This thesis explored the history of military simulations and linked it to the current challenges of interoperability. The research illustrated the challenge of interoperability in integrating different networks, databases, standards, and interfaces and how it results in U.S. Army organizations constantly spending time and money to create and implement irreproducible Live, Virtual, and Constructive (LVC) integrating architectures to accomplish comparable tasks. Although the U.S. Army has made advancements in interoperability, it has struggled with this challenge since the early 1990s. These improvements have been inadequate due to evolving and growing needs of the user coupled with the technical complexities of interoperating legacy systems with emergent systems arising from advances in technology. To better understand the impact of the continued evolution of simulations, this paper mapped Maslow's Hierarchy of Needs with Tolk's Levels of Conceptual Interoperability Model (LCIM). This mapping illustrated a common relationship in both the Hierarchy of Needs and the LCIM model depicting that each level increases with complexity and the proceeding lower level must first be achieved prior to reaching the next. Understanding the continuum of complexity of interoperability, as requirements or needs, helped to determine why the previous funding and technical efforts have been inadequate in mitigating the interoperability challenges within U.S. Army simulations. As the U.S. Army's simulation programs continue to evolve while the military and contractor personnel turnover rate remains near constant, a method of capturing and passing on the tacit knowledge from one personnel staffing life cycle to the next must be developed in order to economically and quickly reproduce complex simulation events. This thesis explored a potential solution to this challenge, the Executable Architecture Systems Engineering (EASE) research project managed by the U.S. Army’s Simulation and Training Technology Center in the Army Research Laboratory within the Research, Development and Engineering Command. However, there are two main drawbacks to EASE; it iv is still in the prototype stage and has not been fully tested and evaluated as a simulation tool within the community of practice. In order to determine if EASE has the potential to reduce the micro as well as macro interoperability, an EASE experiment was conducted as part of this thesis. The following three alternative hypothesis were developed, tested, and accepted as a result of the research for this thesis: Ha1 = Expert stakeholders believe the EASE prototype does have potential as a U.S. Army technical solution to help mitigate the M&S interoperability challenge. Ha2 = Expert stakeholders believe the EASE prototype does have potential as a U.S. Army managerial solution to help mitigate the M&S interoperability challenge. Ha3 = Expert stakeholders believe the EASE prototype does have potential as a U.S. Army knowledge management solution to help mitigate the M&S interoperability challenge. To conduct this experiment, eleven participants representing ten different organizations across the three M&S Domains were selected to test EASE using a modified Technology Acceptance Model (TAM) approach developed by Davis. Indexes were created from the participants’ responses to include both the quality of participants and research questions. The Cronbach Alpha Test for reliability was used to test the reliability of the adapted TAM. The Wilcoxon Signed Ranked test provided the statistical analysis that formed the basis of the research; that determined the EASE project has the potential to help mitigate the interoperability challenges in the U.S. Army's M&S domains.

Theory of turbulence at small scales plays a fundamental role in modeling turbulence and in retrieving information from physical measurements of turbulent flows. A systematic methodology based on direct numerical simulations of turbulent flows is developed to investigate universality of small scale turbulence. Understanding characteristics of the small scale intermittency in turbulent flows and the accuracy of the models, measurements, and theories in predicting it are the main objectives. The research is designed to address two central questions; 1) possible effects of large scale anisotropies on the small scale turbulence and 2) potential biases in characterizing small scale turbulence due to the nature of the quantities used to extract the information, known as surrogates. Direct numerical simulations of forced, isotropic homogeneous turbulence with extraordinarily fine spatial resolution on a periodic box up to 4096 × 4096 × 4096 grid points are analyzed first, to provide a clear insight to the small scale turbulence in the absence of large scale anisotropy. Direct numerical simulations of forced, homogeneous and axisymmetric density stratified flows on a periodic box up to 4096 × 4096 × 2048 grid points with the buoyancy Reynolds number ranging from 10 to 220 are considered next. Different levels of density stratification in the vertical direction cause different levels of large scale anisotropy in the flows. These unique simulations provide a clear picture of turbulent structures over an extensive range of scales. The dissipation rate of turbulent kinetic energy is chosen as the main descriptor of small scale structures. A comprehensive study on surrogates of energy dissipation rate is conducted to identify the best descriptor of the small scale turbulence based on easily measured quantities in physical experiments. In particular, the one-dimensional longitudinal and transverse surrogates, as well as a surrogate based on the asymmetric part of the strain rate tensor, are considered.The statistical analysis of local and locally averaged energy dissipation rate indicates that the small scale intermittency manifested in the energy dissipation rate is universal with intermittency exponent of μ = 0.25 ± 0.05, independent of flow conditions and measurement methods. In contrary, the general shape of the probability density functions of energy dissipation rate is strongly skewed to reflect all the existing dynamics in the flow. The surrogates are fundamentally different than the energy dissipation rate. The longitudinal and transverse surrogates overestimate the intermittency exponent by factors of 1.5 and 2.2, respectively. The scale dependency of the moments of locally averaged energy dissipation rate is proposed as a powerful technique to identify the dominant dynamics of the complex flows for a specific range of scales in physical space. Specifically, for the stratified turbulence, this method suggests a superposition of patches of three-dimensional turbulence superimposed on the background semi two-dimensional stratified flow.

