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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Worldwide Physics-Based Lifetime Prediction of c-Si Modules Due to Solder-Bond Failure

Lifetime prediction of the fielded c-Si solar modules due to location-specific weather conditions has been an important topic of photovoltaic research and the economic viability of solar energy. Data analytic techniques such as the performance ratio method, Statistical clear sky model, and Suns-Vmp methods quantify the degradation from measured data of a solar farm, however, the nonlinear time-dependence and correlated degradations make it difficult to use the empirical degradation rates for ultimate lifetime projection. In this article, we propose a complementary physics-based model to predict the solder bond failure caused by mechanical stress associated with the variations of the temperature. Integrating the worldwide weather information from NASA/NSRDB databases, the model predicts the location-specific output-power degradation and the lifetime of a module due to solder bond failure. The model parameters are calibrated against qualification tests involving thermal cycling of specific batches of modules from a specific technology/manufacturer. The results may be summarized as: 1) Modules installed at higher latitudes show a longer lifetime due to reduced damage accumulation. 2) The reduction of temperature fluctuation close to large bodies of water, such as seashores, increases solder bond lifetime significantly. 3) Relatively speaking, modules installed close to the Tropic of Cancer/Capricorn (23.5 degrees North/South) suffer from a higher solder bond damage and have a shorter lifetime, suggesting a conservative design. This model should serve as a building block of a comprehensive reliability framework that can predict the lifetime of a module that experiences simultaneous and correlated degradation mechanisms involving yellowing, corrosion, and potential-induced degradation.

14 SOLAR ENERGY↗
DOE OSTI2015

SU-E-T-105: An FMEA Survey of Intensity Modulated Radiation Therapy (IMRT) Step and Shoot Dose Delivery Failure Modes

Purpose: To assess the perception of TG-142 tolerance level dose delivery failures in IMRT and the application of FMEA process to this specific aspect of IMRT. Methods: An online survey was distributed to medical physicists worldwide that briefly described 11 different failure modes (FMs) covered by basic quality assurance in step- and-shoot IMRT at or near TG-142 tolerance criteria levels. For each FM, respondents estimated the worst case H&N patient percent dose error and FMEA scores for Occurrence, Detectability, and Severity. Demographic data was also collected. Results: 181 individual and three group responses were submitted. 84% were from North America. Most (76%) individual respondents performed at least 80% clinical work and 92% were nationally certified. Respondent medical physics experience ranged from 2.5–45 years (average 18 years). 52% of individual respondents were at least somewhat familiar with FMEA, while 17% were not familiar. Several IMRT techniques, treatment planning systems and linear accelerator manufacturers were represented. All FMs received widely varying scores ranging from 1–10 for occurrence, at least 1–9 for detectability, and at least 1–7 for severity. Ranking FMs by RPN scores also resulted in large variability, with each FM being ranked both most risky (1st ) and least risky (11th) by different respondents. On average MLC modeling had the highest RPN scores. Individual estimated percent dose errors and severity scores positively correlated (p<0.10) for each FM as expected. No universal correlations were found between the demographic information collected and scoring, percent dose errors, or ranking. Conclusion: FMs investigated overall were evaluated as low to medium risk, with average RPNs less than 110. The ranking of 11 FMs was not agreed upon by the community. Large variability in FMEA scoring may be caused by individual interpretation and/or experience, thus reflecting the subjective nature of the FMEA tool.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-545: MLC Distance Travelled as a Predictor for Motor Failure

