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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

From BERTopic to SysML: Informing Model-Based Failure Analysis With Natural Language Processing for Complex Aerospace Systems

The development of emerging complex aerospace systems will require new approaches for capturing safety incident scenarios as early as possible in the design phase. However, for novel systems, relevant data available is limited. In this work, we propose a framework informing model-based mission assurance activities with historical incident reports, lessons learned, or other relevant engineering documents using natural language processing. In doing so, we investigate whether there is useful information in data sets that are relevant, if not identical, to the system under design and whether, through rigorous systems engineering practice, this information can be effectively leveraged through model-based failure analysis. In a worked case study, we apply state-of-the-art topic modeling techniques to two data sets, a mission relevant data set and a system relevant data set. The sets of topics are merged and interpreted to form a preliminary list of failure topics that can be used to inform the identification of off-nominal modes in the model-based failure modes and effects analysis development. Once data from the system in operation is available, it can be used to update the topics identified. By extracting information about likely failures from relevant historical data sets and utilizing model-based mission assurance to ensure relevance and rigor, unanticipated failures can be reduced, and projects can more effectively learn from past missions.

Failure Analysis↗
NASA NTRSDate not supplied

From BERTopic to SysML: Informing Model-Based Failure Analysis With Natural Language Processing for Complex Aerospace Systems

The development of emerging complex aerospace systems will require new approaches for capturing safety incident scenarios as early as possible in the design phase. However, for novel systems, relevant data available is limited. In this work, we propose a framework informing model-based mission assurance activities with historical incident reports, lessons learned, or other relevant engineering documents using natural language processing. In doing so, we investigate whether there is useful information in data sets that are relevant, if not identical, to the system under design and whether, through rigorous systems engineering practice, this information can be effectively leveraged through model-based failure analysis. In a worked case study, we apply state-of-the-art topic modeling techniques to two data sets, a mission relevant data set and a system relevant data set. The sets of topics are merged and interpreted to form a preliminary list of failure topics that can be used to inform the identification of off-nominal modes in the model-based failure modes and effects analysis development. Once data from the system in operation is available, it can be used to update the topics identified. By extracting information about likely failures from relevant historical data sets and utilizing model-based mission assurance to ensure relevance and rigor, unanticipated failures can be reduced, and projects can more effectively learn from past missions.

Failure Analysis↗
DOE OSTI2024

Root Cause Correlation Analysis of Software Failures via Orthogonal Defect Classification and Natural Language Processing

Systems theoretic process analysis (STPA) is becoming an increasingly popular technique to assess how complex digital software systems can fail. Rather than defining failures by their observable failure events, which may be sparse especially for safety rated nuclear digital instrumentation and control systems (DI&C), failures are defined as postulated unsafe actions under specific contextual conditions. This permits a top-down analysis of system hazards and identifies whether imposed constraints and requirements can sufficiently address undesirable hazards. However, STPA is a qualitative approach at identifying inadequacies in the development process and cannot currently be used to quantify unsafe action likelihoods for probabilistic risk assessment. Therefore, in this work, we examine the root causes of software failure and explore whether a consistent correlation can be linked to specific unsafe action classes. We implement Lbl2Vec, an unsupervised document classification and retrieval algorithm, on a database of 4,096 software defect reports acquired from various open-source software systems. By analyzing sentence structure, embedded labels, and word vectors, we show that certain defect types positively correlate to specific unsafe action classes over others. The correlations developed can be used to estimate the failure probability of safety intended DI&C systems which provides a licensing basis for nuclear plant modernization efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Root Cause Correlation Analysis of Software Failures via Orthogonal Defect Classification and Natural Language Processing

Systems theoretic process analysis (STPA) is becoming an increasingly popular technique to assess how complex digital software systems can fail. Rather than defining failures by their observable failure events, which may be sparse especially for safety rated nuclear digital instrumentation and control systems (DI&C), failures are defined as postulated unsafe actions under specific contextual conditions. This permits a top-down analysis of system hazards and identifies whether imposed constraints and requirements can sufficiently address undesirable hazards. However, STPA is a qualitative approach at identifying inadequacies in the development process and cannot currently be used to quantify unsafe action likelihoods for probabilistic risk assessment. Therefore, in this work, we examine the root causes of software failure and explore whether a consistent correlation can be linked to specific unsafe action classes. We implement Lbl2Vec, an unsupervised document classification and retrieval algorithm, on a database of 4,096 software defect reports acquired from various open-source software systems. By analyzing sentence structure, embedded labels, and word vectors, we show that certain defect types positively correlate to specific unsafe action classes over others. The correlations developed can be used to estimate the failure probability of safety intended DI&C systems which provides a licensing basis for nuclear plant modernization efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

