COMPARISON OF VALVE FAILURE RATES ESTIMATED FROM FIELD FAILURE DATA TO THOSE PREDICTED BY CALIBRATED FMEDA
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HARRIS, STEPHEN P.↗
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Failure Rate Estimates for Passive Mechanical Components
Fusion design projects that reach the stage of design reviews typically require reliability, availability, maintainability and inspectability (RAMI) assessments. While many kinds of reliability and maintainability data are needed for a wide variety of fusion components to quantify a RAMI assessment, one of the types of components requiring data is the passive mechanical component. Passive mechanical components can be items such as metal plates, rods, shafts, brackets, bolts, and other items. Generally these passive components tend to have low failure rates since they are simple designs, have no or few moving parts, and do not need control signals to perform their design function. However, there are no widely recognized datasets for such components, so analysts must use multiple datasets and judgment for failure rates. This report gives recommended failure rates for various failure modes of these components, and gives suggested failure rate multipliers to apply to these failure rates if they reside in harsh environments. If the component resides far from the tokamak then typical industrial failure rates would suffice. Components near the tokamak find conditions can be demanding; similar to those in a fission reactor containment building. Some component failure rates can be modified if the components reside inside the tokamak.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Markov and semi-Markov processes as a failure rate
In this paper the reliability function is defined by the stochastic failure rate process with a non negative and right continuous trajectories. Equations for the conditional reliability functions of an object, under assumption that the failure rate is a semi-Markov process with an at most countable state space are derived. A proper theorem is presented. The linear systems of equations for the appropriate Laplace transforms allow to find the reliability functions for the alternating, the Poisson and the Furry-Yule failure rate processes.
97 MATHEMATICS AND COMPUTING↗
Probabilistic Methods for Cyclical and Coupled Systems with Changing Failure Rates
Advancements in nuclear system designs with automated control features provide many benefits, but can lead to complex coupled systems and dynamic failure scenarios. This is especially true for microreactor designs where components are not expected to be replaced during the reactor’s lifetime. Hence, the life of the system, in addition to the safety, needs to be evaluated. Modeling these sequences of time-dependent events requires addressing cyclical processes and changing failure rates in ways that represent the actual system dynamics in contrast to a single sampling for a component’s time to failure. This research presents two distinct analytical methods for several failure distributions that evaluate a final time to failure used for different scenarios where the time to failure must be sampled multiple times. The first method is used when evaluating a component whose failure rate increases due to an outside event after the initial sampling but before the initially sampled time to failure. The second method is used when evaluating multiple identical components or a component that has been replaced with a new identical version before the second sampling. The two methods were implemented in a few representative case studies developed in the dynamic probabilistic risk assessment tool Event Modeling Risk Assessment using Linked Diagrams. Overall, this paper provides guidelines on how these approaches give a more realistic and accurate dynamic probabilistic risk assessment of complex systems.
97 MATHEMATICS AND COMPUTING↗
Fitting of Failure Rate Data to a Closed Form Solution using Gamma-Poisson Method of Moments
Due to the responsibility and gravity of estimation of nuclear power plant failure rates, it is required that there be consistent and accurate methodology for describing the likelihood of an event occurring. However, the methodology currently employed can vary dependent on the data, as well as subjective conjecture from the expert performing the analysis. The current implementation of the empirical Bayes method to a Gamma-Poisson (GaP) distribution utilizes algorithms to solve for the parameters that do not provide consistent answers. Additionally, to achieve a distribution of the likelihood, the variance of each parameter of the GaP must be determined. There does not exist an exact solution to one of the parameters variance and is typically estimated using techniques like the Kass-Steffey adjustment. Thus a new approach to the problem is proposed, built upon the method of moments for a negative binomial, based upon the work of Bradlow. Using the method of moments approach, we are able to achieve a closed form estimation of the mean and variance for each parameter in the negative binomial distribution. Due to the relationship between the negative binominal and GaP distribution, results can be made between the distributions. The hyper-priors defined assume beta prime distribution which is appropriately informed; the results of this application translate back to the Gamma distribution for easy utilization in SAPHIRE. Additionally several cases are explored using publically available NRC data that considers zero inflated, over dispersed, and Poisson data.
