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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Mitigating commutation failures in HVDC systems with SFCL deployment

Commutation failures represent a prevalent issue encountered in line-commutated-converter high voltage direct current (LCC-HVDC) systems. As the widespread deployment of HVDC systems continues, the risk associated with commutation failures increases, posing a growing threat to power grids due to their potential to trigger severe consequences, including cascading failures and widespread blackouts. This research paper aims to address the significant issue of commutation failure within direct current (DC) systems through advocating for the use of resistive-type Superconducting Fault Current Limiters (R-SFCLs). To substantiate the efficacy of this proposed strategy, an array of simulations are executed using the PSCAD/EMTDC software. This comprehensive study investigates the performance characteristics of R-SFCLs configured with varying resistance values, scrutinizing their response under diverse fault resistance scenarios and distinct fault initiation times within the LCC-HVDC system. The outcomes of these simulations are that SFCLs confer significant advantages for mitigating commutation failures, surpassing traditional mitigation methods in terms of effectiveness. Consequently, SFCLs emerge as an optimal solution to prevent commutation failures in the HVDC systems.

Commutation failure↗
DOE OSTI2019

Type IV failure in weldment of creep resistant ferritic alloys: II. Creep fracture and lifetime prediction

The creep rupture behavior (Type IV failure) for weldments of creep strength enhanced ferritic steel is numerically analyzed, using an integrated microstructure- and micromechanics-based finite element model. To account for the large microstructure gradients across weldments, a two-dimensional digital microstructure is constructed based on the actual observed microstructure of ferritic steel weldment by using the Voronoi-tessellation method. According to the fracture mechanism studies and literature experimental observations, the Type IV failure is identified as an intergranular creep fracture in the fine-grained or intercritical heated affected zone (FGHAZ or ICHAZ). In the present study, the following micromechanics model is employed to determine the micromechanical and microstructural origins for the failure process above, accounting for the underlying physical fracture mechanisms at different length scales, including nucleation of grain boundary cavities, their growth by competition of grain boundary diffusion and grain interior creep, viscous grain boundary sliding, and the emergence of microcracks by coalescence and their evolution to the ultimate failure. The methodology demonstrates the capabilities in modeling the Type IV failure and providing quantitative creep rupture lifetime prediction which shows an excellent agreement with long-term creep experimental data for creep strength enhanced ferritic steels and their weldments. In particular, the drop-off in time to rupture at high temperatures and low stress levels in the creep rupture curves is quantitatively predicted, and the transition of failure mechanisms from creep-controlled to diffusion-controlled creep fracture mechanism is illustrated.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Method for Estimating Time-Series PV Production Loss From Solar Tracking Failures

Solar tracking system failures can dramatically reduce photovoltaic (PV) system energy output through suboptimal irradiance capture and self-shading loss. Detecting tracker failures is not always straightforward; unlike other common PV system failures like inverter outages and blown fuses which take portions of a system completely offline, tracker failures only partially reduce output power and can go unnoticed as a result. Additionally, tracker failures can take one of several forms, each with their own loss characteristics. Here we present two methods of detecting tracker failure events from time-series production data and a method for estimating the associated production loss. Compared with existing detection methods, the proposed power-based detection method showed 0-17% reductions in Type I error rate depending on weather conditions. Here, the loss model estimated production loss with low error (mean bias error = -2.3 %, root-mean-squared error = 6%) in a rudimentary validation.

14 SOLAR ENERGY↗
DOE OSTI2024

Machine Learning and Data Science to Advance Laboratory Earthquake Prediction and Illuminate the Mechanics of Precursors to Failure