A combined experimental and computational analysis of failure mechanisms in open-hole cross-ply laminates under flexural loading

In this work, integrated experimental tests and computational modeling are proposed to investigate the failure mechanisms of open-hole cross-ply carbon fiber reinforced polymer (CFRP) laminated composites. In particular, we propose two effective methods, which include width-tapered double cantilever beam (WTDCB) and fixed-ratio mixed-mode end load split (FRMMELS) tests, to obtain the experimental data more reliably. We then calibrate the traction-separation laws of cohesive zone model (CZM) used among laminas of the composites by leveraging these two methods. The experimental results of fracture energy, i.e. G Ic and G Tc , obtained from WTDCB and FRMMELS tests are generally insensitive to the crack length thus requiring no effort to accurately measure the crack tip. Moreover, FRMMELS sample contains a fixed mixed-mode ratio of G IIc /G Tc depending on the width taper ratio. Examining comparisons between experimental results of FRMMELS tests and failure surface of B–K failure criterion predicted from a curve fitting, good agreement between the predictions and experimental data has been found, indicating that FRMMELS tests are an effective method to determine mixed-mode fracture criterion. In addition, a coupled experimental-computational modeling of WTDCB, edge notched flexure, and FRMMELS tests are adopted to calibrate and validate the interfacial strengths. Finally, failure mechanisms of open-hole cross-ply CFRP laminates under flexural loading have been studied systematically using experimental and multi-scale computational analyses based on the developed CZM model. The initiation and propagation of delamination, the failure of laminated layers as well as load-displacement curves predicted from computational analyses are in good agreement with what we have observed experimentally.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Predicting the proximity to macroscopic failure using local strain populations from dynamic in situ X-ray tomography triaxial compression experiments on rocks

Predicting the proximity of large-scale dynamic failure is a critical concern in the engineering and geophysical sciences. Here we use evolving contractive, dilatational, and shear strain deformation preceding failure in dynamic X-ray tomography experiments to examine which strain components best predict the proximity to failure. We develop machine learning models to predict the proximity to failure using time series of three-dimensional local incremental strain tensor fields acquired in rock deformation experiments under stress conditions of the upper crust. Three-dimensional scans acquired in situ throughout triaxial compression experiments provide a distribution of density contrasts from which we estimate the three-dimensional incremental strain that accumulates between each scan acquisition. Training machine learning models on multiple experiments of six rock types provides suites of feature importance that indicate the predictive power of each feature. Comparing the average importance of groups of features that include information about each strain component quantifies the ability of the contractive, dilatational and shear strain to predict the proximity of macroscopic failure. A total of 24 models of four machine learning algorithms with six rock types indicate that 1) the dilatational strain provides the best predictive power of the strain components, and 2) the intermediate values (25th-75th percentile) of the strain population provide the best predictive power of the statistics of the strain populations. In addition, the success of the predictions of models trained on one rock type and tested on other rock types quantifies the similarities and differences of the precursory strain accumulation process in the six rock types. These similarities suggest the potential existence of a unified theory of brittle rock deformation for a range of rock types.

58 GEOSCIENCES↗
DOE OSTI2023

Failure analysis of nuclear transient-tested UN tristructural isotropic fuel particles in a 3D printed SiC matrix

Fully ceramic microencapsulated fuel elements containing UN tristructural isotropic (TRISO) fuel particles within a 3D printed SiC matrix were subjected to transient testing with varying energy depositions. Detailed post-irradiation examinations were performed, including leaching in hot HNO3 and post-leaching X-ray computed tomography, to quantify the percentage of failed TRISO particles and crack propagation within the particles and surrounding fuel matrix. In parallel, detailed finite element analyses were performed for comparison with experimental findings and to better evaluate transient failure modes. The lowest transient energy deposition—which still exceeded bounding values for high-temperature gas-cooled reactor applications—resulted in no detectable TRISO particle failures or matrix cracking, which was consistent with the simulations. Simulations of the higher-energy transients for which significant TRISO particle failure was expected were generally able to reproduce the transient temperatures and matrix cracking. Thus, the TRISO particle failures were explained based on the effects of local SiC matrix thickness and porosity. Results generally confirmed the high strength of the additively manufactured SiC matrix but also affirmed the need for a modified UN TRISO architecture to prevent SiC matrix cracks from propagating through TRISO layers. This unique failure mode has not historically been considered for TRISO fuels contained in weaker graphite matrices.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Avoiding thermal runaway in lithium-ion batteries using ultrasound detection of early failure mechanisms