Failure Rate↗
Modeling the high-strain-rate failure response of granular materials.
Abstract not provided.
Demaske, Brian James↗
GPU age-aware scheduling to improve the reliability of leadership jobs on Titan
In 2015, OLCF's Titan supercomputer experienced a significant increase in GPU related job failures. The impact on jobs was serious and OLCF decided to replace ~50% of the GPUs. Unfortunately, jobs using more than 20% of the machine (i.e., leadership jobs) continued to encounter higher levels of application failures. These jobs contained significant amounts of both the low-failure rate and high-failure rate GPUs. The impacts of these failures are more adversely felt by leadership jobs due to longer wait times, runtimes, and higher charge rates. In this work, we have designed techniques to increase the use of low-failure GPUs in leadership jobs through targeted resource allocation. We have employed two complementary techniques, updating both the system ordering and the allocation mechanisms. Using simulation, the application of these techniques resulted in a 33% increase in low-failure GPU hours being assigned to leadership jobs. Our GPU Age-Aware Scheduling has been used in production on Titan since July of 2017.
Zimmer, Christopher↗
Facilitating Data Collection of Maintenance Events to Populate the Hydrogen Component Reliability Database (HyCReD)
The Hydrogen Component Reliability Database (HyCReD) is a collaborative project between the National Renewable Energy Laboratory, the University of Maryland, and hydrogen stakeholders to improve safety and reliability for hydrogen facilities by implementing component reliability data taxonomies that support hydrogen infrastructure failure rate analysis. The project aims to quantify failure rates of hydrogen components through high-quality data collection and analysis on root causes and maintenance needed. HyCReD provides a common database for cataloging hydrogen component failures which exists for reliability research in many other mature industries [2]. The database fills a gap for the hydrogen community by providing a scientifically rigorous approach to quantitative risk assessment (QRA), prognostic health management (PHM), and reliability-centered maintenance (RCM) analysis. High level results will be aggregated and anonymized to protect company sensitive information; detailed results will be used to help address issues of hydrogen components. These advanced analytics will support accelerated deployment of hydrogen infrastructure by enabling better: design and safety of projects (safety codes and standards development), infrastructure reliability and cost (component failure rates, maintenance protocols), and component R&D needs (robust supply chain). A key to a successful HyCReD implementation is facilitating the ease of reporting and data quality in the database that can be used for analysis. Maintenance data was a previously identified gap in initial efforts to populate and validate the database taxonomies [3]. Collection of maintenance data will be instrumental in identifying failure modes and rates, identifying incipient component failures or reduced performance, cataloging best practices for maintenance routines and methods for prognostic health management, and quantifying the risk and effect of different failure modes. Several key priorities are identified for streamlined data collection to achieve quality and detailed failure data: Applicability, Ease of Use, Accessibility, and Information Security. The HyCReD team has now begun deployment of the database to several companies and groups that have signed non-disclosure agreements to facilitate the data collection of failures in industry hydrogen refueling station infrastructure. This paper will provide an update into the process of HyCReD deployment including the development of a coding guide for facility personnel to reference and ensure data quality and consistency from one station to another as well as implementation of contextually dependent data fields of system taxonomy and formatted entries to provide ease of use. The goal is to communicate the lessons learned from the roll-out to technicians and engineers in the field, and the addition of need for high level of security to protect all stakeholders.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Correlation Between Weather Alerts and Grid Component Failures for Grid Alert
Weather events cause most grid failures. Often, we even get notifications on our phones to take cover or be prepared for an imminent event. If electric grid utilities had a similar warning that also included probable scenarios and the equipment involved, they could prepare and minimize the effects. Recent research at Idaho National Laboratory into electric grid risk analysis methods resulted in a tool that allows for the development of the most likely scenarios given failure probabilities of grid components. INL has a project with the U.S. Department of Energy’s Cybersecurity, Energy Security, and Emergency Response (CESER) program to develop a Grid Alert application that receives messages from the existing emergency alert system, filters and determines components possibly affected by the emergency event, calculates probable scenarios uses MASTERRI and then notifies the utility if there is significant risk. Historical failure data of elements that comprise the U.S. electric grid have been compiled by utilities and organizations such as the international regulatory body North American Electric Reliability Corporation (NERC). Nominal failure rates are obtained from this data. To make this tool possible, estimated failure rates are needed for different component types given the alert type, severity, and location. Historic weather-related grid element failures are correlated with historic weather events from Integrated Public Alert & Warning System (IPAWS). These correlated events and failures are used along with Bayesian updates from the historical norms to provide a modified failure rate for grid elements in the alert areas and calculate probable scenarios. This discusses the Grid Alert project plan but focuses on the data gathered and process used in determining failure rates for possible grid failure scenarios.
