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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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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Open data sets for assessing photovoltaic system reliability

Photovoltaic (PV) systems have become a cornerstone of renewable energy strategies, particularly due to the significant reduction in solar power costs over the past decade. However, the long-term reliability of PV installations presents a persistent challenge, requiring the development of advanced monitoring and predictive maintenance strategies. A wide range of data types is used to evaluate the health of PV systems, including environmental conditions, electrical performance, and inspection imagery. These data enable methodologies such as machine learning (ML) models for lifetime prediction and computer vision techniques for defect detection. However, the acquisition of high-quality and comprehensive data is difficult, particularly in terms of long-term consistency and data variety. Publicly available data sets serve as valuable resources for addressing these challenges, but they often suffer from fragmentation and are difficult to access. This paper presents a comprehensive review of existing open-source data sets related to PV degradation, analyzing their features, functionalities, and potential applications. We categorize these data sets based on the specific aspects of PV system information they cover, such as environmental conditions, operational monitoring, image inspection and module materials, and propose relevant tools and ML models for processing them. In addition, we propose practices for future data collection and usage, while also discussing potential directions in data-driven research. Our aim is to enhance data utilization and publication among researchers and industry professionals, promoting a deeper understanding of the role of data in enhancing the performance and durability of PV systems.

14 SOLAR ENERGY↗
DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

High-energy-resolution X-ray monochromator calibration using the detailed-balance principle

A new method is presented to calibrate an X-ray energy scale with sub-meV relative accuracy by using the detailed-balance principle of the phonon creation and annihilation. This method is conveniently used to define or verify the energy scale of high-energy-resolution monochromators that are used in inelastic X-ray scattering and nuclear resonant inelastic X-ray scattering instruments at synchrotron radiation facilities. This method does not rely on sample properties and its precision only depends on the statistical data quality. Well calibrated instruments are essential for reliable comparison of data sets obtained at different synchrotron radiation beamlines, of data with theoretical predictions, and of data from other techniques such as neutron or light scattering. The principle of the detailed-balance method is described in this paper and demonstrated experimentally.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Using Synchrophasor Status Word as Data Quality Indicator: What to Expect in the Field?

Data quality plays a crucial role in successful applications of synchrophasor data in power system operation and control. This paper presents the results of a data quality analysis of a multi-year field-recorded synchrophasor dataset. The analysis has identified several typical data quality issues encountered in the field data. An examination of the PMU status words included with the dataset has revealed several inconsistent implementations and the lack of correlation between the PMU data quality and the status word, which impacts the usefulness of such information. Our investigation has concluded that the status word alone as found in the recorded field dataset could not be used as a reliable indicator of data quality for field-recorded data. Several recommendations are proposed to improve the usefulness of the PMU status word.

Cheng, Zheyuan↗
DOE OSTI2015

A Survey on Next-generation Power Grid Data Architecture

The operation and control of power grids will increasingly rely on data. A high-speed, reliable, flexible and secure data architecture is the prerequisite of the next-generation power grid. This paper summarizes the challenges in collecting and utilizing power grid data, and then provides reference data architecture for future power grids. Based on the data architecture deployment, related research on data architecture is reviewed and summarized in several categories including data measurement/actuation, data transmission, data service layer, data utilization, as well as two cross-cutting issues, interoperability and cyber security. Research gaps and future work are also presented.

You, Shutang↗
DOE OSTI2023

An Investigation of Time Distributions for Task Primitives to Support the HUNTER Dynamic Human Reliability Analysis

As an effort to support data collection for dynamic human reliability analysis (HRA), this study investigates time distributions for task primitives defined in the Goals, Operators, Methods, and Selection rules (GOMS)–Human Reliability Analysis (HRA) method. GOMS-HRA was developed to provide cognition-based time and human error probability information for dynamic HRA calculation in the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) framework. HUNTER is a framework to support the dynamic modelling of human error in conjunction with other modelling tools. In this paper, we investigate time distributions using experimental data collected from the Simplified Human Error Experimental Program (SHEEP) study, which suggests an HRA data collection framework to complement full-scope simulator research as well as collect input data for dynamic HRA using simplified simulators such as the Rancor Microworld Simulator. In this study, time required for GOMS-HRA task primitives to satisfy thirteen statistical distributions is investigated. Then, the time distributions from student operators and professional operators are compared and discussed. As a result, this study identified several time distributions on five GOMS-HRA task primitives at a statistically significant level. According to analyses to date, a greater number of significant time distributions was found in abnormal or emergency operating procedures rather than standard operating procedures. In the future, it is expected that the result of this study can provide objective reference on elapsed time data for task primitives as well as help to realistically simulate scenarios within dynamic HRA.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Community Energy by Design: A Simulation-Based Design Workflow Using Measured Data Clustering to Calibrate Urban Building Energy Models (UBEMs)