Thermal runaway leading to catastrophic failure has slowed the adoption of lithium-ion batteries, highlighting the need for early warning systems. In this work, ultrasound is used to detect physical changes in 0.950 Ah Li-ion battery cells by inducing conditions associated with thermal runaway. Ultrasound signal features are extracted as the batteries are cycled under baseline conditions and subsequently subjected to constant current and voltage overcharges. Two conditions identifying failure are defined: a warning to detect the start of overcharge and an emergency stop (E-stop), to immediately take the battery out of service. Using changes in ultrasound signal features, consistent warning and stopping classifications are applied to all failure experiments. On average, a warning notification was issued after 15% of the overcharge time and an E-stop was triggered after 35% of total overcharge duration before failure. Further testing suggests that it is possible to avoid thermal runaway by reacting to intermediate, warning notifications prior to an immediate E-stop. In other words, not only can detection derived by ultrasound identify battery failure before a catastrophic event, but can also provide early, actionable warnings so that overcharges can be detected and corrected quickly enough so the battery does not need to be decommissioned.

25 ENERGY STORAGE↗
DOE OSTI2021

Amorphous complexions alter the tensile failure of nanocrystalline Cu-Zr alloys

Grain boundary-based mechanisms are known to control the plastic deformation and failure of nanocrystalline metals, with manipulation of the boundary structure a promising path for tuning this response. In this study, the role of interfacial structural disorder on plasticity and failure of nanocrystalline Cu-Zr alloys is investigated with in situ scanning electron microscopy tensile deformation experiments. Two model materials are created, one with only the typical ordered grain boundaries and another with amorphous intergranular films interspersed into the boundary network, while the microstructures are otherwise identical. Hence, the importance of complexion type on plasticity and failure is isolated by only varying complexion structure. Here, the tensile experiments show that failure of the samples containing amorphous films is significantly retarded, as evidenced by an increase in the cross-sectional area reduction, a decrease in the occurrence of shear-dominated failure, a decrease in strain localization, and fracture surfaces with more elongated dimple features. As a whole, this study provides direct evidence that structural disorder at the grain boundaries can be beneficial for improving the ductility of nanocrystalline metals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Effects of grain size and porosity on cladding failure in high-burnup UO 2 : A sensitivity and uncertainty study

Isotopic taggants are being studied to aid in the provenance assessment of nuclear materials. However, these taggants must be selected such that they do not adversely affect fuel performance during normal operation or accident scenarios. Taggants are known to affect the fuel’s grain size and porosity. In the work described in this paper, the BISON fuel performance code was used to assess the potential effects of taggants (i.e., grain size and porosity) on fuel rod behavior and cladding failure during a high-burnup, large-break loss-of-coolant accident. Here, 281 individual fuel rods from the same reactor core were modeled for a sensitivity study, a parametric study, and uncertainty quantification. The cladding failure predictions often exhibited stochastic behavior. After additional study, it was found that the cladding failure model is highly sensitive to residual error inherent to numerical approximation solvers. Some strategies to mitigate this sensitivity are discussed. The study found no relationship between known taggant effects and cladding failure status. However, taggants were found to affect the time and location of failure in certain rods. In conclusion, future work to continue investigating and validating these findings is briefly discussed.