24 - POWER TRANSMISSION AND DISTRIBUTION↗
Review and categorization of existing studies on the estimation of probabilistic failure metrics for Reactor Coolant Pressure Boundary piping and steam generator tubes in Nuclear Power Plants
This article presents the first output of the International Atomic Energy Agency (IAEA) Coordinated Research Project (CRP), titled Methodology for Assessing Pipe Failure Rates in Advanced Water-Cooled Reactors (AWCRs), launched in 2018. This IAEA CRP is aimed at developing a good-practices document for estimating the probabilistic failure metrics of piping in AWCRs. The reliability of piping that comprise the Reactor Coolant Pressure Boundary (RCPB) is important for maintaining safe and stable operations of Nuclear Power Plants (NPPs) because failure of those piping components could lead to undesirable consequences, such as plant shutdown, costly repair, the occurrence of Loss-of-Coolant Accidents (LOCAs) and, possibly, subsequent core damage or large release events. Probabilistic failure metrics (e.g., failure rate, failure frequency, or failure probability) of RCPB components are the key inputs to the Probabilistic Safety/Risk Assessment (PSA/PRA) and risk management of NPPs. The estimation of probabilistic failure metrics, however, is challenging, especially for AWCRs, due to the lack of operating experience. Therefore, as the first step of the IAEA CRP activities, this paper is developed to provide a literature review of the existing studies (from 2000 to April 2019) on the estimation of probabilistic failure metrics for RCPB piping and Steam Generator (SG) tubes of NPPs and to categorize them based on four criteria: (1) explicitness of incorporation of physical failure mechanisms; (2) types of failure characterization; (3) types of physical models for degradation; and (4) explicitness of consideration of maintenance. The existing studies are also analyzed from the viewpoint of the following key aspects: (i) uncertainty analysis, (ii) sensitivity analysis, (iii) validation strategies, and (iv) the areas of applications.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
redMaPPer – III. A detailed comparison of the Planck 2013 and SDSS DR8 redMaPPer cluster catalogues
We compare the Planck Sunyaev–Zeldovich (SZ) cluster sample (PSZ1) to the Sloan Digital Sky Survey (SDSS) redMaPPer catalogue, finding that all Planck clusters within the redMaPPer mask and within the redshift range probed by redMaPPer are contained in the redMaPPer cluster catalogue. These common clusters define a tight scaling relation in the richness-SZ mass (λ–MSZ) plane, with an intrinsic scatter in richness of σλ|MSZ=0.266±0.017. The corresponding intrinsic scatter in true cluster halo mass at fixed richness is ≈21 percent. The regularity of this scaling relation is used to identify failures in both catalogues. The failure rates for redMaPPer and PSZ1 1.2 percent and 14.7 percent, respectively. The PSZ1 failure rates decreases to 9.8 percent after removing incorrect redshifts that were drawn from the literature. We note the failure rates in the PSZ1 from this analysis are specific to the SDSS overlap region, and may not be indicative of failure rates over the full Planck survey. We have further identified five PSZ1 sources that suffer from projection effects (multiple rich systems along the line of sight of the SZ detection) and 17 new high-redshift (z ≳ 0.6) cluster candidates of varying degrees of confidence.
