Continuous Reliability Enhancements for Wind (CREW) Database and Analysis Program.
Abstract not provided.
Heinrichs, Todd D.↗
SEARCH · Engineering Papers
Results for “Reliability analysis”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Safety Risk Reliability Model Library
SR2ML is a software package which contains a set of safety and reliability models designed to be interfaced with the INL developed RAVEN code. These models can be employed to perform both static and dynamic system risk analysis and determine risk importance of specific elements of the considered system. Two classes of reliability models have been developed; the first class includes all classical reliability models (Fault-Trees, Event-Trees, Markov models and Reliability Block Diagrams) which have been extended to deal not only with Boolean logic values but also time dependent values. The second class includes several components aging models. Models of these two classes are designed to be included in a RAVEN ensemble model to perform time dependent system reliability analysis (dynamic analysis). Similarly, these models can be interfaced with system analysis codes to determine failure time of systems and evaluate accident progression (static analysis).
Wang, Congjian↗
Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders
Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.
Chemistry↗
Data requirements for improving the Quantitative Risk Assessment of liquid hydrogen storage systems
Quantitative Risk Assessment (QRA) supports the development of risk-informed safety codes and standards which are employed to enable the safe deployment of hydrogen technologies essential to decarbonize the transportation sector. System reliability data is a necessary input for rigorous QRA. Here, the lack of reliability data for bulk liquid hydrogen (LH 2 ) storage systems located on site at fueling stations limits the use of QRAs. In turn, this hinders the ability to develop the necessary safety codes and standards that enable worldwide deployment of these stations. Through a QRA-based analysis of a LH 2 storage system, this work focuses on identifying relevant scenario and probability data currently available and ascertaining future data collection requirements regarding risks specific to liquid hydrogen releases. The work developed consists of the analysis of a general bulk LH 2 storage system design located at a hydrogen fueling station. Failure Mode and Effect Analysis (FMEA) and traditional QRA modeling tools such as Event Sequence Diagrams (ESD) and Fault Tree Analysis (FTA) are employed to identify, rank, and model risk scenarios related to the release of LH 2 . Based on this analysis, scenario and reliability data needs to add LH 2 -related components to QRA are identified with the purpose of improving the future safety and risk assessment of these systems.
08 HYDROGEN↗
Updated Outcome and Analysis of Tumor Response in Mobile Spine and Sacral Chordoma Treated With Definitive High-Dose Photon/Proton Radiation Therapy
Purpose: Treatment of spine and sacral chordoma generally involves surgical resection, usually in conjunction with radiation therapy. In certain circumstances where resection may result in significant neurologic or organ dysfunction, patients can be treated definitively with radiation therapy alone. Herein, we report the outcome and the assessment of tumor response to definitive radiation therapy. Methods and Materials: A retrospective analysis was performed on 40 patients with unresected chordoma treated with photon/proton radiation therapy. Nineteen patients had complete sets of imaging scans. The soft tissue and bone compartments of the tumor were defined separately. Tumor response was evaluated by the modified Response Evaluation Criteria in Solid Tumors (RECIST) and volumetric analysis. Results: With a median follow-up time of 50.3 months, the rates of 5-year local control, overall survival, disease-specific survival, and distant failure were 85.4%, 81.9%, 89.4%, and 20.2%, respectively. Eighty-four computed tomographic and magnetic resonance imaging scans were reviewed. Among the 19 patients, only 4 local failures occurred, and the median tumor dose was 77.4 GyRBE. Analysis at a median follow-up time of 18 months showed significant volumetric reduction of the total target volume (TTV) and the soft tissue target volume (STTV) within the first 24 months after treatment initiation, followed by further gradual reduction throughout the rest of the follow-up period. The median maximum percentage volumetric regressions of TTV and STTV were 43.2% and 70.4%, respectively. There was only a small reduction in bone target volume over time. In comparison with the modified RECIST, volumetric analysis was more reliable, more reproducible, and could help in measuring minimal changes in the tumor volume. Conclusion: These results continue to support the use of high-dose definitive radiation therapy for selected patients with unresected spine and sacral chordomas. Assessment of tumor response to radiation therapy by volumetric analysis is superior to modified RECIST in chordoma patients. Evaluating the soft tissue target volume is an excellent indicator of tumor response.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Dispersion of magnetic fields in molecular clouds. IV. Analysis of interferometry data
We expand on the dispersion analysis of polarimetry maps toward applications to interferometry data. We show how the filtering of low spatial frequencies can be accounted for within the idealized Gaussian turbulence model, initially introduced for single-dish data analysis, to recover reliable estimates for correlation lengths of magnetized turbulence, as well as magnetic field strengths (plane-of-the-sky component) using the Davis–Chandrasekhar–Fermi method. We apply our updated technique to TADPOL/CARMA data obtained on W3(OH), W3 Main, and DR21(OH). For W3(OH), our analysis yields a turbulence correlation length δ≃19 mpc, a ratio of turbulent-to-total magnetic energy 〈B{sub t}{sup 2}〉/〈B{sup 2}〉≃0.58, and a magnetic field strength B{sub 0}∼1.1 mG; for W3 Main δ≃22 mpc, 〈B{sub t}{sup 2}〉/〈B{sup 2}〉≃0.74, and B{sub 0}∼0.7 mG; while for DR21(OH) δ≃12 mpc, 〈B{sub t}{sup 2}〉/〈B{sup 2}〉≃0.70, and B{sub 0}∼1.2 mG.
