1,504 research outputs found

    Design of Energy Storage Controls Using Genetic Algorithms for Stochastic Problems

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    A successful power system in military applications (warship, aircraft, armored vehicle etc.) must operate acceptably under a wide range of conditions involving different loading configurations; it must maintain war fighting ability and recover quickly and stably after being damaged. The introduction of energy storage for the power system of an electric warship integrated engineering plant (IEP) may increase the availability and survivability of the electrical power under these conditions. Herein, the problem of energy storage control is addressed in terms of maximizing the average performance. A notional medium-voltage dc system is used as the system model in the study. A linear programming model is used to simulate the power system, and two sets of states, mission states and damage states, are formulated to simulate the stochastic scenarios with which the IEP may be confronted. A genetic algorithm is applied to the design of IEP to find optimized energy storage control parameters. By using this algorithm, the maximum average performance of power system is found

    Robust and Efficient Swarm Communication Topologies for Hostile Environments

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    Swarm Intelligence-based optimization techniques combine systematic exploration of the search space with information available from neighbors and rely strongly on communication among agents. These algorithms are typically employed to solve problems where the function landscape is not adequately known and there are multiple local optima that could result in premature convergence for other algorithms. Applications of such algorithms can be found in communication systems involving design of networks for efficient information dissemination to a target group, targeted drug-delivery where drug molecules search for the affected site before diffusing, and high-value target localization with a network of drones. In several of such applications, the agents face a hostile environment that can result in loss of agents during the search. Such a loss changes the communication topology of the agents and hence the information available to agents, ultimately influencing the performance of the algorithm. In this paper, we present a study of the impact of loss of agents on the performance of such algorithms as a function of the initial network configuration. We use particle swarm optimization to optimize an objective function with multiple sub-optimal regions in a hostile environment and study its performance for a range of network topologies with loss of agents. The results reveal interesting trade-offs between efficiency, robustness, and performance for different topologies that are subsequently leveraged to discover general properties of networks that maximize performance. Moreover, networks with small-world properties are seen to maximize performance under hostile conditions

    Resilience Model for Teams of Autonomous Unmanned Aerial Vehicles (UAV) Executing Surveillance Missions

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    Teams of low-cost Unmanned Aerial Vehicles (UAVs) have gained acceptance as an alternative for cooperatively searching and surveilling terrains. These UAVs are assembled with low-reliability components, so unit failures are possible. Losing UAVs to failures decreases the team\u27s coverage efficiency and impacts communication, given that UAVs are also communication nodes. Such is the case of a Flying Ad Hoc Network (FANET), where the failure of a communication node may isolate segments of the network covering several nodes. The main goal of this study is to develop a resilience model that would allow us to analyze the effects of individual UAV failures on the team\u27s performance to improve the team\u27s resilience. The proposed solution models and simulates the UAV team using Agent-Based Modeling and Simulation. UAVs are modeled as autonomous agents, and the searched terrain as a two-dimensional M x N grid. Communication between agents permits having the exact data on the transit and occupation of all cells in real time. Such communication allows the UAV agents to estimate the best alternatives to move within the grid and know the exact number of all agents\u27 visits to the cells. Each UAV is simulated as a hobbyist, fixed-wing airplane equipped with a generic set of actuators and a generic controller. Individual UAV failures are simulated following reliability Fault Trees. Each affected UAV is disabled and eliminated from the pool of active units. After each unit failure, the system generates a new topology. It produces a set of minimum-distance trees for each node (UAV) in the grid. The new trees will thus depict the rearrangement links as required after a node failure or if changes occur in the topology due to node movement. The model should generate parameters such as the number and location of compromised nodes, performance before and after the failure, and the estimated time of restitution needed to model the team\u27s resilience. The study addresses three research goals: identifying appropriate tools for modeling UAV scenarios, developing a model for assessing UAVs team resilience that overcomes previous studies\u27 limitations, and testing the model through multiple simulations. The study fills a gap in the literature as previous studies focus on system communication disruptions (i.e., node failures) without considering UAV unit reliability. This consideration becomes critical as using small, low-cost units prone to failure becomes widespread

    Data driven theory for knowledge discovery in the exact sciences with applications to thermonuclear fusion

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    In recent years, the techniques of the exact sciences have been applied to the analysis of increasingly complex and non-linear systems. The related uncertainties and the large amounts of data available have progressively shown the limits of the traditional hypothesis driven methods, based on first principle theories. Therefore, a new approach of data driven theory formulation has been developed. It is based on the manipulation of symbols with genetic computing and it is meant to complement traditional procedures, by exploring large datasets to find the most suitable mathematical models to interpret them. The paper reports on the vast amounts of numerical tests that have shown the potential of the new techniques to provide very useful insights in various studies, ranging from the formulation of scaling laws to the original identification of the most appropriate dimensionless variables to investigate a given system. The application to some of the most complex experiments in physics, in particular thermonuclear plasmas, has proved the capability of the methodology to address real problems, even highly nonlinear and practically important ones such as catastrophic instabilities. The proposed tools are therefore being increasingly used in various fields of science and they constitute a very good set of techniques to bridge the gap between experiments, traditional data analysis and theory formulation

