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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Modeling interconnections of safety and financial performance of nuclear power plants, part 3: Spatiotemporal probabilistic physics-of-failure analysis and its connection to safety and financial performance

Here, this paper is a byproduct of a line of research by the authors to analyze interrelationships of safety and financial performance of nuclear power plants (NPPs). The result of this line of research is summarized in three parts: Part 1 covers a categorical review of relevant literature and the theoretical bases that support the methodological developments in Part 2. Part 2 introduces an Integrated Enterprise Risk Management (I-ERM) methodological framework to quantify the interconnections of safety and financial performance with a focus on operation and maintenance (O&M) of NPPs. Part 2 has also demonstrated the applicability and values of the I-ERM methodology through an NPP case study. This paper is Part 3, where detailed development and implementation of one of the I-ERM modules, i.e., probabilistic physics-of-failure (PPoF) analysis, and its connection with safety and financial performance is reported. In this article, the physical failure modeling for hardware components is advanced by incorporating finite element analysis (FEA) into PPoF analysis and coupling the FEA-based PPoF with the maintenance performance through a renewal process model. This article covers two scientific contributions: (i) first-of-its-kind incorporation of FEA into the PPoF model of thermal fatigue for NPP components; and (ii) advancing the interface between the PPoF analysis and the renewal process model in order to deal with spatiotemporal FEA outputs and to efficiently estimate the physical transition rates even when the PPoF outputs are dominated by success data. Through the incorporation of FEA, the resolution of the PPoF analysis is enhanced as spatiotemporal conditions such as stress and temperature can be considered explicitly instead of relying on simplified assumptions or analytical models with reduced spatiotemporal dimensions. To demonstrate an application of the FEA-based PPoF analysis and its coupling with maintenance through the renewal process model, a case study is conducted using excess letdown elbow piping in the chemical and volume control system of a Pressurized Water Reactor.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Probabilistic physics of failure approach to fusion systems & Risk-efficiency optimization of nuclear co-generation [Slides]

An overview of the progress made towards two separate summer internship projects: a probabilistic physics of failure approach to fusion systems and a risk efficiency optimization of nuclear co-generation. Original version summarized the progress through end of June, and the revision version summarizes the progress through the entire internship to August 1st.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Integrating renewal process modeling with Probabilistic Physics-of-Failure: Application to Loss of Coolant Accident (LOCA) frequency estimations in nuclear power plants

Renewal process modeling is used for the failure prediction of hardware components under periodic maintenance. While most studies utilized data-driven approaches to estimate the input parameters for renewal process models, this paper initiates a line of research to integrate renewal process modeling with probabilistic models of underlying mechanisms associated with physical degradation and maintenance. At this stage of the research, the methodology integrates Markov modeling with Probabilistic Physics-of-Failure (PPoF) models of degradation, while maintenance is treated by a data-driven approach. This methodology is valuable to obtain a more accurate estimation of component reliability and availablity, especially when (i) components are highly reliable, and failure data are limited, (ii) historical data are unreliable due to changes in design, operation, and maintenance, or (iii) advanced technologies have emerged limiting operational data. The methodology explicitly incorporates the underlying spatiotemporal causes of failure into the renewal model, allowing to rank the criticality of causal factors to improve maintenance and mitigation strategies. Although the new methodology is applicable for component reliability and availability analysis in diverse industries, this report demonstrates its value for estimating frequencies of a Loss-Of-Coolant Accident (LOCA), which is an initiating event in Probabilistic Risk Assessment (PRA) of Nuclear Power Plants (NPPs).

42 ENGINEERING↗
DOE OSTI2023

Cyber-physical cascading failure and resilience of power grid: A comprehensive review

Smart grid technologies are based on the integration of the cyber network and the power grid into a cyber-physical power system (CPPS). The increasing cyber-physical interdependencies bring about tremendous opportunities for the modeling, monitoring, control, and protection of power grids, but also create new types of vulnerabilities and failure mechanisms threatening the reliability and resiliency of system operation. A major concern regarding the interdependent networks is the cascading failure (CF), where a small initial disturbance/failure in the network results in a seemingly unexpected large-scale failure. Although there has been a significant volume of recent work in the CF research of CPPS, a comprehensive review remains unavailable. This article aims to fill the gap by providing a systematic literature survey regarding the modeling, analysis, and mitigation of CF in CPPS. The open research questions for further research are also discussed. This article allows researchers to easily understand the state of the art of CF research in CPPS and fosters future work required towards full resolutions to the remaining questions and challenges.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Physics of Failure Analysis of Nuclear Fusion Divertor Monoblock: An Analytical Approach