79 ASTRONOMY AND ASTROPHYSICS↗
Using XFRACAS as a PVROM database tool.
ReliaSoft s XFRACAS is a tool chosen by Sandia National Laboratories (SNL) for the purpose of collecting and organizing photovoltaic (PV) system field data that is to be used in assessing PV system reliability. It is a Web-based, closed-loop, incident (failure) reporting, analysis, and corrective action system software package designed for the acquisition, management and analysis of quality and reliability data from multiple sources. The ability to export PV system times-to-failure and times-to-suspension for ready analysis by ReliaSoft s Weibull++ and RGA was a primary consideration in choosing XFRACAS as a reliability data collection tool.
97 MATHEMATICS AND COMPUTING↗
Valuing intra-day coordination of electric power and natural gas system operations
Not Available
Craig, Michael↗
The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: Baryon Acoustic Oscillations in Fourier-space
Here, we analyse the Baryon Acoustic Oscillation (BAO) signal of the final Baryon Oscillation Spectroscopic Survey (BOSS) data release (DR12). Our analysis is performed in Fourier-space, using the power spectrum monopole and quadrupole. The dataset includes 1 198 006 galaxies over the redshift range 0.2 < z < 0.75. We divide this dataset into three (overlapping) redshift bins with the effective redshifts z eff = 0.38, 0.51 and 0.61. We demonstrate the reliability of our analysis pipeline using N-body simulations as well as 1000 MultiDark-Patchy mock catalogues, which mimic the BOSS-DR12 target selection. We apply density eld reconstruction to enhance the BAO signal-to-noise ratio. By including the power spectrum quadrupole we can sep-arate the line-of-sight and angular modes, which allows us to constrain the angular diameter distance D A (z) and the Hubble parameter H ( z ) separately. We obtain two independent 1 : 6% and 1 : 5% constraints on D A (z) and 2.9% and 2.3% constraints on H(z) for the low (z eff = 0.38) and high (z eff = 0.61) redshift bin, respectively. We obtain two independent 1% and 0.9% constraints on the angular averaged distance D V (z), when ignoring the Alcock-Paczynski e ect. The detection significance of the BAO signal is of the order of 8σ (post-reconstruction) for each of the three redshift bins. Our results are in good agreement with the Planck prediction within CDM. This paper is part of a set that analyses the final galaxy clustering dataset from BOSS. The measurements and likelihoods presented here are combined with others in Alam et al. (2016) to produce the final cosmological constraints from BOSS.
79 ASTRONOMY AND ASTROPHYSICS↗
Assessment of the frequency and nature of erroneous x-ray photoelectron spectroscopy analyses in the scientific literature
Here, this study was undertaken to understand the extent and nature of problems in x-ray photoelectron spectroscopy (XPS) data reported in the literature. It first presents an assessment of the XPS data in three high-quality journals over a six-month period. This analysis of 409 publications showing XPS spectra provides insight into how XPS is being used, identifies the common mistakes or errors in XPS analysis, and reveals which elements are most commonly analyzed. More than 65% of the 409 papers showed fitting of XP spectra. An ad hoc group (herein identified as “the committee”) of experienced XPS analysts reviewed these spectra and found that peak fitting was a common source of significant errors. The papers were ranked based on the perceived seriousness of the errors, which ranged from minor to major. Major errors, which, in the opinion of the ad hoc committee, can render the interpretation of the data meaningless, occurred when fitting protocols ignored underlying physics and chemistry or contained major errors in the analysis. Consistent with other materials analysis data, ca. 30% of the XPS data or analysis was identified as having major errors. Out of the publications with fitted spectra, ca. 40% had major errors. The most common elements analyzed by XPS in the papers sampled and researched at an online database, include carbon, oxygen, nitrogen, sulfur, and titanium. A scrutiny of the papers showing carbon and oxygen XPS spectra revealed the classes of materials being studied and the extent of problems in these analyses. As might be expected, C 1s and O 1s analyses are most often performed on sp2-type materials and inorganic oxides, respectively. These findings have helped focus a series of XPS guides and tutorials that deal with common analysis issues. The extent of problematic data is larger than the authors had expected. Quantification of the problem, examination of some of the common problem areas, and the development of targeted guides and tutorials may provide both the motivation and resources that enable the community to improve the overall quality and reliability of XPS analysis reported in the literature.
