Classical and reactive molecular dynamics: Principles and applications in combustion and energy systems

Molecular dynamics (MD) has evolved into a ubiquitous, versatile and powerful computational method for fundamental research in science branches such as biology, chemistry, biomedicine and physics over the past 60 years. Powered by rapidly advanced supercomputing technologies in recent decades, MD has entered the engineering domain as a first-principle predictive method for material properties, physicochemical processes, and even as a design tool. Such developments have far-reaching consequences, and are covered for the first time in the present paper, with a focus on MD for combustion and energy systems encompassing topics like gas/liquid/solid fuel oxidation, pyrolysis, catalytic combustion, heterogeneous combustion, electrochemistry, nanoparticle synthesis, heat transfer, phase change, and fluid mechanics. First, the theoretical framework of the MD methodology is described systemically, covering both classical and reactive MD. The emphasis is on the development of the reactive force field (ReaxFF) MD, which enables chemical reactions to be simulated within the MD framework, utilizing quantum chemistry calculations and/or experimental data for the force field training. Second, details of the numerical methods, boundary conditions, post-processing and computational costs of MD simulations are provided. This is followed by a critical review of selected applications of classical and reactive MD methods in combustion and energy systems. It is demonstrated that the ReaxFF MD has been successfully deployed to gain fundamental insights into pyrolysis and/or oxidation of gas/liquid/solid fuels, revealing detailed energy changes and chemical pathways. Moreover, the complex physico-chemical dynamic processes in catalytic reactions, soot formation, and flame synthesis of nanoparticles are made plainly visible from an atomistic perspective. Flow, heat transfer and phase change phenomena are also scrutinized by MD simulations. Unprecedented details of nanoscale processes such as droplet collision, fuel droplet evaporation, and CO2 capture and storage under subcritical and supercritical conditions are examined at the atomic level. Finally, the outlook for atomistic simulations of combustion and energy systems is discussed in the context of emerging computing platforms, machine learning and multiscale modelling

Bridging microscopy with molecular dynamics and quantum simulations: An AtomAI based pipeline

Recent advances in (scanning) transmission electron microscopy have enabled routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for atomistic simulations. In this fashion, theory will address experimentally emerging structures, as opposed to the full range of theoretically possible atomic configurations. However, this challenge is highly non-trivial due to the extreme disparity between intrinsic time scales accessible to modern simulations and microscopy, as well as latencies of microscopy and simulations per se. Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment, to enable the selection of regions of interest and exploring them using physical simulations. Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors. The pathways to ensure the structural stability and compensate for the observational biases universally present in the data are identified in the workflow. This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects. However, it is universal and can be used for other material systems

Developing Multi-Scale Models for Water Quality Management in Drinking Water Distribution Systems

Drinking water supply systems belong to the group of critical infrastructure systems that support the socioeconomic development of our modern societies. In addition, drinking water infrastructure plays a key role in the protection of public health by providing a common access to clean and safe water for all our municipal, industrial, and firefighting purposes. Yet, in the United States, much of our national water infrastructure is now approaching the end of its useful life while investments in its replacement and rehabilitation have been consistently inadequate. Furthermore, the aging water infrastructure has often been operated empirically, and the embracement of modern technologies in infrastructure monitoring and management has been limited. Deterioration of the water infrastructure and poor water quality management practices both have serious impacts on public health due to the increased likelihood of contamination events and waterborne disease outbreaks. Water quality reaching the consumers’ taps is largely dependent on a group of physical, chemical, and biological interactions that take place as the water transports through the pipes of the distribution system and inside premise plumbing. These interactions include the decay of disinfectant residuals, the formation of disinfection by-products (DBPs), the corrosion of pipe materials, and the growth and accumulation of microbial species. In addition, the highly dynamic nature of the system’s hydraulics adds another layer of complexity as they control the fate and transport of the various constituents. On the other hand, the huge scale of water distribution systems contributes dramatically to this deterioration mainly due to the long transport times between treatment and consumption points. Hence, utilities face a considerable challenge to efficiently manage the water quality in their aging distribution systems, and to stay in compliance with all regulatory standards. By integrating on-line monitoring with real-time simulation and control, smart water networks offer a promising paradigm shift to the way utilities manage water quality in their systems. Yet, multiple scientific gaps and engineering challenges still stand in the way towards the successful implementation of such advanced systems. In general, a fundamental understanding of the different physical, chemical, and biological processes that control the water quality is a crucial first step towards developing useful modeling tools. Furthermore, water quality models need to be accurate; to properly simulate the concentrations of the different constituents at the points of consumption, and fast; to allow their implementation in real-time optimization algorithms that sample different operational scenarios in real-time. On-line water quality monitoring tools need be both reliable and inexpensive to enable the ubiquitous surveillance of the system at all times. The main objective of this dissertation is to create advanced computational tools for water quality management in water distribution systems through the development and application of a multi-scale modeling framework. Since the above-mentioned interactions take place at different length and time scales, this work aims at developing computational models that are capable of providing the best description of each of the processes of interest by properly simulating each of its underlying phenomena at its appropriate scale of resolution. Molecular scale modeling using tools of ab-initio quantum chemical calculations and molecular dynamics simulations is employed to provide detailed descriptions of the chemical reactions happening at the atomistic level with the aim of investigating reaction mechanisms and developing novel materials for environmental sensing. Continuum scale reactive-transport models are developed for simulating the spatial and temporal distributions of the different compounds at the pipe level considering the effects of the dynamic hydraulics in the system driven by the spatiotemporal variability in water demands. System scale models are designed to optimize the operation of the different elements of the system by performing large-scale simulations coupled with optimization algorithms to identify the optimal operational strategies as a basis for accurate decision-making and superior water quality management. In conclusion, the computational models developed in this study can either be implemented as stand-alone tools for simulating the fundamental processes dictating the water quality at different scales of resolution, or be integrated into a unified framework in which information from the small scale models are propagated into the larger scale models to render a high fidelity representation of these processes