Earthquakes represent one of our greatest natural hazards and in recent years human induced seismicity is adding to the threat. Even a modest improvement in the ability to forecast devastating large earthquakes or smaller shallow events associated with fluid injection could save thousands of lives and billions of dollars. Current efforts to forecast earthquakes are limited by knowledge of earthquake physics and hampered by a lack of reliable lab or field observations. However, recent work has provided a critical opportunity for advancement. We have found: 1) clear and consistent precursors prior to earthquake-like failure in the laboratory and 2) that lab earthquakes can be predicted using machine learning (ML). These works show that stick-slip failure events –the lab equivalent of earthquakes– are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. Remarkably, ML predicts the fault zone stress state, the failure time and in some cases the magnitude of lab earthquakes. In addition, the observations include clear precursors to failure in the form of changes in fault zone properties prior to lab earthquakes. Precursors have been observed in previous laboratory studies but their origin is poorly understood and their possible connection to ML based earthquake prediction is unknown. The work conducted under our project has dramatically expanded these efforts. We have developed an integrated data science approach to illuminate the physics of earthquake precursors and lab earthquake prediction. Our work has accelerated the development of ML, artificial intelligence (AI), and related data science approaches by providing massive data sets that are tightly connected to critical scientific problems and by bringing together leading subject matter experts and data scientists. Earthquake physics involves phenomena that are far from equilibrium. Our work has leveraged data science methods to illuminate these phenomena and investigate how they relate to earthquake prediction. In addition to a large database with many types of labeled events that is available to everyone, our work has advanced the fundamental understanding of seismic forecasting, earthquake physics, and fault rheology

58 GEOSCIENCES↗
DOE OSTI2023

Develop and Connect TRISO Failure Analysis and Uncertainty Quantification to Fission Product Release Calculation Capability

The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering-scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. Stress-based failure probability has been developed and analyzed to assess the integrity of tri-structural isotropic (TRISO) fuel particles during fuel life cycles. While simple, stress-based approaches to failure probability leveraging the Weibull statistical distribution entails a number of drawbacks when stress concentration occurs near crack tips, including finite element mesh size dependency. In this report, we use an interaction integral approach to the computation of stress intensity factors in functionally graded materials (FGM) for axisymmetric models. The inner pyrolytic carbon (IPyC) cracking induced silicon carbide (SiC) failure is one of the dominated failure modes in TRISO failure analysis. In this study, we consider a crack in the IPyC layer perpendicular to the SiC layer. The interface between these two TRISO layers is considered to be porous, which we simulate considering a transition of mechanical properties over the porous length. These aspects are considered in the computation of stress intensity factor (SIF) from a fracture mechanics approach and compared with the known stress-based failure probability approach.

42 ENGINEERING↗
DOE OSTI2022

Classification of Photovoltaic Failures with Hidden Markov Modeling, an Unsupervised Statistical Approach

Failure detection methods are of significant interest for photovoltaic (PV) site operators to help reduce gaps between expected and observed energy generation. Current approaches for field-based fault detection, however, rely on multiple data inputs and can suffer from interpretability issues. In contrast, this work offers an unsupervised statistical approach that leverages hidden Markov models (HMM) to identify failures occurring at PV sites. Using performance index data from 104 sites across the United States, individual PV-HMM models are trained and evaluated for failure detection and transition probabilities. This analysis indicates that the trained PV-HMM models have the highest probability of remaining in their current state (87.1% to 93.5%), whereas the transition probability from normal to failure (6.5%) is lower than the transition from failure to normal (12.9%) states. A comparison of these patterns using both threshold levels and operations and maintenance (O&M) tickets indicate high precision rates of PV-HMMs (median = 82.4%) across all of the sites. Although additional work is needed to assess sensitivities, the PV-HMM methodology demonstrates significant potential for real-time failure detection as well as extensions into predictive maintenance capabilities for PV.

classification↗
NASA NTRS1996

Relation of systemic and local muscle exercise capacity to skeletal muscle characteristics in men with congestive heart failure

