410,449 research outputs found

    Integrated Design Tools for Embedded Control Systems

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    Currently, computer-based control systems are still being implemented using the same techniques as 10 years ago. The purpose of this project is the development of a design framework, consisting of tools and libraries, which allows the designer to build high reliable heterogeneous real-time embedded systems in a very short time at a fraction of the present day costs. The ultimate focus of current research is on transformation control laws to efficient concurrent algorithms, with concerns about important non-functional real-time control systems demands, such as fault-tolerance, safety,\ud reliability, etc.\ud The approach is based on software implementation of CSP process algebra, in a modern way (pure objectoriented design in Java). Furthermore, it is intended that the tool will support the desirable system-engineering stepwise refinement design approach, relying on past research achievements ¿ the mechatronics design trajectory based on the building-blocks approach, covering all complex (mechatronics) engineering phases: physical system modeling, control law design, embedded control system implementation and real-life realization. Therefore, we expect that this project will result in an\ud adequate tool, with results applicable in a wide range of target hardware platforms, based on common (off-theshelf) distributed heterogeneous (cheap) processing units

    Issues in Assessing Short-Term Water Supply Capabilities of Reservoir Systems

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    The Texas Commission on Environmental Quality (TCEQ) uses a Water Availability Modeling System (WAM) to support long-term regional and statewide water resources planning and management. The water availability studies are based on the modeling capabilities of the Water Rights Analysis Package (WRAP). This research improves the understanding of decision support tools for short-term river basin management. Current reservoir storage levels must be considered to assess short-term frequencies and reliabilities. Conditional reliability modeling (CRM) is used to assess the likelihood of meeting targets for instream flow, reservoir storage, water supply diversion and hydroelectric power generation in the near future (next month to next several years), conditioned upon preceding storage. This study uses data for the Brazos River Basin from the TCEQ WAM System to assess key complexities of water supply reliability analysis in general and conditional reliability modeling in particular. These complexities include uncertainties associated with river basin hydrology, estimating yield-reliability relationships for individual reservoirs and multiple reservoir systems, conventional long-term planning versus short-term adaptive management and other modeling and analysis issues. The modeling capabilities of WRAP were expanded to support near real-time operation of dams under various stream flow conditions. The sensitivity to changes in modeling options is assessed for short and long-term simulations. Traditional and newly developed methodologies for estimating firm yields and water supply reliabilities are evaluated. Guidelines are developed regarding the practical application of firm yield analyses and conditional reliability modeling. Important applications of this research include real-time decision support during drought and routinely recurring operational planning activities. A case study of the drought of 2009 uses the CRM features of WRAP for these applications

    Bayesian Network Approach to Assessing System Reliability for Improving System Design and Optimizing System Maintenance

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    abstract: A quantitative analysis of a system that has a complex reliability structure always involves considerable challenges. This dissertation mainly addresses uncertainty in- herent in complicated reliability structures that may cause unexpected and undesired results. The reliability structure uncertainty cannot be handled by the traditional relia- bility analysis tools such as Fault Tree and Reliability Block Diagram due to their deterministic Boolean logic. Therefore, I employ Bayesian network that provides a flexible modeling method for building a multivariate distribution. By representing a system reliability structure as a joint distribution, the uncertainty and correlations existing between system’s elements can effectively be modeled in a probabilistic man- ner. This dissertation focuses on analyzing system reliability for the entire system life cycle, particularly, production stage and early design stages. In production stage, the research investigates a system that is continuously mon- itored by on-board sensors. With modeling the complex reliability structure by Bayesian network integrated with various stochastic processes, I propose several methodologies that evaluate system reliability on real-time basis and optimize main- tenance schedules. In early design stages, the research aims to predict system reliability based on the current system design and to improve the design if necessary. The three main challenges in this research are: 1) the lack of field failure data, 2) the complex reliability structure and 3) how to effectively improve the design. To tackle the difficulties, I present several modeling approaches using Bayesian inference and nonparametric Bayesian network where the system is explicitly analyzed through the sensitivity analysis. In addition, this modeling approach is enhanced by incorporating a temporal dimension. However, the nonparametric Bayesian network approach generally accompanies with high computational efforts, especially, when a complex and large system is modeled. To alleviate this computational burden, I also suggest to building a surrogate model with quantile regression. In summary, this dissertation studies and explores the use of Bayesian network in analyzing complex systems. All proposed methodologies are demonstrated by case studies.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

    Continuous-time Look-Ahead Scheduling of Energy Storage in Regulation Markets

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    Energy storage (ES) devices offer valuable flexibility services, including regulation reserve, in power systems operation that could improve the reliability and cost-efficiency of systems with high penetration of renewable energy resources. In this paper, a continuous-time look-ahead regulation capacity scheduling model is proposed, which more accurately models and schedules the regulation capacity trajectories provided by generating units and ES devices in real-time power systems operation. A function space solution method is proposed to reduce the dimensionality of the continuous-time problem by modeling the parameter and decision trajectories in a function space formed by Bernstein polynomials, which converts the continuous-time problem into a linear programming problem. Numerical results, conducted on the IEEE Reliability Test System, show lower operation cost and less regulation scarcity events in real-time power systems operation due to efficient deployment of the ES flexibility in regulation markets

    AGENT AUTONOMY APPROACH TO PROBABILISTIC PHYSICS-OF-FAILURE MODELING OF COMPLEX DYNAMIC SYSTEMS WITH INTERACTING FAILURE MECHANISMS

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    A novel computational and inference framework of the physics-of-failure (PoF) reliability modeling for complex dynamic systems has been established in this research. The PoF-based reliability models are used to perform a real time simulation of system failure processes, so that the system level reliability modeling would constitute inferences from checking the status of component level reliability at any given time. The "agent autonomy" concept is applied as a solution method for the system-level probabilistic PoF-based (i.e. PPoF-based) modeling. This concept originated from artificial intelligence (AI) as a leading intelligent computational inference in modeling of multi agents systems (MAS). The concept of agent autonomy in the context of reliability modeling was first proposed by M. Azarkhail [1], where a fundamentally new idea of system representation by autonomous intelligent agents for the purpose of reliability modeling was introduced. Contribution of the current work lies in the further development of the agent anatomy concept, particularly the refined agent classification within the scope of the PoF-based system reliability modeling, new approaches to the learning and the autonomy properties of the intelligent agents, and modeling interacting failure mechanisms within the dynamic engineering system. The autonomous property of intelligent agents is defined as agent's ability to self-activate, deactivate or completely redefine their role in the analysis. This property of agents and the ability to model interacting failure mechanisms of the system elements makes the agent autonomy fundamentally different from all existing methods of probabilistic PoF-based reliability modeling. 1. Azarkhail, M., "Agent Autonomy Approach to Physics-Based Reliability Modeling of Structures and Mechanical Systems", PhD thesis, University of Maryland, College Park, 2007
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