A PROBABILISTIC MECHANISTIC APPROACH FOR ASSESSING THE RUPTURE FREQUENCY OF SMALL MODULAR REACTOR STEAM GENERATOR TUBES USING UNCERTAIN INPUTS FROM IN-SERVICE INSPECTIONS

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

Cross-layer system reliability assessment framework for hardware faults

System reliability estimation during early design phases facilitates informed decisions for the integration of effective protection mechanisms against different classes of hardware faults. When not all system abstraction layers (technology, circuit, microarchitecture, software) are factored in such an estimation model, the delivered reliability reports must be excessively pessimistic and thus lead to unacceptably expensive, over-designed systems. We propose a scalable, cross-layer methodology and supporting suite of tools for accurate but fast estimations of computing systems reliability. The backbone of the methodology is a component-based Bayesian model, which effectively calculates system reliability based on the masking probabilities of individual hardware and software components considering their complex interactions. Our detailed experimental evaluation for different technologies, microarchitectures, and benchmarks demonstrates that the proposed model delivers very accurate reliability estimations (FIT rates) compared to statistically significant but slow fault injection campaigns at the microarchitecture level.Peer ReviewedPostprint (author's final draft

Applying the General Path Model to Estimation of Remaining Useful Life

The ultimate goal of most prognostic systems is accurate prediction of the remaining useful life of individual systems or components based on their use and performance. This class of prognostic algorithms is termed Effects-Based or Type III Prognostics. Traditional individual-based prognostics involve identifying an appropriate degradation measure to characterize the system's progression to failure. A functional fit of this parameter is then extrapolated to a pre-defined failure threshold to estimate the remaining useful life of the system or component. This paper proposes a specific formulation of the General Path Model with dynamic Bayesian updating as one effects-based prognostic algorithm. The method is illustrated with an application to the prognostics challenge problem posed at PHM '08

"Making Safety Happen" Through Probabilistic Risk Assessment at NASA

NASA is using Probabilistic Risk Assessment (PRA) as one of the tools in its Safety & Mission Assurance (S&MA) tool belt to identify and quantify risks associated with human spaceflight. This paper discusses some of the challenges and benefits associated with developing and using PRA for NASA human space programs. Some programs have entered operation prior to developing a PRA, while some have implemented PRA from the start of the program. It has been observed that the earlier a design change is made in the concept or design phase, the less impact it has on cost and schedule. Not finding risks until the operation phase yields much costlier design changes and major delays, which can result in discussions of just accepting the risk. Risk contributors identified by PRA are not just associated with hardware failures. They include but are not limited to crew fatality due to medical causes, the environment the vehicle and crew are exposed to, the software being used, and the reliability of the crew performing required actions. Some programs have entered operation prior to developing a PRA, and while PRA can still provide a benefit for operations and future design trades, the benefit of implementing PRA from the start of the program provides the added benefit of informing design and reducing risk early in program development. Currently, NASAs International Space Station (ISS) program is in its 20th year of on-orbit operations around the Earth and has several new programs in the design phase preparing to enter the operation phase all of which have active (or living) PRAs. These programs incorporate PRA as part of their Risk-Informed, Decision-Making (RIDM) process. For new NASA human spaceflight programs discussion begins with mission concept, establishing requirements, forming the PRA team, and continues through the design cycles into the operational phase. Several examples of PRA related applications and observed lessons are included

National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program, 1989, volume 1

The 1989 Johnson Space Center (JSC) National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program was conducted by Texas A and M University and JSC. The 10-week program was operated under the auspices of the ASEE. The program at JSC, as well as the programs at other NASA Centers, was funded by the Office of University Affairs, NASA Headquarters, Washington, D.C. The objectives of the program, which began nationally in 1964 and at JSC in 1965, are: (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate an exchange of ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of participants' institutions; and (4) to contribute to the research objective of the NASA Centers

Practical Methods for Optimizing Equipment Maintenance Strategies Using an Analytic Hierarchy Process and Prognostic Algorithms

Many large organizations report limited success using Condition Based Maintenance (CbM). This work explains some of the causes for limited success, and recommends practical methods that enable the benefits of CbM. The backbone of CbM is a Prognostics and Health Management (PHM) system. Use of PHM alone does not ensure success; it needs to be integrated into enterprise level processes and culture, and aligned with customer expectations. To integrate PHM, this work recommends a novel life cycle framework, expanding the concept of maintenance into several levels beginning with an overarching maintenance strategy and subordinate policies, tactics, and PHM analytical methods. During the design and in-service phases of the equipment’s life, an organization must prove that a maintenance policy satisfies specific safety and technical requirements, business practices, and is supported by the logistic and resourcing plan to satisfy end-user needs and expectations. These factors often compete with each other because they are designed and considered separately, and serve disparate customers. This work recommends using the Analytic Hierarchy Process (AHP) as a practical method for consolidating input from stakeholders and quantifying the most preferred maintenance policy. AHP forces simultaneous consideration of all factors, resolving conflicts in the trade-space of the decision process. When used within the recommended life cycle framework, it is a vehicle for justifying the decision to transition from generalized high-level concepts down to specific lower-level actions. This work demonstrates AHP using degradation data, prognostic algorithms, cost data, and stakeholder input to select the most preferred maintenance policy for a paint coating system. It concludes the following for this particular system: A proactive maintenance policy is most preferred, and a predictive (CbM) policy is more preferred than predeterminative (time-directed) and corrective policies. A General Path prognostic Model with Bayesian updating (GPM) provides the most accurate prediction of the Remaining Useful Life (RUL). Long periods between inspections and use of categorical variables in inspection reports severely limit the accuracy in predicting the RUL. In summary, this work recommends using the proposed life cycle model, AHP, PHM, a GPM model, and embedded sensors to improve the success of a CbM policy

Through life costing in defence electronic systems: an integrated data-driven multi-level approach

Cost estimating is a business process that is critical to the defence sector, where many products have low volumes and long life cycles. The nature of a defence system is often unique (for example, a naval platform) which consists of a number of sub-systems and components. For the design of such a system cost estimating is a critical task, in particular the requirement to predict the cost throughout the systems lifetime. The aim of this paper therefore is to discuss an integrated approach that provides a general framework for through life costing in defence systems via the development of: (1) a generic data library to support designers and cost estimators, (2) data searching and transfer mechanisms to support a top-down and bottom-up hybrid cost modelling approach, (3) capturing reliability data to support product services. The paper is divided into several sections, first, a review of relevant research projects concerning integration and data capture for cost modelling. This is followed by a section, which highlights problems of performing cost estimates for low volume products, and subsequently the proposed solution, methods of cost estimation and example applications. Perhaps most importantly, the methods created in this research are able to enhance decision-making and accelerate the responsiveness of the business bidding process

Small Modular Reactors (SMR) Probabilistic Risk As

A key area of the Small Modular Reactor (SMR) Pro