Major, George H.↗
A large-scale study of soft-errors on GPUs in the field
Parallelism provided by the GPU architecture has enabled domain scientists to simulate physical phenomena at a much faster rate and finer granularity than what was previously possible by CPU-based large-scale clusters. Architecture researchers have been investigating reliability characteristics of GPUs and innovating techniques to increase the reliability of these emerging computing devices. Such efforts are often guided by technology projections and simplistic scientific kernels, and performed using architectural simulators and modeling tools. Lack of large-scale field data impedes the effectiveness of such efforts. This study attempts to bridge this gap by presenting a large-scale field data analysis of GPU reliability. We characterize and quantify different kinds of soft-errors on the Titan supercomputer's GPU nodes. Our study uncovers several interesting and previously unknown insights about the characteristics and impact of soft-errors.
Nie, Bin↗
Consistent thermodynamic properties for alicyclic components of jet fuels: Experimental data, estimation methods, and homologous series trends
Alkylcycloalkanes represent a significant fraction of jet fuel components. An evaluation of their thermodynamic properties, enthalpies of formation in liquid and gas phases and enthalpies of vaporization, was conducted. A combination of available experimental data, up-to-date group-contribution methods, high-level quantum-chemical calculations, and homologous series trends was used to identify outliers and to recommend the most reliable values. The group-contribution approach was found to work well for the enthalpies of vaporization. Its performance for the enthalpies of formation in the liquid and gas phases was found to be substantially less effective, especially considering notable differences in this property among stereoisomers. Computationally affordable high-level ab initio results and homologous series trend analysis appeared more reliable. In conclusion, the recommended property values for 212 individual compounds and their isomeric mixtures were provided.
09 BIOMASS FUELS↗
Modeling of microstructural effects on electromigration failure
Current electromigration models used for simulation and analysis of interconnect reliability lack the appropriate description of metal microstructure and consequently have a very limited predictive capability. Therefore, the main objective of our work was obtaining more sophisticated electromigration tools. The problem is addressed through a combination of different levels of atomistic modeling and already available, continuum level macroscopic models. A novel method for an ab initio calculation of the effective valence for electromigration is presented and its application on the analysis of EM behavior is demonstrated. Additionally, a simple analytical model for the early electromigration lifetime is obtained. We have shown that its application provides a reasonable estimate for the early electromigration failures including the effect of microstructure. A simulation study is also applied on electromigration failure in tin solder bumps, where it contributed the understanding of the role of tin crystal anisotropy in the degradation mechanism of solder bumps.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Bridging Equipment Reliability Data and Risk Informed Decisions in a Plant Operation Context
Industry equipment reliability and asset management programs are essential elements that help ensure the safe and economical operation of nuclear power plants. The effectiveness of these programs is addressed in several industry-developed and regulatory programs. The Risk-Informed Asset Management (RIAM) project is tasked to develop tools in support of the equipment reliability and asset management programs at nuclear power plants. These tools are designed to create a direct bridge between component health/lifecycle data and decision making (e.g., maintenance scheduling and project prioritization). The goal of this article is to provide a guide for specific use cases that the RIAM project is targeting. We have grouped uses cases into three main areas. The first area focuses on the analysis of equipment reliability data with a particular emphasis on condition-based data, such as test/surveillance reports and component monitoring data. The second area focuses on the integration of equipment reliability into system/plant reliability models to determine system/plant health and identify the components that are critical to maintain an operational system. Lastly, the third area manages plant resources, such as maintenance activities and replacement scheduling using optimization methods. Here the primary focus is on supporting typical system engineer decisions regarding maintenance activity scheduling and component aging management. This is performed in a risk-informed context where the term “risk” is broadly constructed to include both plant reliability and economics. This framework combines data analytics tools to analyze equipment reliability data with risk-informed methods designed to support system engineer decisions (e.g., maintenance and replacement schedules, optimal maintenance posture) in a customizable workflow.