Original - A slide package that summarizes research progress of 2025 summer high school internship. To be presented at an internal group presentation to all the high school interns and mentors within C200, plus potential more interested audience from INL. Revision - Based on the original LRS-approved slide package, an updated slide package and a poster were prepared with slight changes (e.g., adjusting the layout of slides and reproducing some plots for better clarity) for the interns' final showcase on August 13. The showcase will be public-facing (i.e., not limited to INL attendees).

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Prediction of Lumen Output and Chromaticity Shift in LEDs Using Kalman Filter and Extended Kalman Filter Based Models

Abstract— Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. Life prediction of L70 life for the LEDs used in SSL luminaires from KF and EKF based models have been compared with the TM-21 model predictions and experimental data.

Lall, Pradeep↗
DOE OSTI2014

Prediction of L70 Life and Assessment of Color Shift for Solid-State Lighting Using Kalman Filter and Extended Kalman Filter-Based Models

Solid-state lighting (SSL) luminaires containing light-emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in nonclimate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at the end of life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn“. The LEDs continually degrade, and the light output decreases eventually below useful levels causing failure. Currently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time, causing lumen depreciation. The underlying TM-21 model may not capture the failure physics in presence of multiple failure mechanisms. The correlation of lumen maintenance with underlying physics of degradation at system level is needed. In this paper, Kalman filter (KF) and extended Kalman Filters (EKF) have been used to develop a 70% Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand-hour LM-80 test data for various LEDs are used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise, and measurement noise. The future state of the lumen depreciation are estimated based on a second-order Kalman Filter model and a Bayesian Framework. The measured state variable are related to the underlying damage using physics-b- sed models. Life prediction of L70 life for the LEDs used in SSL luminaires from KF- and EKF-based models are also compared with the TM-21 model predictions and experimental data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

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↗
DOE OSTI2023

Passive Safety System Reliability Analysis: Methodology, Application, and Results

Many advanced reactor designs rely on passive systems to provide assurance that certain safety functions, such as reactivity control and reactor heat removal, are maintained during transient scenarios. The performance of these systems is sensitive to boundary and environmental conditions and can lead to functional failure, which is the inability of the system to perform as intended even without any physical failures. Additionally, conditions can cause the passive systems to operate in an intermediate or degraded state. These operational modes of passive systems and their overall reliability can be difficult to capture in traditional probabilistic frameworks where discrete operating modes are assumed or time-dependent boundary conditions are not accounted for. Novel strategies and methods are needed to be able to capture the reliability of these passive systems and their impact on facility safety. The current abstract provides a summary of such a methodology and examples of its use in a case study, the GE Hitachi (GEH) PRISM probabilistic safety assessment (PSA), and the U.S. Department of Energy (USDOE) Versatile Test Reactor (VTR) PSA.

Bucknor, M.↗
DOE OSTI2014

SU-E-T-192: FMEA Severity Scores - Do We Really Know?

Purpose: Failure modes and effects analysis (FMEA) is a subjective risk mitigation technique that has not been applied to physics-specific quality management practices. There is a need for quantitative FMEA data as called for in the literature. This work focuses specifically on quantifying FMEA severity scores for physics components of IMRT delivery and comparing to subjective scores. Methods: Eleven physical failure modes (FMs) for head and neck IMRT dose calculation and delivery are examined near commonly accepted tolerance criteria levels. Phantom treatment planning studies and dosimetry measurements (requiring decommissioning in several cases) are performed to determine the magnitude of dose delivery errors for the FMs (i.e., severity of the FM). Resultant quantitative severity scores are compared to FMEA scores obtained through an international survey and focus group studies. Results: Physical measurements for six FMs have resulted in significant PTV dose errors up to 4.3% as well as close to 1 mm significant distance-to-agreement error between PTV and OAR. Of the 129 survey responses, the vast majority of the responders used Varian machines with Pinnacle and Eclipse planning systems. The average years of experience was 17, yet familiarity with FMEA less than expected. Survey reports perception of dose delivery error magnitude varies widely, in some cases 50% difference in dose delivery error expected amongst respondents. Substantial variance is also seen for all FMs in occurrence, detectability, and severity scores assigned with average variance values of 5.5, 4.6, and 2.2, respectively. Survey shows for MLC positional FM(2mm) average of 7.6% dose error expected (range 0–50%) compared to 2% error seen in measurement. Analysis of ranking in survey, treatment planning studies, and quantitative value comparison will be presented. Conclusion: Resultant quantitative severity scores will expand the utility of FMEA for radiotherapy and verify accuracy of FMEA results compared to highly variable subjective scores.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Cyber–Physical Correlation Effects in Defense Games for Large Discrete Infrastructures