Doped UO 2↗
DOE OSTI2025

Investigation of the temperature-dependent failure processes in PVD Cr-coated ZIRLO nuclear fuel cladding using in situ X-ray micro-tomography imaging

In this work, an accident tolerant fuel cladding system with Cr coating deposited using physical vapour deposition (PVD) method onto commercial Optimized ZIRLO™ was studied. The cladding tubes were machined into C-rings and loaded to failure under compression with real-time synchrotron micro-X-ray computed tomography (XCT) imaging at room temperature (RT), 345 °C, 650 °C and 950 °C in argon (Ar) atmosphere. The mechanical behaviour and failure processes were found strongly temperature-dependent where the Cr coating showed brittle fracture at RT and 345 °C, ductile fracture at 650 °C and a reversion to brittle fracture at 950 °C. Nanoindentation measurements and scanning electron microscopy (SEM) imaging were conducted on the materials after high temperature testing. It was found that recrystallisation of the Cr coating occurred at elevated temperatures, which significantly affected its local properties hence the failure behaviour at different temperatures. This work represents the first in situ 3D XCT observation of progressive failure processes in PVD Cr-coated ZIRLO claddings up to 950 °C providing critical insights into its brittle-to-ductile transition (BDTT) behaviour and subsequent ductile-to-brittle reversion with the increase in temperature. Moreover, results are compared with other PVD Cr-coated Zircaloy materials from open literature; the influences of the coating microstructure and local properties on the failure stress/strain and fracture processes are discussed.

Yuan, Guanjie↗
DOE OSTI2024

Shock-induced twinning/detwinning and spall failure in Cu–Ta nanolaminates at atomic scales

Here, this study provides new insights into the role of interfaces on the deformation and failure mechanisms in shock-loaded Cu–Ta–Cu trilayer system. The thickness of the Ta layer, piston velocities, and shock pulse durations were varied to explore the impact of impedance mismatch and loading conditions on spallation behavior and twin formation. It was found that the interfaces play a crucial role in the dynamic response of these multilayered systems since secondary reflection waves generated at the interfaces significantly affected the peak stress and pressure profiles, influencing void nucleation and failure modes. In the trilayer systems, failure predominantly occurred at interfaces and within the Ta layer, with void nucleation sites and twinning behavior being markedly different compared to single-crystal Cu and Ta. Increasing the Ta layer thickness modified the wave interactions, leading to different failure locations. Higher piston velocities were associated with increased spall strength by enhancing wave interactions and void formation, particularly at the interfaces and within the Ta layer, under specific configurations. Additionally, shorter shock pulse durations facilitated earlier initiation of the release fan, reducing twin formation and altering the failure dynamics by accelerating twin annihilation and pressure release.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Techno-Economic Impact of Partial String Failure in Multi-string Energy Storage Systems

Electric utilities and large industrial/commercial customers, which integrate multi-MW multi-string energy storage systems (ESS), are typically at a significant risk of string failure. This results in the loss of system performance and capacity. In order to evaluate the accurate costs and benefits associated with a particular battery system, it is essential to incorporate the costs associated with battery string failure and, as a result, reduced capacity. This report analyzes how the economic losses can be estimated based on market rules and ESS availability. A framework for calculation of economic losses is developed and implemented with an illustrative string failure case for Independent System Operator (ISO-NE) forward capacity market. Further scenarios and case studies are analyzed by varying the probability of string failure as well as the capacity bid into the market. These scenarios are used to assess the variability of economic losses and the impact of string failure on the final realized benefits from the battery's market participation.

Fotedar, Vanshika↗
DOE OSTI2024

From Failure to Insight: Analyzing Disk Breakdowns in Large-Scale HPC Environments

Disk failure data provides valuable insights for preventing failures, enhancing storage robustness, guiding system design and deployment, and ensuring reliable operations at data centers. This paper introduces two disk failure datasets collected from large-scale HPC production environments over the past five years, comprising over 5,000 failure records from more than 40,000 disks. We analyzed these datasets across multiple dimensions, including temporal, spatial, and relational trends, and performed a comprehensive reliability assessment. Our analysis yielded numerous observations and insights that influence various operational aspects of HPC storage systems. We believe this study offers a holistic understanding of disk failure trends likely to interest the HPC storage community.

George, Anjus↗
DOE OSTI2025

FIRE: A Failure-Adaptive RL Framework for Edge Computing Migrations

In edge computing, users' service profiles are migrated between edge servers due to user mobility. Reinforcement Learning (RL) frameworks have been proposed to do so, often trained on simulated data. However, existing RL frameworks overlook occasional server failures, which although rare, impact latency-sensitive applications like AR/VR and real- time obstacle detection. These rare failures, being not adequately represented in historical training data, pose a challenge for data-driven RL algorithms. We introduce FIRE, a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose FIRE-ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. FIRE considers delay, migration, failure, and backup placement costs across individual and shared service profiles. We prove FIRE-ImRE's boundedness and convergence to optimality. Next, we introduce novel deep Q-learning (FIRE-ImDQL) and actor critic (FIRE-ImACRE) versions of our algorithm to enhance scalability. Here, we extend our framework to accommodate users with varying risk tolerances of rare failure events. Through trace-driven experiments, we show that FIRE reduces edge computing costs compared to vanilla RL and the greedy baseline in the event of failures.