Purpose: To study the frequency of Multi-Leaf Collimator (MLC) leaf failures, investigate methods to predict them and reduce linac downtime. Methods: A Varian HD120 MLC was used in our study. The hyperterminal MLC errors logged from 06/2012 to 12/2014 were collected. Along with the hyperterminal errors, the MLC motor changes and all other MLC interventions by the linear accelerator engineer were recorded. The MLC dynalog files were also recorded on a daily basis for each treatment and during linac QA. The dynalog files were analyzed to calculate root mean square errors (RMS) and cumulative MLC travel distance per motor. An in-house MatLab code was used to analyze all dynalog files, record RMS errors and calculate the distance each MLC traveled per day. Results: A total of 269 interventions were recorded over a period of 18 months. Of these, 146 included MLC motor leaf change, 39 T-nut replacements, and 84 MLC cleaning sessions. Leaves close to the middle of each side required the most maintenance. In the A bank, leaves A27 to A40 recorded 73% of all interventions, while the same leaves in the B bank counted for 52% of the interventions. On average, leaves in the middle of the bank had their motors changed approximately every 1500m of travel. Finally, it was found that the number of RMS errors increased prior to an MLC motor change. Conclusion: An MLC dynalog file analysis software was developed that can be used to log daily MLC usage. Our eighteen-month data analysis showed that there is a correlation between the distance an MLC travels, the RMS and the life of the MLC motor. We plan to use this tool to predict MLC motor failures and with proper and timely intervention, reduce the downtime of the linac during clinical hours.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Collection of Disk Failure Events from Alpine, the Parallel File System for Summit Supercomputer

This dataset contains disk (HDD) failure events collected from the Alpine storage system of the Summit supercomputer, hosted at OLCF, spanning from January 4, 2019, to December 21, 2023 (a total of 4 years, 11 months, and 18 days), covering 89% of its operational lifetime. It includes 3,766 disk failure events, each recorded with its detection timestamp (in ISO 8601 format) and detailed by its location within the storage system - rack, enclosure, and drive slot number.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Disk Failure Dataset from the Campaign Storage System

This dataset consists of 1,389 disk (HDD) failure events collected from the Campaign storage system at LANL. The Campaign system supported various compute platforms throughout its lifespan, including Cielo, Fire, Ice, and notably, the Trinity supercomputer. Each recorded event includes its detection timestamp (in ISO 8601 format) and details such as its location within the storage system—rack, enclosure, and drive slot number. The data, spanning from May 4, 2021, to July 25, 2023 (2 years, 2 months, and 22 days), represents failure events from the terminal years of Campaign's operational period, accounting for 26% of its total operational time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Multiaxial Creep-Fatigue and Creep-Ratcheting Failures of Grade 91 and Haynes 230 Alloys Toward Addressing Design Issues of Gen IV Nuclear Power Plants

The proposed research will develop systematic sets of uniaxial and multiaxial experimental data at a very high temperature (850-950°C) for Alloy 617. The loading histories to be prescribed in the experiments will induce creep-fatigue and creep-ratcheting failure mechanisms. These experimental responses will be scrutinized in order to quantify the influences of temperature and creep on fatigue and ratcheting failures. A unified constitutive model (UCM) will be developed and validated against these experimental responses. The improved UCM will be incorporated into the widely used finite element commercial software packages ANSYS. The modified ANSYS will be validated so that it can be used for evaluating the very high temperature ASME-NH design-by-analysis methodology for Alloy 617 and thereby addressing the ASME-NH design code issues.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Taxonomy for Common-Cause Failure Vulnerability and Mitigation

Applying current guidance and practices for common-cause failure (CCF) mitigation to digital instrumentation and control (I&C) systems has proven problematic, and the regulatory environment has been unpredictable. The potential for CCF vulnerability inhibits I&C modernization, thereby challenging the long-term sustainability of existing plants. For new plants and advanced reactor concepts, concern about CCF vulnerability in highly integrated digital I&C systems imposes a design burden that results in higher costs and increased complexity. The regulatory uncertainty in determining which mitigation strategies will be acceptable (e.g., what diversity is needed and how much is sufficient) drives designers to adopt complicated, costly solutions devised for existing plants. To address the conditions that constrain the transition to digital I&C technology by the US nuclear industry, crosscutting research is needed to resolve uncertainty, demonstrate necessary characteristics, and establish an objective basis for qualification of digital technology for nuclear power plant (NPP) I&C applications. To fulfill this research need, Oak Ridge National Laboratory is investigating mitigation of CCF vulnerability for nuclear-qualified applications. The outcome of this research is expected to contribute to a fundamentally sound, comprehensive basis to qualify digital technology for nuclear power applications. This report documents the development of a CCF taxonomy. The basis for the CCF taxonomy was generated by determining consistent terminology and establishing a classification approach. The terminology is based on definitions from standards, guides, and relevant nuclear power industry technical reports. The classification approach is derived from identified classification schemes focused on I&C systems and key characteristics, including failure modes. The CCF taxonomy provides the basis for a systematic organization of key systems aspects relevant to analyzing the potential for CCF vulnerability and the suitability of mitigation techniques. Development of an effective CCF taxonomy will help to provide a framework for establishing the objective analysis and assessment capabilities desired to facilitate rigorous identification of fault types and triggers that are the fundamental elements of CCF.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Evaluation of Pad 18 Spent Mercury Gold Trap Stainless Steel Container Failure

