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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Data-Driven Probabilistic Voltage Risk Assessment of MiniWECC System with Uncertain PVs and Wind Generations using Realistic Data

Here, it is found from actual data that due to generation dispatch and uncertain renewable generations and loads with complicated correlations, inferring the probabilistic distributions for uncertain inputs is challenging. Many probabilistic power flow approaches have been developed in the literature but their validations using realistic systems and data are lacking. This paper proposes a data-driven probabilistic analysis approach for system risk assessment of the miniWECC system using actual data. The sparse Gaussian process (SGP) is advocated to quantify the impacts of uncertain inputs on voltage security. SGP does not need the probability distribution function of uncertain inputs, can handle correlations and is highly computationally efficient. Results on the miniWECC system using realistic data show that SGP outperforms existing approaches and is able to quantify the voltage violation risks.

17 WIND ENERGY↗
DOE OSTI2016

MO-FG-202-03: Efficient Data Collection of Continuous 2D and Discrete Relative Dosimetric Data for Annual LINAC QA Using TrueBeam Developer Mode and a 1D Scanning Tank

Purpose: To develop a method to exploit real-time dynamic machine and couch parameter control during linear accelerator (LINAC) beam delivery to facilitate efficient performance of TG-142 suggested, Annual LINAC QA tests. Methods: Varian’s TrueBeam Developer Mode (Varian Medical Systems, Palo Alto, CA) facilitates control of Varian’s TrueBeam LINAC via instructions provided in Extensible Markup Language (XML) files. This allows machine and couch parameters to be varied dynamically, in real-time, during beam delivery. Custom XML files were created to allow for the collection of (1) continuous Tissue Maximum Ratios (TMRs), (2) beam profiles, and (3) continuous output factors using a 1D-scanning tank. TMRs were acquired by orienting an ionization chamber (IC) at isocenter (depth=25cm) and synchronizing a depth scan towards the water surface while lowering the couch at 1mm/s. For beam profiles, the couch was driven laterally and longitudinally while logging IC electrometer readings. Output factors (OFs) where collected by continually varying field sizes (4×4 to 30×30-cm{sup 2}) at a constant speed of 6.66 mm/s. To validate measurements, comparisons were made to data collected using traditional methods (e.g. 1D or 3D tank). Results: All data collecting using the proposed methods agreed with traditionally collected data (TMRs within 1%, OFs within 0.5% and beam profile agreement within 1% / 1mm) while taking less time to collect (factor of approximately 1/10) and with a finer sample resolution. Conclusion: TrueBeam developer mode facilitates collection of continuous data with the same accuracy as traditionally collected data with a finer resolution in less time. Results demonstrate an order of magnitude increase in sampled resolution and an order of magnitude reduction in collection time compared to traditional acquisition methods (e.g. 3D scanning tank). We are currently extending this approach to perform other TG-142 tasks.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2019

A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment

This paper describes a strategy for a general search used by the ATLAS Collaboration to find potential indications of new physics. Events are classified according to their final state into many event classes. For each event class an automated search algorithm tests whether the data are compatible with the Monte Carlo simulated expectation in several distributions sensitive to the effects of new physics. The significance of a deviation is quantified using pseudo-experiments. A data selection with a significant deviation defines a signal region for a dedicated follow-up analysis with an improved background expectation. The analysis of the data-derived signal regions on a new dataset allows a statistical interpretation without the large look-elsewhere effect. The sensitivity of the approach is discussed using Standard Model processes and benchmark signals of new physics. As an example, results are shown for 3.2 fb –1 of proton–proton collision data at a centre-of-mass energy of 13 TeV collected with the ATLAS detector at the LHC in 2015, in which more than 700 event classes and more than 10 5 regions have been analysed. Furthermore, no significant deviations are found and consequently no data-derived signal regions for a follow-up analysis have been defined.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Measurement of the photon identification efficiencies with the ATLAS detector using LHC Run 2 data collected in 2015 and 2016

The efficiency of the photon identification criteria in the ATLAS detector is measured using 36.1 fb 1 to 36.7 fb 1 of pp collision data at √s = 13 TeV collected in 2015 and 2016. The efficiencies are measured separately for converted and unconverted isolated photons, in four different pseudorapidity regions, for transverse momenta between 10 GeV and 1.5 TeV. The results from the combination of three data-driven techniques are compared with the predictions from simulation after correcting the variables describing the shape of electromagnetic showers in simulation for the average differences observed relative to data. Data-to-simulation efficiency ratios are determined to account for the small residual efficiency differences. These factors are measured with uncertainties between 0.5% and 5% depending on the photon transverse momentum and pseudorapidity. The impact of the isolation criteria on the photon identification efficiency, and that of additional soft pp interactions, are also discussed. The probability of reconstructing an electron as a photon candidate is measured in data, and compared with the predictions from simulation. In conclusion, the efficiency of the reconstruction of photon conversions is measured using a sample of photon candidates from Z → μμγ events, exploiting the properties of the ratio of the energies deposited in the first and second longitudinal layers of the ATLAS electromagnetic calorimeter.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Open Energy Data Initiative (OpenEDI) - Open Data Access Tools (OEDI-ODAT) [SWR 20-57]

