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[en] An algorithmic approach is presented that leads to reduction in the computational requirements for process system dynamic reliability and safety analysis using discrete state transition models through the utilization of vector processing and a sparse matrix technique. The new algorithm is demonstrated on an example system taken from boiling water reactors. The results show reduction in random access memory requirements by a factor of 20 and reduction in the computational time by a factor of 7 with respect to the original algorithm
[en] The project entitled, 'Uncertainty Quantification in the Reliability and Risk Assessment of Generation IV Reactors', was conducted as a DOE NERI project collaboration between Texas A and M University and The Ohio State University between March 2006 and June 2009. The overall goal of the proposed project was to develop practical approaches and tools by which dynamic reliability and risk assessment techniques can be used to augment the uncertainty quantification process in probabilistic risk assessment (PRA) methods and PRA applications for Generation IV reactors. This report is the Final Scientific/Technical Report summarizing the project.
[en] A user-friendly, interactive software package is described that can be used for fault diagnosis in dynamic systems. The methodology is based on the representation of system evolution in time as probability of transitions between sets of magnitude intervals in the state/parameter space. The software is developed in C++ for Windows NT platform. The display capabilities of the software and its implementation are illustrated using a second order system
[en] The continuous cell-to-cell mapping technique (CCCMT) is a recently proposed Markovian approach which can be used for the dynamic reliability and safety analysis of process control systems, as well as for the global analysis of nonlinear dynamic systems in general. A parametric study is performed on the computational efficiency of CCCMT as a function of various integration schemes. The results show that a variable stepsize scheme is desirable with a sufficient required precision. Among the integration schemes considered, a fourth order Runge-Kutta scheme seems to be preferable for short term simulations and the mid-point implicit scheme seems to be preferable for long term simulations or to obtain the steady-state system behavior
[en] A probabilistic approach is presented which can be used for the estimation of system parameters and unmonitored state variables towards model-based fault diagnosis in dynamic systems. The method can be used with any type of input-output model and can accommodate noisy data and/or parameter/modeling uncertainties. The methodology is based on Markovian representation of system dynamics in discretized state space. The example system used for the illustration of the methodology focuses on the intake, fueling, combustion and exhaust components of internal combustion engines. The results show that the methodology is capable of estimating the system parameters and tracking the unmonitored dynamic variables within user-specified magnitude intervals (which may reflect noise in the monitored data, random changes in the parameters or modeling uncertainties in general) within data collection time and hence has potential for on-line implementation
[en] The DSD (dynamic system doctor) is a system-independent interactive software under development for on-line state/parameter estimation in dynamic system. The DSD estimation algorithm uses a recursive technique based on the representation of the system dynamics in terms of transition probabilities between user-specified cells that partition the system parameter/state space during user-specified time intervals. The DSD is naturally suited to accommodate noise and modeling uncertainties. The DSD yields both the ranges and the probability distributions of the uncertainty in the estimated parameters/variables. Recent accomplishments in improving the code include development of a user interface module for model and partitioning data input, development of multi-threading capability, dynamic partitioning, and recursive partitioning. The DSD is currently capable of tracking several parameters/state variables per task (or system) for on-line monitoring; multiple tasks can be run. The algorithm is transparent to the user. The model and data input are accomplished through dialog windows. The software is highly transportable because of its self-installment/deinstallment capability. A current practical implementation is global xenon estimation in nuclear reactors in real time. Future applications will include in-core power monitoring
[en] Uncertainties in the initial conditions and parameters of process systems can lead to large variations in the predicted system performance, sometimes with catastrophic consequences. For systems with non-linear dynamics, often the only generally applicable approach to assess the impact of these uncertainties on system performance is a search through the operation and parameter range of interest by direct integration of the system equations. A methodology is presented that can be used to assess process reliability and safety under such uncertainties much faster than direct integration. The methodology extends the capabilities of a previously developed discrete state transition modeling approach to include capability for the determination of initial conditions that lead to desirable system operation. Implementation of the methodology on an example system taken from nuclear reactor dynamics shows that: a) the methodology can handle both small and large uncertainties in system parameters and initial conditions, and b) fast conventional approaches such as perturbation analysis may lead to the choice of more restrictive or narrower operational ranges for the system than that required by reliability/safety considerations