Failure of the Pad 18 spent mercury gold trap stainless steel waste container is principally attributed to corrosion induced by degradation of plasticized polyvinyl chloride (pPVC) waste packaging material. Dehydrochlorination of pPVC polymer by thermal and/or radiolytic degradation is well-known to evolve HCl gas, which is highly corrosive to stainless steel and other metals in the presence of moisture. Degradation of the pPVC packaging material was likely caused by radiolysis in the presence of tritium gas within the waste container, though other degradation mechanisms (aging, thermo-oxidation, plasticizer migration) over 30 years storage may have contributed. Corrosion was also likely enhanced by the crevice in the container weld design, and may have been enhanced by the presence of tritiated water. Similar non-failed spent mercury gold trap waste containers did not show radiographic evidence of plastic packaging or trapped free liquid within the container. Therefore, those containers are not expected to exhibit similar failures. Halogenated polymers such as pPVC subject to degradation can evolve halide gases such as HCl, which is corrosive in the presence of moisture and can generate pressure in sealed systems.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Pellet-Cladding Mechanical Interaction Failure Threshold for Reactivity Initiated Accidents for Pressurized Water Reactors and Boiling Water Reactors

Pacific Northwest National Laboratory (PNNL) has been requested by the U.S. Nuclear Regulatory Commission to evaluate the reactivity initiated accident (RIA) tests that have recently been performed in the Nuclear Safety Research Reactor (NSRR) and CABRI (French research reactor) on uranium dioxide (UO 2 ) and mixed uranium and plutonium dioxide (MOX) fuels, and to propose pellet-cladding mechanical interaction (PCMI) failure thresholds for RIA events. This report discusses how PNNL developed PCMI failure thresholds for RIA based on least squares (LSQ) regression fits to the RIA test data from cold-worked stress relief annealed (CWSRA) and recrystallized annealed (RXA) cladding alloys under pressurized water reactor (PWR) hot zero power (HZP) conditions and boiling water reactor (BWR) cold zero power (CZP) conditions.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Developing Component-Specific Prior Distributions for Common Cause Failure Alpha Factors

This report presents the development of component-specific common cause failure (CCF) prior distributions for five component categories: pump, valve, strainer, generator, and other. For ease in comparing these component-specific CCF priors with the 2015 generic CCF priors, the same method used to develop the 2015 generic CCF priors was also employed in this report, along with the same set of failure data (1997–2015) featured in INL/EXT-21-43723. For selected CCF templates, this report also evaluates the effects of applying component-specific priors to CCF parameters.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Fatigue failure at low temperatures

The fatigue failure of steel 20 at low temperatures is considered. Strain hardening of the surface layers is found to occur in adiabatic conditions at low temperatures. That increases the fatigue strength. Fatigue cracks form and grow along the grain boundaries. The influence of impurities on the fatigue failure of low-carbon steel is studied.

42 ENGINEERING↗
DOE OSTI2018

Influence of the Lattice Parameter on the Low-Temperature Brittle Failure of Metals

Abstract—The brittle failure of metals at low temperatures is analyzed. It is shown that the ductile–brittle transition temperature depends on the lattice parameter: with increase in the lattice parameter, the ductile–brittle transition temperature falls. The brittle failure of a material depends on its crystal lattice and its electron structure and on whether impurities are present at the grain boundaries. Increase in brittle strength of steels by optimization of their heat treatment is proposed.