This paper presents a workflow that informs urban design decisions using measured data clustering to calibrate urban building energy models. The method's goal is to support urban design in terms of form, building systems configurations, as well as influencing user behavior aspects in the built environment through a systemic analysis of measured data to develop reliable future-case design scenario energy models. Detailed data on appliance-level electricity use are employed via data clustering to calibrate a urban building energy model for the Mueller community in Austin, TX, USA. The data were collected by the Pecan Street Institute for a year in 2014 from consumers in Austin and other surrounding cities. First, collected energy data were restructured and cleaned from corrupt and/or missing information. Second, in order to identify common energy use patterns, a model-based clustering algorithm for functional data was applied. Behavioral/usage profiles are determined through clustering and translated into usage schedules and behaviors. As a result, an urban building energy model built in the urban modeling interface (umi) was calibrated, with fully calibrated and semi-calibrated buildings, within a maximum calibration error margin of 14% for daily-scale averages. Finally, an illustration of calibrated-urban building energy model design case scenarios is presented, and implications on community energy potential effects are discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Reverberation Mapping of PG 0934+013 with the Southern African Large Telescope

We present the variability and time-lag measurements of PG 0934+013 based on a photometric and spectroscopic monitoring campaign over a two year period. We obtained 46 epochs of data from the spectroscopic campaign, which was carried out using the Southern African Large Telescope with ∼1 week cadence over two sets of four month-long observing period, while we obtained 80 epochs of B-band imaging data using a few 1 m class telescopes. Due to the seven month gap between the two observing periods, we separately measured the time lags of broad emission lines, including Hβ, by comparing the emission line light curve with the B-band continuum light curve using the cross-correlation function techniques. We determined the Hβ lag, τ{sub cent}=8.46{sub −2.14}{sup +2.08} days in the observed frame based on Year 2 data, while the time lag from Year 1 data was not reliably determined. Using the rms spectrum of Year 2 data, we measured the Hβ line dispersion σ{sub line} = 668 ± 44 km s{sup −1} after correcting for the spectral resolution. Adopting a virial factor f = 4.47 from Woo et al. (2015), we determined the black hole mass M {sub BH} = 3.13{sub −0.93}{sup +0.91}×10{sup 6} M {sub ⊙,} based on the Hβ time lag and velocity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

On the reliability of nanoindentation hardness of Al{sub 2}O{sub 3} films grown on Si-wafer by atomic layer deposition

The interest in applying thin films on Si-wafer substrate for microelectromechanical systems devices by using atomic layer deposition (ALD) has raised the demand on reliable mechanical property data of the films. This study aims to find a quick method for obtaining nanoindentation hardness of thin films on silicon with improved reliability. This is achieved by ensuring that the film hardness is determined under the condition that no plastic deformation occurs in the substrate. In the study, ALD Al{sub 2}O{sub 3} films having thickness varying from 10 to 600 nm were deposited on a single-side polished silicon wafer at 300 °C. A sharp cube-corner indenter was used for the nanoindentation measurements. A thorough study on the Si-wafer reference revealed that at a specific contact depth of about 8 nm the wafer deformation in loading transferred from elastic to elastic–plastic state. Furthermore, the occurrence of this transition was associated with a sharp increase of the power-law exponent, m, when the unloading data were fitted to a power-law relation. Since m is only slightly material dependent and should fall between 1.2 and 1.6 for different indenter geometry having elastic contact to common materials, it is proposed that the high m values are the results from the inelastic events during unloading. This inelasticity is linked to phase transformations during pressure releasing, a unique phenomenon widely observed in single crystal silicon. Therefore, it is concluded that m could be used to monitor the mechanical state of the Si substrate when the whole coating system is loaded. A suggested indentation depth range can then be assigned to each film thickness to provide guidelines for obtaining reliable property data. The results show good consistence for films thicker than 20 nm and the nanoindentation hardness is about 11 GPa independent of film thickness.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