[en] The cell-to-cell-mapping technique (CCMT) models system evolution in terms of probability of transitions within a user-specified time interval (e.g., data-sampling interval) between sets of user-defined parameter/state variable magnitude intervals (cells). The cell-to-cell transition probabilities are obtained from the given linear or nonlinear plant model. In conjunction with monitored data and the plant model, the Dynamic System Doctor (DSD) software package uses the CCMT to determine the probability of finding the unmonitored parameter/state variables in a given cell at a given time recursively from a Markov chain. The most important feature of the methodology with regard to model-based fault diagnosis is that it can automatically account for uncertainties in the monitored system state, inputs, and modeling uncertainties through the appropriate choice of the cells, as well as providing a probabilistic measure to rank the likelihood of faults in view of these uncertainties. Such a ranking is particularly important for risk-informed regulation and risk monitoring of nuclear power plants. The DSD estimation algorithm is based on the assumptions that (a) the measurement noise is uniformly distributed and (b) the measured variables are part of the state variable vector. A new theoretical basis is presented for CCMT-based state/parameter estimation that waives these assumptions using a Bayesian interpretation of the approach and expands the applicability range of DSD, as well as providing a link to the conventional state/parameter estimation schemes. The resulting improvements are illustrated using a point reactor xenon evolution model in the presence of thermal feedback and compared to the previous DSD algorithm. The results of the study show that the new theoretical basis (a) increases the applicability of methodology to arbitrary observers and arbitrary noise distributions in the monitored data, as well as to arbitrary uncertainties in the model parameters; (b) leads to improvements in the estimation speed and accuracy; and (c) allows the estimator to be used for noise reduction in the monitored data. The connection between DSD and conventional state/parameter estimation schemes is shown and illustrated for the least-squares estimator, maximum likelihood estimator, and Kalman filter using a recently proposed scheme for directly measuring local power density in nuclear reactor cores
[en] The Light Water Reactor Sustainability (LWRS) Program is a research and development (R and D) program sponsored by the U.S. Department of Energy (DOE). The program is operated in close collaboration with industry R and D programs to provide the technical foundations for licensing and managing the long-term, safe, and economical operation of Nuclear Power Plants that are currently in operation. The LWRS Program focus is on longer-term and higher-risk/reward research that contributes to the national policy objectives of energy and environmental security. Advanced instruments and control (I and C) technologies are needed to support the safe and reliable production of power from nuclear energy systems during sustained periods of operation up to and beyond their expected licensed lifetime. This requires that new capabilities to achieve process control be developed and eventually implemented in existing nuclear assets. It also requires that approaches be developed and proven to achieve sustainability of I and C systems throughout the period of extended operation. The strategic objective of the LWRS Program Advanced Instrumentation, Information, and Control Systems Technology R and D pathway is to establish a technical basis for new technologies needed to achieve safety and reliability of operating nuclear assets and to implement new technologies in nuclear energy systems. This will be achieved by carrying out a program of R and D to develop scientific knowledge in the areas of: (1) Sensors, diagnostics, and prognostics to support characterization and prediction of the effects of aging and degradation phenomena effects on critical systems, structures, and components (SSCs); (2) Online monitoring of SSCs and active components, generation of information, and methods to analyze and employ online monitoring information; (3) New methods for visualization, integration, and information use to enhance state awareness and leverage expertise to achieve safer, more readily available electricity generation. As an initial step in accomplishing this effort, the Light Water Reactor Sustainability Workshop on Advanced Instrumentation, Information, and Control Systems and Human-System Interface Technologies was held March 20-21, 2009, in Columbus, Ohio, to enable industry stakeholders and researchers in identification of the nuclear industry's needs in the areas of future I and C technologies and corresponding technology gaps and research capabilities. Approaches for collaboration to bridge or fill the technology gaps were presented and R and D activities and priorities recommended. This report documents the presentations and discussions of the workshop and is intended to serve as a basis for the plan under development to achieve the goals of the I and C research pathway.
[en] The time-dependent unavailability of periodically tested and repaired aging components which have first failure densities satisfying the normal and the Weibull distributions with aging threshold are investigated using a generalized formula for surveillance testing and repair. The formalism is applied to the case of periodic, good-as-old surveillance testing with good-as-new repair. Actual aging data are used. The results show that aging can have significant impacts on the availability and reliability of a component and the system containing the component. More specifically: a) the time dependent unavailability of aging components can go through large swings with varying magnitude in time before stabilizing to the standard saw-tooth behavior of periodically tested components with constant failure rates, and b) approximating the unavailability by a constant failure rate can lead to large differences even when an average unavailability is calculated