Astrophysics,ASTRO↗
Prediction of Safety Level of Low-Head and Ownerless Hydraulic Structures
Methods of systems reliability theory are applied to the results of an expert inspection of more than 1000 low-head hydraulic power systems in the Moscow region. The rates of failure of homogeneous earth dams are estimated. A state graph of a hydraulic structure, represented as a system that passes through discrete states from a normal to a dangerous level without maintenance or repair, is proposed. The constant failure rates for these states are calculated. The probabilistic characteristics of the safety level of low-head earth dams are predicted.
42 ENGINEERING↗
A Causal Approach to Integrate Component Health Data into System Reliability Models
Two of the challenges of current plant reliability approaches are the ability to integrate plant health data, and to support decision making. Condition based data and diagnostic/prognostic information are in fact not considered into plant reliability models to inform system engineers on the most critical components. Currently, the propagation of quantitative health data from the component to the system level is a challenge given the diverse nature/structure of the data. On the other hand, plant reliability methods (which are typically based on fault-trees or reliability block diagrams) can effectively propagate data from the component to the system level, but values of failure rates or failure probabilities are an approximated integral representation of the past industry-wide operational experience, and it neglects the present component health status (e.g., diagnostic and condition-based data) and health projection (when available from prognostic data). Our first claim is that system reliability models should propagate health information from the component to the system/plant level in order to provide a quantitative snapshot of system/plant health and identify the most critical components. Our second claim is that component health should be informed solely by that specific component current and historical performance data and should not be an approximated integral representation of the past industry-wide operational experience. This paper is directly supporting these two claims by proposing a different approach to perform reliability modeling which relies on available component diagnostic, prognostic and condition-based data to measure component health, and it propagates this information through fault tree models. The propagation of health data from the component to the system level is performed not in terms of probability, but in terms of margins where margin is defined as the “distance” between the present actual status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated to a component performance, a margin-based approach focuses on the cause of an undesired component performance (i.e., component health). Hence, thinking of reliability in terms of margins implies decision making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical components.
97 MATHEMATICS AND COMPUTING↗
Coupling RAVEN to SAPHIRE for Performing Time Dependent Probabilistic Risk Assessment
Cost Risk Analysis Framework Tool (CRAFT) was recently developed by Idaho National Laboratory (INL) to perform economical risk analysis by combining physics, risk and cost information of components in a Nuclear Power Plant (NPP) that can be quickly deployable through the nuclear industry to evaluate the risk associated to capital projects and asset management [1]. In this framework, RAVEN is coupled with SAPHIRE to perform the time dependent risk/safety assessment of NPPs. Traditionally, the risk associated to the NPPs can be determined by classical Probabilistic Risk Assessment (PRA) tools, such as SAPHIRE, under the assumption that all failure rates are constant over time. In some circumstances, however, the assumption is rendered invalid since that the failure rate of a component may gradually change in a systematic way, for example, because of aging and degradation. In addition, recent data analysis in [2] has suggested decreasing values of failure rates for several important components of NPPs. A PRA that includes these components should employ the time-dependent failure rates in order to accurately estimate the risk at any given time. This time-dependent PRA is achieved by the coupling between RAVEN and SAPHIRE.
42 ENGINEERING↗
Coupling RAVEN to SAPHIRE for Performing Time Dependent Probabilistic Risk Assessment
Cost Risk Analysis Framework Tool (CRAFT) was recently developed by Idaho National Laboratory (INL) to perform economical risk analysis by combining physics, risk and cost information of components in a Nuclear Power Plant (NPP) that can be quickly deployable through the nuclear industry to evaluate the risk associated to capital projects and asset management [1]. In this framework, RAVEN is coupled with SAPHIRE to perform the time dependent risk/safety assessment of NPPs. Traditionally, the risk associated to the NPPs can be determined by classical Probabilistic Risk Assessment (PRA) tools, such as SAPHIRE, under the assumption that all failure rates are constant over time. In some circumstances, however, the assumption is rendered invalid since that the failure rate of a component may gradually change in a systematic way, for example, because of aging and degradation. In addition, recent data analysis in [2] has suggested decreasing values of failure rates for several important components of NPPs. A PRA that includes these components should employ the time-dependent failure rates in order to accurately estimate the risk at any given time. This time-dependent PRA is achieved by the coupling between RAVEN and SAPHIRE.