Elucidating Nucleation and Growth Behavior of Single-Walled Carbon Nanotubes obtained via Catalyzed Synthesis

The catalytic growth of single-walled carbon nanotubes (SWCNTs) is studied using reactive molecular dynamics (RMD) simulations and density functional theory (DFT) calculations. Computational calculations are performed in order to achieve a better understanding of the catalytic reaction mechanism at the initial stages of synthesis, where most of the structural characteristics are defined. Different process variables such as catalyst chemical composition and size, temperature, pressure, and the nature of catalyst support, can be optimized with the purpose of tuning the structure and physical properties of SWCNTs. Controlling the structure of SWCNTs during synthesis and avoiding additional purification and/or separation processes are critical for the direct use of SWCNTs in electronic devices. RMD simulations demonstrate that small catalyst particles favor the growth of lengthy nanotubes over catalyst encapsulation as a result of an increase of the curvature energies of the carbon capsule. Furthermore, simulations performed over deposited catalyst particles demonstrate that the catalyst-support adhesion must be controlled in order to grow nanotubes with high structural quality and avoid catalyst poisoning. Results herein reported suggest that growth conditions must be optimum to minimize the nucleation of topological defects in nanotubes. RMD trajectories prove the vital role played by the catalyst surface in healing defects via adsorption and diffusion. These results significantly impact the field of chirality control since the presence of defects introduce misorientation of hexagons, shifts the overall chiral angle, and therefore, modifies the physical properties of the nanotube. DFT calculations are employed to evaluate the interaction between SWCNTs and the ST-cut quartz substrate. The outstanding performance of CNT-based FET relies on the alignment of the horizontally grown nanotubes on silica substrates, as well as on the selective growth of semiconducting nanotubes. It is demonstrated that finite-length zigzag nanotubes are adsorbed stronger than armchair tubes on the quartz support. This suggests that the nanotube electronic band structure is a key factor on the preferential adsorption of zigzag tubes. DFT calculations suggest that patterns of unsaturated silicon atoms of silica surfaces define the crystallographic directions of preferential alignment. These patterns might be chemically altered in order to favor other directions of alignment

Investigating Advanced Cathode Materials for Li/Na-S Batteries Experimentally and Theoretically

