A. James Clark School of Engineering
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Item A PROBABILISTIC MECHANISTIC APPROACH FOR ASSESSING THE RUPTURE FREQUENCY OF SMALL MODULAR REACTOR STEAM GENERATOR TUBES USING UNCERTAIN INPUTS FROM IN-SERVICE INSPECTIONS(2011) Chatterjee, Kaushik; Modarres, Mohammad; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)One of the significant safety issues in nuclear power plants is the rupture of steam generator tubes leading to the loss of radioactive primary coolant inventory and establishment of a path that would bypass the plant's containment structure. Frequency of steam generator tube ruptures is required in probabilistic safety assessments of pressurized water reactors to determine the risks of radionuclide release. The estimation of this frequency has traditionally been based on non-homogeneous historical data that are not applicable to small modular reactors consisting of new steam generator designs. In this research a probabilistic mechanistic-based approach has been developed for assessing the frequency of steam generator tube ruptures. Physics-of-failure concept has been used to formulate mechanistic degradation models considering the underlying degradation conditions prevailing in steam generators. Uncertainties associated with unknown or partially known factors such as material properties, manufacturing methods, and model uncertainties have been characterized, and considered in the assessment of rupture frequency. An application of the tube rupture frequency assessment approach has been demonstrated for tubes of a typical helically-coiled steam generator proposed in most of the new small modular reactors. The tube rupture frequency estimated through the proposed approach is plant-specific and more representative for use in risk-informed safety assessment of small modular reactors. Information regarding the health condition of steam generator tubes from in-service inspections may be used to update the pre-service estimates of tube rupture frequency. In-service inspection data are uncertain in nature due to detection uncertainties and measurement errors associated with nondestructive evaluation methods, which if not properly accounted for, can result in over- or under-estimation of tube rupture frequency. A Bayesian probabilistic approach has been developed in this research that combines prior knowledge on defects with uncertain in-service inspection data, considering all the associated uncertainties to give a probabilistic description of the real defect size and density in the tubes. An application of the proposed Bayesian approach has been provided. Defect size and density estimated through the proposed Bayesian approach can be used to update the pre-service estimates of tube rupture frequency, in order to support risk-informed maintenance and regulatory decision-making.Item PROGNOSTICS OF POLYMER POSITIVE TEMPERATURE COEFFICIENT RESETTABLE FUSES(2011) Cheng, Shunfeng; Pecht, Michael G; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Polymer positive-temperature-coefficient (PPTC) resettable fuse has been used to circuit-protection designs in computers, automotive circuits, telecommunication devices, and medical devices. PPTC resettable fuse can trip from low resistance to high resistance under over-current conditions. The increase in the resistance decreases the current and protects the circuit. After the abnormal current is removed, and/or power is switched off, the fuse resets to low resistance stage, and can be continuously operated in the circuit. The resettable fuse degrades with the operations resulting in loss or abnormal function of the protection of circuit. This thesis is focused on the prognostics methods for resettable fuses to provide an advance warning of failure and to predict the remaining useful life. The failure precursor parameters are determined first by systematic analysis using failure modes, mechanisms, and effects analysis (FMMEA) followed by a series of experiments to verify these parameters. Then the causes of the observed failures are determined by failure analyses, including the analyses of interconnections between different parts, the microstructures of the polymer composite, the properties (such as crystallinity) of the polymer composite, and the coefficient of thermal expansion (CTE) of different parts. The revealed failure causes include the cracks and gaps between different parts, the agglomerations of the carbon black particles, the change in crystallinity of the polymer composite, and the CTE-mismatches between different parts. Cross validation (CV) sequential probability ratio test (CVSPRT) is developed to detect anomalies. CV methods are introduced into SPRT to determine the model parameters without the need of experience and reduce the false and missed alarms. A moving window training updating based dynamic model parameter optimization (MW-DMPO) n-steps-ahead prognostics method is developed to predict the failure. MW methods update the training data for prediction models by a moving window to contain the latest degradation information/data and improve the prediction accuracy. For each updating of the training data, the model parameters for data-trending model are updated dynamically. Based on the developed MW-DMPO method, a MW cross validation support vector regression (MW-CVSVR) n-steps-ahead prediction is developed to predict failures of PPTC resettable fuses in this thesis. The cross validation method is used to determine the proper SVR model parameters. The CVSPRT anomaly detection method and MW-DMPO n-steps-ahead prognostics method developed in this thesis can be extended as general methods for anomaly detection and failure prediction.