In certain critical infrastructures, correlations between cyber and physical components can be exploited to launch strategic attacks, so that disruptions to one component may affect others and possibly the entire infrastructure. Such correlations must be explicitly taken into account in ensuring the survival of the infrastructure. For large discrete infrastructures characterized by the number of cyber and physical components, we characterize the cyber–physical interactions at two levels: (i) the cyber–physical failure correlation function specifies the conditional survival probability of the cyber sub-infrastructure given that of the physical sub-infrastructure (both specified by their marginal probabilities), and (ii) individual survival probabilities of both sub-infrastructures are characterized by first-order differential conditions expressed in terms of their multiplier functions. We formulate an abstract problem of ensuring the survival probability of a cyber–physical infrastructure with discrete components as a game between the provider and attacker, whose utility functions are composed of infrastructure survival probability terms and cost terms, both expressed in terms of the number of components attacked and reinforced. We derive Nash equilibrium conditions and sensitivity functions that highlight the dependence of infrastructure survival probability on cost terms, correlation functions, multiplier functions, and sub-infrastructure survival probabilities. We apply these analytical results to characterize the defense postures of simplified models of metro systems, cloud computing infrastructures, and smart power grids.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Development and Evaluation of Algorithms to Improve Small- and Medium-Size Commercial Building Operations

Small- and medium-sized (<100,000 sf) commercial buildings (SMBs) represent over 95% of the U.S. commercial building stock and consume over 60% of total site energy consumption. Many of these buildings use rudimentary controls that are mostly manual, with limited scheduling capability, no monitoring or failure management. Therefore, many of these buildings are operated inefficiently and consume excess energy. SMBs typically utilize packaged rooftop units (RTUs) that are controlled by an individual thermostat. There is increased urgency to improve the operating efficiency of existing commercial building stock in the U.S. for many reasons, chief among them is to mitigate the climate change impacts. Studies have shown that managing set points and schedules of the RTUs will result in up to 20% energy and cost savings. Another problem associated with RTUs is short-cycling, where an RTU goes through ON and OFF cycles too frequently. Excessive cycling can lead to excessive wear and lead to premature failure of the compressor or its components. The short cycling can result in a significantly decreased average efficiency (up to 10%), even if there are no physical failures in the equipment. Also, SMBs use a time-of-day scheduling is to start the RTUs before the building will be occupied and shut it off when unoccupied. Ensuring correct use of the zone set points and eliminating frequent cycling of RTUs thereby leading to persistent building operations can significantly increase the operational efficiency of the SMBs. A growing trend is to use low-cost control infrastructure that can enable scalable and cost-effective intelligent building operations. The work reported in this report describes three algorithms for detecting the zone set point temperature, RTU cycling rate and occupancy schedule detection that can be deployed on the low-cost infrastructure. These algorithms only require the zone temperature data for detection. The algorithms have been tested and validated using field data from a number of RTUs from six buildings in different climate locations. Overall, the algorithms were successful in detecting the set points and ON/OFF cycles accurately using the peak detection technique and occupancy schedule using symbolic aggregate approximation technique. The report describes the three algorithms, results from testing the algorithms using field data, how the algorithms can be used to improve SMBs efficiency, and presents related conclusions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Characterizing possible failure modes in physics-informed neural networks (characterizing-pinns-failure-modes) v0.1.0

This software provides an implementation of the physics-informed neural network setup for different differential equations. It provides a way to solve differential equations using machine learning, via a physical constraint term in the loss function. Physics-informed neural networks are a popular approach, but here, the failures of such an approach are characterized and better solutions and training procedures are provided.

Krishnapriyan, Aditi↗