate itself as an inseparable tool to experimental studies for the optimization of automotive engines and the development of future fuels. Traditionally, computer models rely on simplified global reaction steps to simulate the combustion and pollutant formation inside the internal combustion engine. With the current interest in advanced combustion modes and injection strategies, this approach depends on arbitrary adjustment of model parameters that could reduce credibility of the predictions. The purpose of this study is to enhance the combustion model of KIVA, a computational fluid dynamics code, by coupling its fluid mechanics solution with detailed kinetic reactions solved by the chemistry solver, CHEMKIN. As a result, an engine-friendly reaction mechanism for n-heptane was selected to simulate diesel oxidation. Each cell in the computational domain is considered as a perfectly-stirred reactor which undergoes adiabatic constant- volume combustion. The model was applied to an ideally-prepared homogeneous- charge compression-ignition combustion (HCCI) and direct injection (DI) diesel combustion. Ignition and combustion results show that the code successfully simulates the premixed HCCI scenario when compared to traditional combustion models. Direct injection cases, on the other hand, do not offer a reliable prediction mainly due to the lack of turbulent-mixing model, inherent in the perfectly-stirred reactor formulation. In addition, the model is sensitive to intake conditions and experimental uncertainties which require implementation of enhanced predictive tools. It is recommended that future improvements consider turbulent-mixing effects as well as optimization techniques to accurately simulate actual in-cylinder process with reduced computational cost. Furthermore, the model requires the extension of existing fuel oxidation mechanisms to include pollutant formation kinetics for emission control studies.

Applications for nanomaterials in critical technologies

This thesis is devoted to the development, synthesis, properties, and applications of nano materials for critical technologies, including three areas: (1) Microbial contamination of drinking water is a serious problem of global significance. About 51% of the waterborne disease outbreaks in the United States can be attributed to contaminated ground water. Development of metal oxide nanoparticles, as viricidal materials is of technological and fundamental scientific importance. Nanoparticles with high surface areas and ultra small particle sizes have dramatically enhanced efficiency and capacity of virus inactivation, which cannot be achieved by their bulk counterparts. A series of metal oxide nanoparticles, such as iron oxide nanoparticles, zinc oxide nanoparticles and iron oxide-silver nanoparticles, coated on fiber substrates was developed in this research for evaluation of their viricidal activity. We also carried out XRD, TEM, SEM, XPS, surface area measurements, and zeta potential of these nanoparticles. MS2 virus inactivation experiments showed that these metal oxide nanoparticle coated fibers were extremely powerful viricidal materials. Results from this research suggest that zinc oxide nanoparticles with diameter of 3.5 nm, showing an isoelectric point (IEP) at 9.0, were well dispersed on fiberglass. These fibers offer an increase in capacity by orders of magnitude over all other materials. Compared to iron oxide nanoparticles, zinc oxide nanoparticles didn’t show an improvement in inactivation kinetics but inactivation capacities did increase by two orders of magnitude to 99.99%. Furthermore, zinc oxide nanoparticles have higher affinity to viruses than the iron oxide nanoparticles in presence of competing ions. The advantages of zinc oxide depend on high surface charge density, small nanoparticle sizes and capabilities of generating reactive oxygen species. The research at its present stage of development appears to offer the best avenue to remove viruses from water. Without additional chemicals and energy input, this system can be implemented by both points of use (POU) and large-scale use water treatment technology, which will have a significant impact on the water purification industry. (2) A new family of aliphatic polyester lubricants has been developed for use in micro-electromechanical systems (MEMS), specifically for hard disk drives that operate at high spindle speeds (>15000rpm). Our program was initiated to address current problems with spin-off of the perfluoroether (PFPE) lubricants. The new polyester lubricant appears to alleviate spin-off problems and at the same time improves the chemical and thermal stability. This new system provides a low cost alternative to PFPE along with improved adhesion to the substrates. In addition, it displays a much lower viscosity, which may be of importance to stiction related problems. The synthetic route is readily scalable in case additional interest emerges in other areas including small motors. (3) The demand for increased signal transmission speed and device density for the next generation of multilevel integrated circuits has placed stringent demands on materials performance. Currently, integration of the ultra low-k materials in dual Damascene processing requires chemical mechanical polishing (CMP) to planarize the copper. Unfortunately, none of the commercially proposed dielectric candidates display the desired mechanical and thermal properties for successful CMP. A new polydiacetylene thermosetting polymer (DEB-TEB), which displays a low dielectric constant (low-k) of 2.7, was recently developed. This novel material appears to offer the only avenue for designing an ultra low k dielectric (1.85k), which can still display the desired modulus (7.7Gpa) and hardness (2.0Gpa) sufficient to withstand the process of CMP. We focused on further characterization of the thermal properties of spin-on poly (DEB-TEB) ultra-thin film. These include the coefficient of thermal expansion (CTE), biaxial thermal stress, and thermal conductivity. Thus the CTE is 2.0*10-5K-1 in the perpendicular direction and 8.0*10-6 K-1 in the planar direction. The low CTE provides a better match to the Si substrate which minimizes interfacial stress and greatly enhances the reliability of the microprocessors. Initial experiments with oxygen plasma etching suggest a high probability of success for achieving vertical profiles.

Characterizing neural tissues with mass spectrometry imaging via enhanced computational approaches

Neuropeptides affect the activity of the myriad of neuronal circuits in the brain. They are under tight spatial and chemical control and the dynamics of their release and catabolism directly modify neuronal network activity. Understanding neuropeptide functioning requires approaches to determine their chemical and spatial heterogeneity within neural tissue, but most imaging techniques do not provide the complete information desired. To provide chemical information, most imaging techniques used to study the nervous system require preselection and labeling of the peptides of interest; however, mass spectrometry imaging (MSI) detects analytes across a broad mass range without the need to target a specific analyte. When used with matrix-assisted laser desorption/ionization (MALDI), MSI detects analytes in the mass range of neuropeptides. MALDI MSI simultaneously provides spatial and chemical information resulting in images that plot the spatial distributions of neuropeptides over the surface of a thin slice of neural tissue. Here a variety of approaches for neuropeptide characterization are developed. Specifically, several computational approaches are combined with MALDI MSI to create improved approaches that provide spatial distributions and neuropeptide characterizations. After successfully validating these MALDI MSI protocols, the methods are applied to characterize both known and unidentified neuropeptides from neural tissues. The methods are further adapted from tissue analysis to be able to perform tandem MS (MS/MS) imaging on neuronal cultures to enable the study of network formation. In addition, MALDI MSI has been carried out over the timecourse of nervous system regeneration in planarian flatworms resulting in the discovery of two novel neuropeptides that may be involved in planarian regeneration. In addition, several bioinformatic tools are developed to predict final neuropeptide structures and associated masses that can be compared to experimental MSI data in order to make assignments of neuropeptide identities. The integration of computational approaches into the experimental design of MALDI MSI has allowed improved instrument automation and enhanced data acquisition and analysis. These tools also make the methods versatile and adaptable to new sample types.