An Accurate Fast Fluid Dynamics Model for Data Center Applications

Traditional CFD is broadly useful in the design and operation of reliable and efficient data centers. It is nevertheless computationally expensive, particularly when employed for design optimization, which usually requires multiple simulations. To speed-up calculations while retaining essential airflow physics, several researchers have turned to an alternative CFD methodology, namely, fast fluid dynamics (FFD). FFD has been reported to be much faster than traditional CFD, but at an assumed (acceptable) trade-off of reduced accuracy. However, a recent comparison of FFD and traditional CFD for data center plenum applications produced nearly indistinguishable results and suggested that previously-reported FFD/traditional-CFD differences were due primarily to inconsistent: 1) advection schemes, 2) computational grids, and 3) turbulence models. The present paper extends this FFD/traditional-CFD comparison to the data center whitespace and confirms the finding that a true like-for-like comparison produces nearly identical predictions. Our FFD implementation utilizes a first-order upwind finite-volume scheme for advection like traditional CFD, so alternative advection schemes (e.g., semi-Lagrangian) are not considered further here. Likewise, the effect of grid choice on traditional-CFD predictions is well known. However, the effect of turbulence model for data center applications has not been reported extensively so we do consider this topic further here. We compare the standard k-e and a simpler algebraic model to benchmark experimental data from a real data center. We find that the algebraic turbulence model predicts rack-inlet temperatures at a similar level-of-accuracy as the k-e model for our fairly-simple-airflow reference data center.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Experimental study of the downstream effect of LOCA-generated debris on the pressure drop of full scale fuel assembly

The emergency core cooling system (ECCS) works to cool the reactor core in the event of a postulated loss-of-coolant accident (LOCA). The long-term reactor cooling water of ECCS comes from containment sump. Usually, the containment sump contains one or more screens to prevent the components of the ECCS from debris which could be washed into the sump. Large debris is blocked by the sump screens. However, some of the debris, such as fibrous, chemical precipitates and small particles may be ingested into the ECCS and subsequently into the reactor coolant systems (RCS), which would challenge the long-term core cooling (LTCC) and threaten the reactor core safety. NRC address this concern Generic Letter 2004-02. Westinghouse has evaluated the AP1000 1/4 height fuel assembly head-loss test in response to the issue. AVERA also perform the scaled 1/3 height mock-up fuel assembly test. The debris effect on the pressure drop of half-length mock-up PLUS7 fuel assembly of APR1400 were performed in KHNP. Numerical simulation by RELAP5-3D were performed for the debris-generated core blockage and full core as well as core bypass blockage condition were considered. However, the test in the works mentioned above use the scaled down FA (1/4, 1/3 or 1/2 length of a full FA), and didn't give the relationship between pressure drop and debris batches. The purpose of this study was to extend the debris effect to the full scale PWR fuel assembly, and identified batch by batch effect of debris to head loss of FA. The specific experimental procedure is concluded. The pressure drop test without adding debris is performed at the beginning and then three kinds of debris are added to the test loop step by step to test their effect on the pressure drop of fuel assembly. Two kinds of tests have been carried out, and the quantitative pressure data are obtained and analysis is provided. This work provides reliable and quantitative data for nuclear power plant (NPP) downstream effect analysis and safety analysis program, which is important to for licensing of NPP and further benchmark exercise. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Data for Renewable Energy Planning, Policy, and Investment

Reliable, robust, and validated data are critical for informed planning, policy development, and investment in the clean energy sector. The Renewable Energy (RE) Explorer was developed to support data-driven renewable energy analysis that can inform key renewable energy decisions globally. This document presents the types of geospatial and other data at the core of renewable energy analysis and decision making. Individual data sets used to inform decisions vary in relation to spatial and temporal resolution, quality, and overall usefulness. From Data to Decisions, a complementary geospatial data and analysis decision guide, provides an in-depth view of these and other considerations to enable data-driven planning, policymaking, and investment. Data support a wide variety of renewable energy analyses and decisions, including technical and economic potential assessment, renewable energy zone analysis, grid integration, risk and resiliency identification, electrification, and distributed solar photovoltaic potential. This fact sheet provides information on the types of data that are important for renewable energy decision making using the RE Data Explorer or similar types of geospatial analysis tools.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

A Report on Simulation-Driven Reliability and Failure Analysis of Large-Scale Storage Systems

High-performance computing (HPC) storage systems provide data availability and reliability using various hardware and software fault tolerance techniques. Usually, reliability and availability are calculated at the subsystem or component level using limited metrics such as, mean time to failure (MTTF) or mean time to data loss (MTTDL). This often means settling on simple and disconnected failure models (such as exponential failure rate) to achieve tractable and close-formed solutions. However, such models have been shown to be insufficient in assessing end-to-end storage system reliability and availability. We propose a generic simulation framework aimed at analyzing the reliability and availability of storage systems at scale, and investigating what-if scenarios. The framework is designed for an end-to-end storage system, accommodating the various components and subsystems, their interconnections, failure patterns and propagation, and performs dependency analysis to capture a wide-range of failure cases. We evaluate the framework against a large-scale storage system that is in production and analyze its failure projections toward and beyond the end of lifecycle. We also examine the potential operational impact by studying how different types of components affect the overall system reliability and availability, and present the preliminary results

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Machine Learning-Driven Reliability Estimation of PV Inverters Considering Alert-Ambient Variability

Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.