Carbon dioxide (CO2), as a greenhouse gas, has shown to achieve the highest level in history, causes the global warming issue, leading to a 1.2 ℃ increase of the global average temperature. The consumption of fossil fuels is one of the main reasons that cause CO2 emission. Current industrial production of chemicals accounts for 29% of total fossil fuels consumption, which can be the feedstock or raw materials for carbon source, or act as the fuel to generate heat and power. CO2 conversion technologies, e.g., thermo-catalytic reaction and electrochemical reduction, have drawn researchers' attention, since they have the potential to resolve the feedstock and fuel consumption sectors of chemical production at the same time. CO2 conversion technologies use CO2 as the direct carbon source of chemicals and store the intermittent renewable energies as the energy source, which can ultimately achieve a net-zero CO2 emission and produce value-added chemical products. However, there are challenges for a practical application of CO2 conversion technologies. For instance, electrochemical CO2 reduction reaction (ECO2RR) suffers from the low activity and selectivity, while thermocatalytic CO2 conversion, or the CO2 hydrogenation reaction, usually requires harsh reaction conditions and has a low selectivity. Nonetheless, the improvement of developing new promising catalysts remains limited, due to the lack of insights of the reactions. The complex reaction networks and kinetics lead to an elusive reaction mechanism, and various effects, e.g., solvation, potential, structure, and coverage, hinder our fundamental understanding of catalytic processes. Herein, we report the efforts that we have been put in to gain insights of reaction mechanism of CO2 reduction reactions. Bi has shown to reduce CO2 to formic acid (HCOOH), while we have found that, by constituting a Bi-Cu2S heterostructure catalyst, a better catalytic performance was achieved, due to the structural effect of the interface (Chapter 2). However, it is shown that the CO2 electrochemical reduction mechanism on Bi has changed when switching the electrolyte from water to aprotic media, e.g., ionic liquids, and CO was obtained as the main product instead of HCOOH, showing a shift of reaction pathway due to the electrolyte effect (Chapter 3). However, the fundamental understanding of reaction mechanism requires not only the reaction pathways, but the reaction kinetics under reaction conditions, where the lateral or adsorbate-adsorbate interactions play an important role. In this case, we summarized recent advances of applications of machine learning (ML) algorithms for adsorbate-adsorbate interaction model developments to deal with the realistic reaction kinetics (Chapter 4). The lattice based Kinetic Monte Carlo (KMC) has shown promising performances for considering the lateral interactions of surface reactions. We report the mechanistic and KMC kinetic study of CO2 hydrogenation on Cesium promoted Au(111) surface, to gain insights of alkali metal promoting effects under reaction conditions (Chapter 5). To expand the scope, the integration of CO2 reduction with the C-N bond formation provides a promising strategy to produce more value-added product such as urea. Recent studies show that urea can be produced by reducing CO2 and nitrate (NO3-) from wastewater, which mitigate both global warming and nitrate pollution issue. However, the reaction mechanism remains elusive due to the complicated reaction network. Therefore, we employed the first-principles molecular dynamics to reveal the reaction mechanism of C-N coupling and the effect of different reaction conditions including applied potential and electrolyte (Chapter 6). Although recent advances in the computational catalysis field have significantly push forward the understanding of the chemistry nature of heterogeneous catalysis, the gap between theory and experiment remains far beyond bridged due to the complexity nature of the problem in a wide range of time and length scales, hinders the development and discovery of active catalytic materials. Recent advances of narrowing and bridging the complexity gap between theory and experiment with machine learning have been summarized to emphasize the importance of utilizing machine learning for