OBJECTIVES. The present study was undertaken to further characterize changes in skeletal muscle morphology and histochemistry in congestive heart failure and to determine the relation of these changes to abnormalities of systemic and local muscle exercise capacity. BACKGROUND. Abnormalities of skeletal muscle appear to play a role in the limitation of exercise capacity in congestive heart failure, but information on the changes in muscle morphology and biochemistry and their relation to alterations in muscle function is limited. METHODS. Eighteen men with predominantly mild to moderate congestive heart failure (mean +/- SEM New York Heart Association functional class 2.6 +/- 0.2, ejection fraction 24 +/- 2%) and eight age- and gender-matched sedentary control subjects underwent measurements of peak systemic oxygen consumption (VO2) during cycle ergometry, resistance to fatigue of the quadriceps femoris muscle group and biopsy of the vastus lateralis muscle. RESULTS. Peak VO2 and resistance to fatigue were lower in the patients with heart failure than in control subjects (15.7 +/- 1.2 vs. 25.1 +/- 1.5 ml/min-kg and 63 +/- 2% vs. 85 +/- 3%, respectively, both p < 0.001). Patients had a lower proportion of slow twitch, type I fibers than did control subjects (36 +/- 3% vs. 46 +/- 5%, p = 0.048) and a higher proportion of fast twitch, type IIab fibers (18 +/- 3% vs. 7 +/- 2%, p = 0.004). Fiber cross-sectional area was smaller, and single-fiber succinate dehydrogenase activity, a mitochondrial oxidative marker, was lower in patients (both p < or = 0.034). Likewise, the ratio of average fast twitch to slow twitch fiber cross-sectional area was lower in patients (0.780 +/- 0.06 vs. 1.05 +/- 0.08, p = 0.019). Peak VO2 was strongly related to integrated succinate dehydrogenase activity in patients (r = 0.896, p = 0.001). Peak VO2, resistance to fatigue and strength also correlated significantly with several measures of fiber size, especially of fast twitch fibers, in patients. None of the skeletal muscle characteristics examined correlated with exercise capacity in control subjects. CONCLUSIONS. These results indicate that congestive heart failure is associated with changes in the characteristics of skeletal muscle and local as well as systemic exercise performance. There are fewer slow twitch fibers, smaller fast twitch fibers and lower succinate dehydrogenase activity. The latter finding suggests that mitochondrial content of muscle is reduced in heart failure and that impaired aerobic-oxidative capacity may play a role in the limitation of systemic exercise capacity.

Non-NASA Center↗
DOE OSTI2019

Failure of brittle micro-spherical shells embedded in elastomer matrix under indentation

Silicone elastomer filled with glass micro balloons (GMB) is an elastomeric syntactic foam used in electronics and component packaging for encapsulation, potting, stress-relief layer, and electrical insulation purposes. Under mechanical loading, the reinforcing phase, namely the GMBs embedded in the elastomer matrix, may break or delaminate, leading to internal damage and macroscale stiffness degradation, which can alter the material's protective capacity against mechanical shock and vibration. The degree of damage is controlled by the loading history, delamination, and failure behavior of the GMBs. In this work, we investigate the GMB failure behavior in this work wherein we present an indentation experiment to measure the force required to fail individual GMBs that are either embedded in the elastomer matrix or adhered to the surface of an elastomer layer. The indentation apparatus is augmented with an inverted optical microscope to enable in situ imaging of the GMB. Failure modes for the embedded or non-embedded GMBs are discussed based on the morphology of the broken GMBs and the measured failure forces. We also measure the adhesion energy between the glass balloon and the elastomer, based on which the possibility of delamination between the GMB and the surrounding elastomer matrix during the failure process is evaluated. Our results can facilitate the development of a failure criterion of GMBs which is necessary for establishing a physics-based constitutive model to describe the macroscopic damage mechanics of elastomeric syntactic foams.