42 ENGINEERING↗
Natural Hazard Forecast Alert Grid Risk System
Weather events cause most power outages. Often, we even get notifications on our phones to take cover or be prepared for an imminent event. If electric grid utilities had a similar warning that also included probable scenarios and the equipment involved, they could prepare and minimize the effects. Idaho National Laboratory had a project with the U.S. Department of Energy’s Cybersecurity, Energy Security, and Emergency Response program to develop a grid alert application that receives messages from the existing emergency alert system, filters and determines components possibly affected by the emergency event, calculates probable scenarios using MASTERRI (Modeling And Simulation for Targeted Reliability and Resilience Improvement). For high-risk events, the application can then send alert links to subscribed electric distribution utility operations staff to allow them to see and evaluate the scenarios and the impact in a web based interactive map tool. This proof of concept application used data from utilities and organizations, such as the international regulatory body North American Electric Reliability Corporation, which have complied historical failure data of elements that comprise the U.S. electric grid. Nominal failure rates are obtained from this data. To make this tool possible, estimated failure rates were calculated for different component types given the alert type, severity, and location. Historic weather-related grid element failures were correlated with historic weather events from the Integrated Public Alert & Warning System. These correlated events and failures are used along with Bayesian updates from the historical norms to provide a modified failure rate for grid elements in the alert areas and calculate probable scenarios. Working with an industry collaborator, actual grid models and data were used for demonstration cases. This report outlines the work performed for this project.
24 - POWER TRANSMISSION AND DISTRIBUTION↗
Enhanced Component Performance Study: Turbine Driven Pumps 1998–2022
This report presents an enhanced performance evaluation of turbine driven pumps (TDPs) at U.S. commercial nuclear power plants. The data used in this study are based on the operating experience failure reports from calendar year 1998 through 2022 as reported in the Institute of Nuclear Power Operations (INPO) Industry Reporting and Information System (IRIS). The TDP failure modes considered for standby systems are fail to start (FTS), fail to run (FTR) for one hour of operation (FTR=1H), FTR after one hour of operation (FTR>1H), and for normally running systems FTS and FTR. An eight hour unreliability estimate is also calculated and trended. The component reliability estimates and the reliability data are trended for the most recent 10 year period while yearly estimates for reliability are provided for the entire study period. No increasing trends were identified for TDPs for the most recent 10 year period: The following decreasing trends were identified for TDPs for the most recent 10 year period: • Standby TDP FTR>1H failure rate • Normally running TDP FTR failure rate • Standby TDP unavailability • Standby MDP total unreliability (8-hour mission) • Standby TDP frequency of start demands (demands per reactor year) • Standby TDP frequency of FTR=1H hours (hours per reactor year) • Standby TDP frequency of FTR>1H events (failures per reactor year) • Normally running TDP frequency of start demands • Normally running TDP frequency of run hours • Normally running TDP frequency of FTR events.
99 GENERAL AND MISCELLANEOUS↗
Time-Dependent Reliability Analysis of Nuclear Hybrid Energy Systems
This paper focuses on developing a framework for comprehensive time-dependent reliability analysis of a nuclear hybrid energy system (NHES) design. Subsystem interactions of this complex integrated system under stochastic electricity demand and load-following operation capabilities requires dynamic reliability assessment of the NHES in component, subsystem and system level. In order to capture the dynamic operational behavior of critical systems and components, a detailed thermal hydraulic model of the NHES is generated using Modelica and tested under different electricity demand histories generated using RAVEN. The physical data (e.g., valve position, flow rates, inlet outlet water temperatures) gathered from Modelica used to calculate time-dependent failure rates through fitting data into the piecewise Weibull distribution. Optimum maintenance interval is calculated, and maintenance cost estimates based on the calculated reliability metric is presented for the selected component. Component time-dependent failure rates fed into the subsystem reliability model built with non-Markovian Stochastic Petri Nets. The component and subsystem failure rates are calculated and updated in every hour to characterize the system behavior appropriately and to understand operational reliability of the NHES design in a given time.
Guler Yigitoglu, Askin↗