Edge computing↗
DOE OSTI2020

Mesh Failure Prediction Using Deep Learning Techniques

The goal of this joint effort between SRI and LLNL has been to investigate the use of state-of-the-art deep learning architectures to effectively explore the correlations between a set of mesh zone attributes and manifestations of failure in a given mesh simulation and draw useful conclusions towards a practical failure detection system. Each zone attribute is a scalar feature that is recorded separately for each zone during simulation, resulting in a multivariate time series signal for each zone. Currently, these features are used to manually decide where to apply manual relaxations so that simulations run without any failures. Our long-term vision is to build a machine learning system that is capable of reliably detecting upcoming failures and is able to apply finer-grained targeted relaxations automatically to relevant zones to avoid an expected failure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Thermal-Mechanical Elastic-Plastic and Ductile Failure Model Calibrations for 304L Stainless Steel Alloy

Numerical simulations of metallic structures undergoing rapid loading into the plastic range require material models that accurately represent the response. In general, the material response can be seen as having four interrelated parts: the baseline response under slow loading, the effect of strain rate, the conversion of plastic work into heat and the effect of temperature. In essence, the material behaves in a thermal-mechanical manner if the loading is fast enough so when heat is generated by plastic deformation it raises the temperature and therefore influences the mechanical response. In these cases, appropriate models that can capture the aspects listed above are necessary. The material of interest here is 304L stainless steel, and the objective of this work is to calibrate thermal-mechanical models: one for the constitutive behavior and another for failure. The work was accomplished by first designing and conducting a material test program to provide data for the calibration of the models. The test program included uniaxial tension tests conducted at room temperature, 150 and 300 C and at strain rates between 10 –4 and 10 3 1/s. It also included notched tension and shear-dominated compression hat tests specifically designed to calibrate the failure model. All test specimens were extracted from a single piece of plate to maintain consistency. The constitutive model adopted was a modular $J_2$ plasticity model with isotropic hardening that included rate and temperature dependence. A criterion for failure initiation based on a critical value of equivalent plastic strain fitted the failure data appropriately and was adopted. Possible ranges of the values of the parameters of the models were determined partially on historical data from calibrations of the same alloy from other lots and are given here. The calibration of the parameters of the models were based on finite element simulations of the various material tests using relatively ne meshes and hexahedral elements. When using the model in structural finite element calculations, however, element formulations and sizes different from those in the calibration are likely to be used. A brief investigation demonstrated that the failure initiation predictions can be particularly sensitive to the element selection and provided an initial guide to compensate for the effect of element size in a specific example.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Feedstock-Conversion Interface Consortium: Failure Mode and Effects Analysis Summary Report (FY2022)

This report provides an overview of the development of failure modes and effects analysis (FMEA) and its implementation as a systematic criticality and risk assessment tool supporting a quality by design (QbD) approach for FCIC research. This report also provides a high-level overview of the results for the FMEA evaluation of two feedstock preprocessing system configurations: (1) generation of pine residue materials for high-temperature pyrolysis conversion and (2) generation of corn stover materials for low-temperature conversion using deacetylation and disc mechanical refining pretreatment for fermentation to hydrocarbons. For the results presented in this report, our FMEA interviews included two approaches. The first approach was to perform FMEA interviews for the entire system of unit operations giving a wholistic system level view. The second approach consisted of detailed interviews for each individual unit operation within the system allowing for a “deep dive” into the specific failures for the individual components within the configuration. These two approaches provide different resolutions of information. The FMEA results of this report were focused on failures associated with meeting critical quality attributes (CQAs) identified for the target conversion processes for each processed feedstock type. The information gathered through the FMEA interviews include estimations of risk scores for meeting each given CQA specification, identification of the impacts for not meeting a CQA specification, capturing causes associated with material attributes and process parameters for each failure, identification of current detection methods, and speculation of potential mitigation strategies for decreasing a failure’s risk score. The complete results of all FMEA interviews are provided in the Appendices of this report.

09 BIOMASS FUELS↗