42 ENGINEERING↗
DOE OSTI2017

Quantitative Approach to Failure Mode and Effect Analysis for Linear Accelerator Quality Assurance

Purpose: To determine clinic-specific linear accelerator quality assurance (QA) TG-142 test frequencies, to maximize physicist time efficiency and patient treatment quality. Methods and Materials: A novel quantitative approach to failure mode and effect analysis is proposed. Nine linear accelerator-years of QA records provided data on failure occurrence rates. The severity of test failure was modeled by introducing corresponding errors into head and neck intensity modulated radiation therapy treatment plans. The relative risk of daily linear accelerator QA was calculated as a function of frequency of test performance. Results: Although the failure severity was greatest for daily imaging QA (imaging vs treatment isocenter and imaging positioning/repositioning), the failure occurrence rate was greatest for output and laser testing. The composite ranking results suggest that performing output and lasers tests daily, imaging versus treatment isocenter and imaging positioning/repositioning tests weekly, and optical distance indicator and jaws versus light field tests biweekly would be acceptable for non-stereotactic radiosurgery/stereotactic body radiation therapy linear accelerators. Conclusions: Failure mode and effect analysis is a useful tool to determine the relative importance of QA tests from TG-142. Because there are practical time limitations on how many QA tests can be performed, this analysis highlights which tests are the most important and suggests the frequency of testing based on each test's risk priority number.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Failure behavior of SiC/SiC composite tubes under strain rates similar to the pellet-cladding mechanical interaction phase of reactivity-initiated accidents

The mechanical response of a nuclear-grade silicon carbide fiber-reinforced silicon carbide matrix (SiC/SiC) composite was investigated under mechanical loading conditions mimicking the pellet-cladding mechanical interaction (PCMI) phase of a reactivity-initiated accident (RIA). In a RIA, cladding deformation and failure can be induced by the rapid thermal expansion of the nuclear fuel. A pulse-controlled modified-burst test was used to investigate RIA-like PCMI scenarios on SiC/SiC composite samples at pulse widths from 12 to 100 ms.The strain-driven nature of the cladding sample deformation was due to the rapid internal pressurization and subsequent expansion of a secondary tube. A digital-image correlation technique was used to measure strains from the speckle-painted outer surface of the tubes. The failure strains of samples tested at slower rates, such as RIA event durations of 52 and 100 ms, showed good agreement with the literature-reported values for similar composites tested at slow strain rates. Additionally, the failure strain showed good agreement with reference expansion-due-to-compression tests at slow strain rate. However, a decrease in the failure strain was determined for the fast-rate (12 ms) tests. This indicated that the failure strain of these composites might be influenced by the strain rate during RIA-like events. In conclusion, the failure strains observed in the tests corresponded to local energy depositions of approximately 50 cal/g UO 2 from hot zero power, with an initial condition of pellet–cladding gap closure prior to the event. In-pile transient testing of these concepts that would result in hoop strain due to PCMI in the range of 0.5–1.0% is recommended.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Predicting cell-to-cell failure propagation and limits of propagation in lithium-ion cell stacks

Thermal runaway of lithium-ion batteries is a risk that is magnified when stacks of lithium-ion cells are used for large scale energy storage. When limits of propagation can be identified so that systems can be designed to prevent large scale cascading failure even if a failure does occur, these systems will be safer. This work addresses the prediction of cell-to-cell failure propagation and the propagation limits in lithium-ion cell stacks to better understand and identify safe designs. A thermal-runaway model is presented based on recent developments in thermochemical source terms. It is noted that propagating failure is characterized by temperatures above which calorimetry data is available. Results show high temperature propagating failure predictions are too rapid unless an intra-particle diffusion limit is included, introducing a Damköhler number limiter into the rate expression. This new model form is evaluated against cell-to-cell failure propagation where the end cell of a stack is forced into thermal runaway through a nail-induced short circuit. Limits of propagation for this configuration are identified. Results show cell-to-cell propagation predictions are consistent with measurements over a range of cell states of charge and with the introduction of metal plates between cells to add system heat capacity representative of structural members. This consistency extends from scenarios where propagation occurs through scenarios where propagation is prevented.

25 ENERGY STORAGE↗
DOE OSTI2020

Deformation Precursors to Catastrophic Failure in Rocks

Forecasting the timing of catastrophic failure, such as crustal earthquakes, has been a central concern for centuries. Such forecasting requires identifying signals that evolve or accelerate in the precursory phase leading to failure, and the subset of signals that may be detected in the crust. We develop machine learning models to predict the proximity of catastrophic failure in synchrotron X-ray tomography triaxial compression experiments on rocks using characteristics of evolving fracture networks. We then examine the characteristics that most strongly influence the model results, and thus may be considered the best predictors of the proximity of macroscopic failure. Finally, the resulting suite of predictive parameters underscores the importance of dilation in the precursory phase leading to catastrophic failure. The results indicate that the evolution of the strain energy density field may provide more robust predictions of the proximity of failure than other existing metrics of rock deformation.