    Simulating Maritime Chokepoint Disruption in the Global Food Supply

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    The three food crops of wheat, maize, and rice make up almost two-thirds of the world\u27s dietary energy needs. Of these three, just six countries provide 70% of the global supply. Furthermore, soybeans account for three-quarters of global livestock feed, and only three countries provide 80% of the global supply. Considering over half of the world\u27s exported supply of these four commodities are exported via maritime means, the free ow of marine traffic becomes paramount. Current models lack the ability to capture the inherent variance displayed in the maritime transport system, which can lead to inaccurate assumptions about how the system functions - assumptions that could ultimately bring chaos to an importing economy. To capture this inherent variance, a discrete-event simulation was built to better understand how disruptions in this system impact those who rely on its unhindered functionality. Monthly export data is used, and the maritime chokepoints of the Panama Canal, the Suez Canal, and the Strait of Gibraltar are modeled for disruption. Results indicate significant food shortages for all importers studied, with some receiving 97% less of a commodity in a given month. China is particularly sensitive to a closure of the Panama Canal in the months of September - January. Egypt and Spain could expect significant food decreases if the Strait of Gibraltar were to close in any month, with Spain experiencing its worst declines should a disruption occur in September. Marine traffic through the Strait of Malacca was also significantly impacted when any of the three chokepoints studied were closed

    Airborne Directional Networking: Topology Control Protocol Design

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    This research identifies and evaluates the impact of several architectural design choices in relation to airborne networking in contested environments related to autonomous topology control. Using simulation, we evaluate topology reconfiguration effectiveness using classical performance metrics for different point-to-point communication architectures. Our attention is focused on the design choices which have the greatest impact on reliability, scalability, and performance. In this work, we discuss the impact of several practical considerations of airborne networking in contested environments related to autonomous topology control modeling. Using simulation, we derive multiple classical performance metrics to evaluate topology reconfiguration effectiveness for different point-to-point communication architecture attributes for the purpose of qualifying protocol design elements

    System importance measures: A new approach to resilient systems-of-systems

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    Resilience is the ability to withstand and recover rapidly from disruptions. While this attribute has been the focus of research in several fields, in the case of system-of-systems (SoSs), addressing resilience is particularly interesting and challenging. As infrastructure SoSs, such as power, transportation, and communication networks, grow in complexity and interconnectivity, measuring and improving the resilience of these SoSs is vital in terms of safety and providing uninterrupted services. ^ The characteristics of systems-of-systems make analysis and design of resilience challenging. However, these features also offer opportunities to make SoSs resilient using unconventional methods. In this research, we present a new approach to the process of resilience design. The core idea behind the proposed design process is a set of system importance measures (SIMs) that identify systems crucial to overall resilience. Using the results from the SIMs, we determine appropriate strategies from a list of design principles to improve SoS resilience. The main contribution of this research is the development of an aid to design that provides specific guidance on where and how resources need to be targeted. Based on the needs of an SoS, decision-makers can iterate through the design process to identify a set of practical and effective design improvements. ^ We use two case studies to demonstrate how the SIM-based design process can inform decision-making in the context of SoS resilience. The first case study focuses on a naval warfare SoS and describes how the resilience framework can leverage existing simulation models to support end-to-end design. We proceed through stages of the design approach using an agent-based model (ABM) that enables us to demonstrate how simulation tools and analytical models help determine the necessary inputs for the design process and, subsequently, inform decision-making regarding SoS resilience. ^ The second case study considers the urban transportation network in Boston. This case study focuses on interpreting the results of the resilience framework and on describing how they can be used to guide design choices in large infrastructure networks. We use different resilience maps to highlight the range of design-related information that can be obtained from the framework. ^ Specific advantages of the SIM-based resilience design include: (1) incorporates SoS- specific features within existing risk-based design processes - the SIMs determine the relative importance of different systems based on their impacts on SoS-level performance, and suggestions for resilience improvement draw from design options that leverage SoS- specific characteristics, such as the ability to adapt quickly (such as add new systems or re-task existing ones) and to provide partial recovery of performance in the aftermath of a disruption; (2) allows rapid understanding of different areas of concern within the SoS - the visual nature of the resilience map (a key outcome of the SIM analysis) provides a useful way to summarize the current resilience of the SoS as well as point to key systems of concern; and (3) provides a platform for multiple analysts and decision- makers to study, modify, discuss and documentoptions for SoS
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