rational catalyst design (Chapter 7). / Doctor of Philosophy / Global warming issue is a rising topic in recent years which has severe impacts on environments. One of the main reasons is the increase level of greenhouse gases that prevent the release of heat that captured from the sun. Carbon dioxide (CO2) is achieving the highest level in history due to the human activities including the consumption of fossil fuels. Therefore, CO2 conversion technologies are needed to tackle reduce the CO2 level in the atmosphere and the emission of CO2 in industries. CO2 conversion technologies, e.g., thermo-catalytic reaction and electrochemical reduction, have drawn researchers' attention, since they have the potential to resolve the feedstock and fuel consumption sectors of chemical production at the same time. However, the complexity of the CO2 conversion processes hinders the development of new technologies. Since the nature of these technologies are heterogeneous catalytic reactions, all reactions are happening at the interface between catalysts and reactants/products, which calls for the understanding of interfacial mechanisms of CO2 reduction reactions. For this type of high degree of freedom problem where many phases including solid-solid, solid-liquid, and solid-gas phases exist, multiscale simulations turn out to be a proper approach since the wide time and length scale that can be covered. Herein, we employed different multiscale modeling methods to tackle various CO2 reduction problems. For electrochemical reduction of CO2, we designed a novel Bi-Cu2S hetero-structured catalyst, which has abundant interfacial sites between Bi and Cu2S, demonstrating the improved catalytic performance of ECO2RR toward formate production. At the same time, it has been found that in non-aqueous solution, the reaction pathway has been switched, where CO is obtained as the final product instead of formate. This effect has been investigated using constant potential calculation method to probe the reaction under reaction condition. For thermo-catalytic reactions, we studied the CO2 hydrogenation on Cesium promoted Au(111) surface using quantum mechanics and kinetic Monte Carlo (KMC) calculations, to gain insights of alkali metal promoting effects under reaction conditions. To expand the scope, the integration of CO2 electroreduction with C-N coupling is a promising strategy for global warming and pollution control, which utilizes the nitrate (NO3-) from wastewater and CO2 to produce high value-added product such as urea. The fundamental investigation of reaction mechanism of C-N coupling has been studied using first principles molecular dynamics.

Molecular dynamics simulations were carried out on traditional polymers copolymerized with POSS (Polyhedral Oligomeric Silsesquioxanes) derivatives to identify the reason behind improved properties imparted to the conventional polymers with the chemical incorporation of POSS. Two classes of systems are used in the present study, namely the polystyrene and polymethyl methacrylate systems. Seven systems are studied in the polystyrene class. The effect of corner substituent groups of the POSS cage on the properties of the polymer nanocomposites was studied using the polystyrene. In addition, the effect of the type of cage structure on the properties was studied using T8, T10 and T12 POSS cage structures containing phenyl substituents on each POSS cage. Systems with polymethyl methacrylate were studied to analyze the effect of mole percent of POSS on the polymer properties, holding the corner substituents on the POSS unit constant. The corner function used was the isobutyl group. The properties analyzed using simulations include glass transition temperature, volumetric thermal expansion coefficient, X-ray scattering data, solubility parameter and mechanical properties. In both polystyrene and polymethyl methacrylate systems, simulations were also carried out on the pure parent polymers for the sake of comparison. The effect of forcefield on the predicted properties was studied using both COMPASS and PCFF forcefields. Performance analysis of the code used in the present simulation was done by analyzing the parallel run time of simulations involving pure atactic polystyrene.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.