58 GEOSCIENCES↗
DOE OSTI2016

SU-F-R-20: Image Texture Features Correlate with Time to Local Failure in Lung SBRT Patients

Purpose: To explore possible correlation between CT image-based texture and histogram features and time-to-local-failure in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT).Methods and Materials: From an IRB-approved lung SBRT registry for patients treated between 2009–2013 we selected 48 (20 male, 28 female) patients with local failure. Median patient age was 72.3±10.3 years. Mean time to local failure was 15 ± 7.1 months. Physician-contoured gross tumor volumes (GTV) on the planning CT images were processed and 3D gray-level co-occurrence matrix (GLCM) based texture and histogram features were calculated in Matlab. Data were exported to R and a multiple linear regression model was used to examine the relationship between texture features and time-to-local-failure. Results: Multiple linear regression revealed that entropy (p=0.0233, multiple R2=0.60) from GLCM-based texture analysis and the standard deviation (p=0.0194, multiple R2=0.60) from the histogram-based features were statistically significantly correlated with the time-to-local-failure. Conclusion: Image-based texture analysis can be used to predict certain aspects of treatment outcomes of NSCLC patients treated with SBRT. We found entropy and standard deviation calculated for the GTV on the CT images displayed a statistically significant correlation with and time-to-local-failure in lung SBRT patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-H-BRC-02: Failure Mode and Effect Analysis of Liver Stereotactic Body Radiotherapy

Purpose: To identify areas of improvement in our liver stereotactic body radiation therapy (SBRT) program, using failure mode and effect analysis (FMEA). Methods: A multidisciplinary group consisting of one physician, three physicists, one dosimetrist and two therapists was formed. A process map covering 10 major stages of the liver SBRT program from the initial diagnosis to post treatment follow-up was generated. A total of 102 failure modes, together with their causes and effects, were identified. The occurrence (O), severity (S) and lack of detectability (D) were independently scored. The ranking was done using the risk probability number (RPN) defined as the product of average O, S and D numbers for each mode. The scores were normalized to remove inter-observer variability, while preserving individual ranking order. Further, a correlation analysis on the overall agreement on rank order of all failure modes resulted in positive values for successive pairs of evaluators. The failure modes with the highest RPN value were considered for further investigation. Results: The average normalized RPN values for all modes were 39 with a range of 9 to 103. The FMEA analysis resulted in the identification of the top 10 critical failures modes as: Incorrect CT-MR registration, MR scan not performed in treatment position, patient movement between CBCT acquisition and treatment, daily IGRT QA not verified, incorrect or incomplete ITV delineation, OAR contours not verified, inaccurate normal liver effective dose (Veff) calculation, failure of bolus tracking for 4D CT scan, setup instructions not followed for treatment and plan evaluation metrics missed. Conclusion: The application of FMEA to our liver SBRT program led to the identification and possible improvement of areas affecting patient safety.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Can Applications Recover from fsync Failures?

We analyze how file systems and modern data-intensive applications react to fsync failures. First, we characterize how three Linux file systems (ext4, XFS, Btrfs) behave in the presence of failures. We find commonalities across file systems (pages are always marked clean, certain block writes always lead to unavailability) as well as differences (page content and failure reporting is varied). Next, we study how five widely used applications (PostgreSQL, LMDB, LevelDB, SQLite, Redis) handle fsync failures. Our findings show that although applications use many failure-handling strategies, none are sufficient: fsync failures can cause catastrophic outcomes such as data loss and corruption. Our findings have strong implications for the design of file systems and applications that intend to provide strong durability guarantees.

