<p> Energy storage has received much attention due to the increasing use of energy, especially renewable energy. Lithium-ion batteries have great characteristics for electrical energy storage. Higher specific energy density, cycle life, cell voltage, shorter charge times gives lithium-ion batteries favorable energy storage characteristics over other battery chemistries. Although lithium-ion batteries are increasing in use for electrical energy storage, their safety still poses an engineering problem. When lithium-ion batteries are abused, they can enter thermal runaway. This event is dangerous as it can eject hot gases and shrapnel. Previous studies focused on different aspects of thermal runaway, for example, heat generation from chemical reactions, propagation to other cells, and the physics of gas venting. One phenomenon that has not received much attention is the evaporation of the electrolyte out of a failed lithium-ion battery. Understanding the effect of electrolyte evaporation is key to having a more complete understanding of thermal runaway. In this thesis, the physics of electrolyte evaporation is studied with the purpose of developing more accurate thermal abuse models. An evaporation model was developed, based on porous drying theory and a 1-D liquid diffusion process. Experiments were conducted to identify the liquid diffusion coefficient which governs the rate of electrolyte transport within the porous separator within the battery. The 1-D liquid diffusion model was then implemented into an existing thermal abuse model and exercised for a typical oven test scenario. Results showed that the physics-based evaporation model resulted in excellent agreement with experimental data at different oven temperatures.</p>

<p> Orbital Debris is a growing challenge for the Space Industry. The increasing density of derelict objects in high-value orbital regimes is resulting in more conjunction warnings and break-up events with cascading repercussions on active satellites and spacecraft. The recent rapid growth of the commercial space industry, in particular proliferated satellite constellations, has placed orbital debris remediation at the forefront of Space Industry efforts. The need to remove existing debris, combined with a growing demand for active satellite life extension services, has created an emerging market for space logistics, in particular spacecraft capable of rendezvous and docking, orbital refueling, debris deorbiting, or object relocation. This market has seen numerous companies emerge with multi-purpose on-orbit servicing platforms. This ecosystem poses technological, economical, and policy questions to decision-makers looking to acquire platforms or invest in technologies and requires a System-of-Systems approach to determine mission and system concepts of merit. An open-source modeling, analysis, and simulation software toolbox has been developed which enables rapid early-stage analysis and design of diverse fleets of on-orbit servicing platforms, with a specific emphasis on active debris removal applications. The toolbox provides fetching and processing of real-time orbital catalog data, clustering and scoring of high-value debris targets, flexible and efficient multi-vehicle multi-objective time-varying routing optimization, and fleet-level lifecycle cost estimation. The toolbox is applied to a diverse sample of promising commercial platforms to enable government decision-makers to make sound investment and acquisition decisions to support the development of ADR technologies, missions, and companies. </p>

Conducting seamless Live Virtual Constructive (LVC) simulation remains the most challenging issue of Modeling and Simulation (M&S). There is a lack of interoperability, limited reuse and loose integration between the Live, Virtual and/or Constructive assets across multiple Standard Simulation Architectures (SSAs). There have been various theoretical research endeavors about solving these problems but their solutions resulted in complex and inflexible integration, long user-usage time and high cost for LVC simulation. The goal of this research is to provide an Agile Roadmap for the Live Virtual Constructive-Integrating Training Architecture (LVC-ITA) that will address the above problems and introduce interoperable LVC simulation. Therefore, this research describes how the newest M&S technologies can be utilized for LVC simulation interoperability and integration. Then, we will examine the optimal procedure to develop an agile roadmap for the LVC-ITA. In addition, this research illustrated a case study using an Adaptive distributed parallel Simulation environment for Interoperable and reusable Model (AddSIM) that is a component based integrated simulation engine. The agile roadmap of the LVC-ITA that reflects the lessons learned from the case study will contribute to guide M&S communities to an efficient path to increase interaction of M&S simulation across systems.

International Telemetering Conference Proceedings / October 26-29, 1998 / Town & Country Resort Hotel and Convention Center, San Diego, California / Current changes in the way that large flight test systems are utilized have affected the industry’s methodology in both the early design phases and in the implementation of nextgeneration hardware and software. The reduction of available RF spectrum, the implementation of packet telemetry methods and systems, and a desire to implement commercial-off-the-shelf (COTS) hardware are only some of the considerations that telemetry systems integrators and product houses have to face. This paper describes how test methodology changes affect current large systems design at both government test ranges and at airframe/missile manufacturer test facilities. In addition, consideration is given to the area of increased processing power as it affects hardware and software design, the leveraging of such current and future telecommunications technology as network switch technology and compression, cross utilization, standardized technology, and the movement toward platform-independent software.

This dissertation investigates the value of deep reinforcement learning (DRL) within an agent-based model (ABM) of a large open-air venue. The intent is to reduce civilian casualties in an active shooting incident (ASI). There has been a steady increase of ASIs in the United States of America for over 20 years, and some of the most casualty-producing events have been in open spaces and open-air venues. More research should be conducted within the field to help discover policies that can mitigate the threat of a shooter in extremis. This study uses the concept of dynamic signage, controlled by a DRL policy, to guide civilians away from the threat and toward a safe exit in the modeled environment. It was found that a well-trained DRL policy can significantly reduce civilian casualties as compared to baseline scenarios. Further, the DRL policy can assist decision makers in determining how many signs to use in an environment and where to place them. Finally, research using DRL in the ASI space can yield systems and policies that will help reduce the impact of active shooters during an incident.