97 - MATHEMATICS AND COMPUTING↗
Ultrahigh performance LC/FT-MS non-targeted screening for biomass burning organic aerosol with MZmine2 and MFAssignR
In recent years, ultrahigh performance liquid chromatography Fourier transform mass spectrometry (LC/FT-MS) based non-targeted screening (NTS) methods have become increasingly popular for comprehensive analysis of complex organic mixtures. However, applying these methods for environmental complex mixture analysis is challenging due to the extreme complexity of natural samples and a lack of standard samples or surrogates for environmental complex mixtures. Furthermore, limited molecular markers in the databases and insufficient data processing software workflows make the application of these methods more challenging for environmental complex mixtures. In this work, we implement a new NTS data processing workflow to process data collected from ultrahigh performance liquid chromatography and Fourier transform Orbitrap Elite Mass Spectrometry (LC/FT-MS) by combining MZmine2 and MFAssignR, two opensource data processing tools and commercial Mesquite liquid smoke as a surrogate for biomass burning organic aerosol. MZmine2.53 data extraction followed MFAssignR molecular formula assignment offered noise free and highly accurate 1733 individual molecular formulas presented in liquid smoke with 4906 molecular species, including isomers. The results of this new approach were consistent with the results of direct infusion FT-MS analysis confirming its reliability. Over 90% of the molecular formulas presented in mesquite liquid smoke were matched with the molecular formulas of ambient biomass burning organic aerosol. This suggests the potential use of commercial liquid smoke as a surrogate for biomass burning organic aerosol research. Furthermore, the presented method significantly improves the identification of the molecular composition of biomass burning organic aerosol by successfully addressing some of the limitations related to the data analysis and giving a semi quantitative insight into the analysis.
54 ENVIRONMENTAL SCIENCES↗
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↗
Chapter 14 - Reliability of Wind Turbines
The global wind energy industry has grown at a fast pace during the past half-decade. Advancements from design and manufacturing to operation and maintenance have led to reduced capital and maintenance costs, which make wind power an indispensable source for a comprehensive solution to global electricity needs. Once wind turbines are installed, the opportunity to lower wind power costs is mainly through improved operation and maintenance practices. Modern wind turbines are equipped with tens or hundreds of measurement channels and are generating an abundance of data, with lot of efforts being put into data analysis by both the research community and the industry. One type of analysis is through the exploration of reliability engineering methods based on readily available data or maintenance records collected at typical wind power plants. If adopted and conducted appropriately, these analyses can quickly save operation and maintenance costs in a potentially impactful manner. The wind industry has adopted this discipline more broadly in recent years. This chapter discusses wind turbine reliability by highlighting the methodology of reliability engineering life data analysis. It first briefly discusses the fundamentals of wind turbine reliability and the current industry status. Then, the reliability engineering method for life analysis, including data collection, model development, and forecasting, is presented in detail and illustrated through two case studies. The chapter concludes with some remarks on potential opportunities to improve wind turbine reliability. An owner and operator's perspective is taken and mechanical components are used to exemplify the potential benefits of reliability engineering analysis to improve wind turbine reliability and availability.
database↗
Porous Enzymatic Membrane for Nanotextured Glucose Sweat Sensors with High Stability toward Reliable Noninvasive Health Monitoring
Development of reliable glucose sensors for noninvasive monitoring without interruption or limiting users' mobility is highly desirable, especially for diabetes diagnostics, which requires routine/long-term monitoring. However, their applications are largely limited by the relatively poor stability. In this paper, a porous membrane is synthesized for effective enzyme immobilization and it is robustly anchored to the modified nanotextured electrode solid contacts, so as to realize glucose sensors with significantly enhanced sensing stability and mechanical robustness. To the best of our knowledge, this is the first report of utilizing such nanoporous membranes for electrochemical sensor applications, which eliminates enzyme escape and provides a sufficient surface area for molecular/ion diffusion and interactions, thus ensuring the sustainable catalytic activities of the sensors and generating reliable measureable signals during noninvasive monitoring. The as-assembled nanostructured glucose sensors demonstrate reliable long-term stable monitoring with a minimal response drift for up to 20 h, which delivers a remarkable enhancement. Moreover, they can be integrated into a microfluidic sensing patch for noninvasive sweat glucose monitoring. The as-synthesized nanostructured glucose sensors with remarkable stability can inspire developments of various enzymatic biosensors for reliable noninvasive composition analysis and their ultimate applications in predictive clinical diagnostics, personalized health-care monitoring, and chronic diseases management.
36 MATERIALS SCIENCE↗