Computer Science↗
DOE OSTI2017

Epidemic failure detection and consensus for extreme parallelism

Future extreme-scale high-performance computing systems will be required to work under frequent component failures. The MPI Forum s User Level Failure Mitigation proposal has introduced an operation, MPI Comm shrink, to synchronize the alive processes on the list of failed processes, so that applications can continue to execute even in the presence of failures by adopting algorithm-based fault tolerance techniques. This MPI Comm shrink operation requires a failure detection and consensus algorithm. This paper presents three novel failure detection and consensus algorithms using Gossiping. The proposed algorithms were implemented and tested using the Extreme-scale Simulator. The results show that in all algorithms the number of Gossip cycles to achieve global consensus scales logarithmically with system size. The second algorithm also shows better scalability in terms of memory and network bandwidth usage and a perfect synchronization in achieving global consensus. The third approach is a three-phase distributed failure detection and consensus algorithm and provides consistency guarantees even in very large and extreme-scale systems while at the same time being memory and bandwidth efficient.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Vitality models found useful in modeling tag-failure times in acoustic-tag survival studies

Abstract Acoustic telemetry studies often rely on the assumption that premature tag failure does not affect the validity of inferences. However, in some cases this assumption is possibly or likely invalid and it is necessary to apply a correction to estimation procedures. The question of which approaches and specific models are best suited to modeling acoustic tag failures has received little research attention. In this short communication, we present a meta-analysis of 42 acoustic tag-life studies, originally used to correct survival studies involving outmigrating juvenile salmonids in the Columbia/Snake river basin. We compare the performance of nine alternative parametric models including common failure–time/survival models and the vitality models of Li and Anderson Theor Popul Biol 76:118–131, (2009) and Demogr Res 28:341–372, (2013). The tag-life studies used acoustic tags from three different tag manufacturers, had expected lifetimes between 12 and 61 days, and had dry weights ranging from 0.22 to 1.65 g. In 57% of the cases, the vitality models of Li and Anderson Theor Popul Biol 76:118–131, (2009) and Demogr Res 28:341–372, (2013) fit the tag-failure times best. The vitality models were also the second-best choices in 17% of the cases. Together, the vitality models, log-logistic, (19%), and gamma models (14%) accounted for 90% of the models selected. Unlike more traditional failure–time models (e.g., Weibull, Gompertz, gamma, and log-logistic), the vitality models are capable of characterizing both the early onset of tag failure due to manufacturing errors and the anticipated battery life. We provide further guidance on appropriate sample sizes (50–100 tags) and procedures to be considered when applying precise tag-life corrections in release–recapture survival studies.

Skalski, John R. (ORCID:0000000270702505)↗
DOE OSTI2024

Brittle failure analysis and modeling of high-burnup PWR fuel cladding alloys

The aim of this research is the development of methods for predicting mechanical behavior and identification of limiting conditions to prevent brittle failure of high-burnup (HBU) pressure water reactor (PWR) fuel cladding alloys. A finite element (FE) model of the ring compression test (RCT) was created to analyze the failure behavior of zirconium-based alloys with radial hydrides during the RCT. An elastic-plastic material model describes the zirconium alloy. The stress-strain curve needed for the elastic-plastic material model was derived by inverse finite element analyses. Cohesive zone modeling is used to reproduce sudden load drops during RCT loading. Based on the failure mechanism in non-irradiated ZIRLO (R) claddings, a micro-mechanical model was developed that distinguishes between brittle failure along hydrides and ductile failure of the zirconium matrix. Two different cohesive laws representing these types of failure are present in the same cohesive interface. The key differences between these constitutive laws are the cohesive strength, the stress at which damage initiates, and the cohesive energy, which is the damage energy dissipated by the cohesive zone. Statistically generated matrix-hydride distributions were mapped onto the cohesive elements and simulations with focus on the first load drop were performed. Computational results are in good agreement with the RCT results conducted on high-burnup M5 (R) samples. It could be shown that crack initiation and propagation strongly depend on the specific configuration of hydrides and matrix material in the fracture area.

Simbruner, Kai↗