42 ENGINEERING↗
DOE OSTI2022

High Accuracy Indicators of Androgen Suppression Therapy Failure for Prostate Cancer—A Modeling Study

Prostate cancer is a serious public health concern in the United States. The primary obstacle to effective long-term management for prostate cancer patients is the eventual development of treatment resistance. Due to the uniquely chaotic nature of the neoplastic genome, it is difficult to determine the evolution of tumor composition over the course of treatment. Hence, a drug is often applied continuously past the point of effectiveness, thereby losing any potential treatment combination with that drug permanently to resistance. If a clinician is aware of the timing of resistance to a particular drug, then they may have a crucial opportunity to adjust the treatment to retain the drug’s usefulness in a potential treatment combination or strategy. In this study, we investigate new methods of predicting treatment failure due to treatment resistance using a novel mechanistic model built on an evolutionary interpretation of Droop cell quota theory. We analyze our proposed methods using patient PSA and androgen data from a clinical trial of intermittent treatment with androgen deprivation therapy. Our results produce two indicators of treatment failure. The first indicator, proposed from the evolutionary nature of the cancer population, is calculated using our mathematical model with a predictive accuracy of 87.3% (sensitivity: 96.1%, specificity: 65%). The second indicator, conjectured from the implication of the first indicator, is calculated directly from serum androgen and PSA data with a predictive accuracy of 88.7% (sensitivity: 90.2%, specificity: 85%). Our results demonstrate the potential and feasibility of using an evolutionary tumor dynamics model in combination with the appropriate data to aid in the adaptive management of prostate cancer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Failure Modes, Mechanisms, Effects, and Criticality Analysis of Ceramic Anodes of Solid Oxide Fuel Cells

Solid oxide fuel cells (SOFCs) are a highly efficient chemical to electrical energy conversion devices that have potential in a global energy strategy. The wide adoption of SOFCs is currently limited by cost and concerns about cell durability. Improved understanding of their degradation modes and mechanisms combined with reduction–oxidation stable anodes via all-ceramic-anode cell technology are expected to lead to durability improvements, while economies of scale for production will mitigate cost of commercialization. This paper presents an Ishikawa analysis and a failure modes, mechanisms, effects, and criticality analysis (FMMECA) for all-ceramic anode based SOFCs. FMMECA takes into account the life cycle conditions, multiple failure mechanisms, and their potential effects on fuel-cell health and safety.

42 ENGINEERING↗
DOE OSTI2024

Generic and ML Workloads in an HPC Datacenter: Node Energy, Job Failures, and Node-Job Analysis

HPC datacenters offer a backbone to the modern digital society. Increasingly, they run Machine Learning (ML) jobs next to generic, compute-intensive workloads, supporting science, business, and other decision-making processes. However, understanding how ML jobs impact the operation of HPC datacenters, relative to generic jobs, remains desirable but understudied. In this work, we leverage long-term operational data, collected from a national-scale production HPC datacenter, and statistically compare how ML and generic jobs can impact the performance, failures, resource utilization, and energy consumption of HPC datacenters. Our study provides key insights, e.g., ML-related power usage causes GPU nodes to run into temperature limitations, median/mean runtime and failure rates are higher for ML jobs than for generic jobs, both ML and generic jobs exhibit highly variable arrival processes and resource demands, significant amounts of energy are spent on unsuccessfully terminating jobs, and concurrent jobs tend to terminate in the same state. We open-source our cleaned-up data traces on Zenodo (https://doi. org/10.5281/zenodo.13685426), and provide our analysis toolkit as software hosted on GitHub (https://github.com/atlarge-research/2024-icpads-hpc-workload-characterization). This study offers multiple benefits for data center administrators, who can improve operational efficiency, and for researchers, who can further improve system designs, scheduling techniques, etc.

crossanalysis↗
NASA NTRS2000

Human versus automation in responding to failures: an expected-value analysis

A simple analytical criterion is provided for deciding whether a human or automation is best for a failure detection task. The method is based on expected-value decision theory in much the same way as is signal detection. It requires specification of the probabilities of misses (false negatives) and false alarms (false positives) for both human and automation being considered, as well as factors independent of the choice--namely, costs and benefits of incorrect and correct decisions as well as the prior probability of failure. The method can also serve as a basis for comparing different modes of automation. Some limiting cases of application are discussed, as are some decision criteria other than expected value. Actual or potential applications include the design and evaluation of any system in which either humans or automation are being considered.

NASA Discipline Space Human Factors↗