Lithium/sodium-sulfur (Li/Na-S) batteries hold practical promise for next-generation batteries because of high energy density and low cost. Development is impeded at present however because of unsatisfied discharge capacity and stability in long cycling. Advanced materials can serve as sulfur host materials to improve the capacities and stability of the lithium/sodium-sulfur batteries. More importantly, they provide suitable models with which to connect and test experimental results with theoretical predictions. This is crucial to develop insight into the relationship between electrochemical behavior of sulfur and the structural properties of sulfur host materials. This thesis explores sulfur and its intermediates adsorption/redox conversion mechanisms and investigate crucial structural-property relationships of the advanced nanomaterials as sulfur host materials in high-performance lithium/sodium-sulfur batteries. First, A unique three-dimensional hybrid of nickel sulfide and carbon hollow spheres was synthesized as a sulfur host. The uniformly distributed nickel sulfide can greatly promote adsorption capability towards polysulfides. Meanwhile, the hollow carbon spheres increase sulfur loading as well as the overall conductivity of the sulfur host. Utilized in an electrode, this 3D hybrid sulfur host achieved a capacity of 695 mA h g-1 after 300 cycles at 0.5 C and a low capacity decay of 0.013% per cycle. Second, a two-dimensional (2D) MoN-VN heterostructure is investigated as a model sulfur host. The 2D heterostructure can regulate polysulfides and improve sulfur utilization efficiency. This resulted in superior rating and cycling performance. More importantly, incorporation of V in the heterostructure can effectively tailor the electronic structure of MoN. This leads to enhanced polysulfides adsorption. Last, a two-dimensional (2D) metal-framework (MOF) is investigated as a model sulfur host for Na-S batteries. The MOF can enhance polysulfides adsorption and conversion kinetics. This resulted in superior rating and cycling performance. Through a combination of advanced experimental characterization techniques and theoretical computations based on the 2D nanomaterials, an in-depth understanding of sulfur redox and the structure-properties relationships in metal-sulfur batteries have been obtained.Thesis (Ph.D.) -- University of Adelaide, School of Chemical Engineering & Advanced Materials, 201

TECHNIQUES AND PROTOCOLS FOR DISTRIBUTED MEDIA STREAMING

Ph.DDOCTOR OF PHILOSOPH

Federal User Fees: A Design Guide

Other written product issued by the Government Accountability Office with an abstract that begins "The federal government will need to make the most of its resources to meet the emerging challenges of the 21st century. As new priorities emerge, policymakers have demonstrated interest in user fees as a means of financing new and existing services. User fees can be designed to reduce the burden on taxpayers to finance the portions of activities that provide benefits to identifiable users above and beyond what is normally provided to the public. By charging the costs of those programs or activities to beneficiaries, user fees can also promote economic efficiency and equity. However, to achieve these goals, user fees must be well designed. GAO was asked to study how user fee design characteristics may influence the effectiveness of user fees. Specifically, GAO examined how the four key design and implementation characteristics of user fees--how fees are set, collected, used, and reviewed--may affect the economic efficiency, equity, revenue adequacy, and administrative burden of cost-based fees. GAO reviewed economic and policy literature on federal and nonfederal user fees, including prior GAO work, and used relevant case examples to illustrate different types of design elements and the impacts they may have.

Compilation of thesis abstracts, December 2006

NPS Class of December 2006This quarter’s Compilation of Abstracts summarizes cutting-edge, security-related research conducted by NPS students and presented as theses, dissertations, and capstone reports. Each expands knowledge in its field.http://archive.org/details/compilationofsis109452750

Development of Interatomic Potentials with Uncertainty Quantification: Applications to Two-dimensional Materials

University of Minnesota Ph.D. dissertation.July 2019. Major: Aerospace Engineering and Mechanics. Advisor: Ellad Tadmor. 1 computer file (PDF); xiii, 198 pages.Atomistic simulation is a powerful computational tool to investigate materials on the microscopic scale and is widely employed to study a large variety of problems in science and engineering. Empirical interatomic potentials have proven to be an indis- pensable part of atomistic simulation due to their unrivaled computational efficiency in describing the interactions between atoms, which produce the forces governing atomic motion and deformation. Atomistic simulation with interatomic potentials, however, has historically been viewed as a tool limited to provide only qualitative insight. A key reason is that in such simulations there are many sources of uncertainty that are difficult to quantify, thus failing to give confidence interval on the obtained results. This thesis presents my research work on the development of interatomic potentials with the ability to quantify the uncertainty in simulation results. The methods to train interatomic po- tentials and quantify the uncertainty are demonstrated via two-dimensional materials and heterostructures throughout this thesis, whose low-dimensional nature makes them distinct from their three-dimensional counterparts in many aspects. Both physics-based and machine learning interatomic potentials are developed for MoS2 and multilayer graphene structures. The new potentials accurately model the interactions in these systems, reproducing a number of structural, energetic, elastic, and thermal properties obtained from first-principles calculations and experiments. For physics-based poten- tials, a method based on Fisher information theory is used to analyze the parametric sensitivity and the uncertainty in material properties obtained from phase average. We show that the dropout technique can be applied to train neural network potentials and demonstrate how to obtain the predictions and the associated uncertainties of material properties practically and efficiently from such potentials. Putting all these ingredients of my research work together, we create an open-source fitting framework to train inter- atomic potentials and hope it can make the development and deployment of interatomic potentials easier and less error prone for other researchers