14 SOLAR ENERGY↗
DOE OSTI2014

Weak data do not make a free lunch, only a cheap meal

Refinement and analysis of four structures with various data resolution cutoffs suggests that at present there are no reliable criteria for judging the diffraction data resolution limit and the condition I/σ(I) = 2.0 is reasonable. However, extending the limit by about 0.2 Å beyond the resolution defined by this threshold does not deteriorate the quality of refined structures and in some cases may be beneficial. Four data sets were processed at resolutions significantly exceeding the criteria traditionally used for estimating the diffraction data resolution limit. The analysis of these data and the corresponding model-quality indicators suggests that the criteria of resolution limits widely adopted in the past may be somewhat conservative. Various parameters, such as R{sub merge} and I/σ(I), optical resolution and the correlation coefficients CC{sub 1/2} and CC*, can be used for judging the internal data quality, whereas the reliability factors R and R{sub free} as well as the maximum-likelihood target values and real-space map correlation coefficients can be used to estimate the agreement between the data and the refined model. However, none of these criteria provide a reliable estimate of the data resolution cutoff limit. The analysis suggests that extension of the maximum resolution by about 0.2 Å beyond the currently adopted limit where the I/σ(I) value drops to 2.0 does not degrade the quality of the refined structural models, but may sometimes be advantageous. Such an extension may be particularly beneficial for significantly anisotropic diffraction. Extension of the maximum resolution at the stage of data collection and structure refinement is cheap in terms of the required effort and is definitely more advisable than accepting a too conservative resolution cutoff, which is unfortunately quite frequent among the crystal structures deposited in the Protein Data Bank.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Anticipating Optical Availability in Hybrid RF/FSO Links Using RF Beacons and Deep Learning

Radiofrequency (RF) communications offer reliable but low data rates and energy-inefficient satellite links, while free-space optical (FSO) promises high bandwidth but struggles with disturbances imposed by atmospheric effects. A hybrid RF/FSO architecture aims to achieve optimal reliability along with high data rates for space communications. Accurate prediction of dynamic ground-to-satellite FSO link availability is critical for routing decisions in low-earth orbit constellations. In this paper, we propose a system leveraging ubiquitous RF links to proactively forecast FSO link degradation prior to signal drops below threshold levels. This enables pre-calculation of rerouting to maximally maintain high data rate FSO links throughout the duration of weather effects. We implement a supervised learning model to anticipate FSO attenuation based on the analysis of RF patterns. Through the simulation of a dense lower earth orbit (LEO) satellite constellation, we demonstrate the efficacy of our approach in a simulated satellite network, highlighting the balance between predictive accuracy and prediction duration. An emulated cloud attenuation model is proposed to provide insight into the temporal profiles of RF signals and their correlation to FSO channel dynamics. Our investigation sheds light on the trade-offs between prediction horizon and accuracy arising from RF beacon numbers and proximity.

FSO availability↗
DOE OSTI2015

Probing microhydration effect on the electronic structure of the GFP chromophore anion: Photoelectron spectroscopy and theoretical investigations

The photophysics of the Green Fluorescent Protein (GFP) chromophore is critically dependent on its local structure and on its environment. Despite extensive experimental and computational studies, there remain many open questions regarding the key fundamental variables that govern this process. One outstanding problem is the role of autoionization as a possible relaxation pathway of the excited state under different environmental conditions. This issue is considered in our work through combined experimental and theoretical studies of microsolvated clusters of the deprotonated p-hydroxybenzylidene-2,3-dimethylimidazolinone anion (HBDI{sup −}), an analog of the GFP chromophore. Through selective generation of microsolvated structures of predetermined size and subsequent analysis of experimental photoelectron spectra by high level ab initio methods, we are able to precisely identify the structure of the system, establish the accuracy of theoretical data, and provide reliable description of auto-ionization process as a function of hydrogen-bonding environment. Our study clearly illustrates the first few water molecules progressively stabilize the excited state of the chromophore anion against the autodetached neutral state, which should be an important trait for crystallographic water molecules in GFPs that has not been fully explored to date.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