Open Energy Data Initiative (OpenEDI): Advancing Analytics and Research Innovation through Improved Data Access. The Open Energy Data Initiative (OEDI) aims to improve and automate access of high-value energy data sets across the U.S. Department of Energy’s (DOE’s) programs, offices, and national laboratories. Sponsored by the DOE, this platform is being implemented by the National Renewable Energy Laboratory (NREL) to make data actionable and discoverable by researchers and industry to accelerate analysis and advance innovation. Partners at other national laboratories will help utilize the platform for analysis, can be directly involved in determining requirements, and will be resources for providing new datasets that can be shared with the public to expand innovation.

Rager, David↗
DOE OSTI2019

ASCENDS: Advanced Data SCiENce Toolkit for Non-Data Scientists

Data science capabilities are becoming important more and more. Even though there exist many data science and machine learning tools, they are not intuitive for many traditional scientists who are not familiar with programming. ASCENDS provides easy and intuitive interfaces (command-line interface, graphical user interface) to users so they can use data science capabilities without having to understand programming language. ASCENDS performs data visualization, machine learning model training, storing and prediction (regression problem), automatic hyperparameter tuning, and supports various machine learning models including random forest, kernel ridge, nearest neighbor, neural network, etc. Users can use standard CSV (comma separated values) format data set and investigate the feature columns that are highly correlated to a target column; further, they can learn ML models, then store and use them for predictions. The tool is supposed to be generically applicable so that it can be used for many different applications.

Sangkeun, MattLee↗
DOE OSTI2021

Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography

Parallel-beam tomography systems at synchrotron facilities have limited field of view (FOV) determined by the available beam size and detector system coverage. Scanning the full size of samples bigger than the FOV requires various data acquisition schemes such as grid scan, 360-degree scan with offset center-of-rotation (COR), helical scan, or combinations of these schemes. Though straightforward to implement, these scanning techniques have not often been used due to the lack of software and methods to process such types of data in an easy and automated fashion. The ease of use and automation is critical at synchrotron facilities where using visual inspection in data processing steps such as image stitching, COR determination, or helical data conversion is impractical due to the large size of datasets. Here, we provide methods and their implementations in a Python package, named Algotom, for not only processing such data types but also with the highest quality possible. The efficiency and ease of use of these tools can help to extend applications of parallel-beam tomography systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Eureka: Enabling Fine-Grained Access and Range Queries on Compressed Scientific Data via Data-Index Co-Compression

Handling large-scale scientific data in high-performance computing (HPC) environments poses significant challenges, including excessive I/O, high storage costs, and slow query performance. Traditional approaches often require full data decompression and scans, making them impractical for real-time or interactive analysis. To address these limitations, we introduce Eureka, a unified data-index co-compression framework that enables fine-grained access and efficient range queries on compressed scientific datasets. Eureka integrates spatial domain decomposition with block-wise error-bounded lossy compression to support selective decompression. It constructs a hierarchical AVL-tree index during compression to capture block-level value ranges, enabling fast pruning during query execution. To reduce metadata overhead, the index itself is also compressed while ensuring recall-preserving results. Experiments on six diverse HPC simulation datasets show that Eureka achieves up to 25x data compression and over 300x index compression, surpassing state-of-the-art compressors such as SZ3 and ZFP in rate-distortion performance. Additionally, Eureka delivers over 30x speedup for low-selectivity range queries, making it a scalable and efficient solution for modern scientific data analysis.

Yan, Ning↗
DOE OSTI2013

Review of Data from Shock Physics Experiments SPE-1, SPE-2 and SPE-3: Data Corrections and Phenomenology Analysis

The NNSA is conducting a series of high explosive (HE) tests – the Source Physics Experiments – at the National Nuclear Security Site (NNSS). These experiments are intended to investigate the generation of geophysical signals from controlled sources. These signals will aid in the development of physics-based models to aid in the discrimination of signals emitted from these and other possible sources, such as earthquakes and clandestine nuclear events. The SPE events include an array of accelerometers near to the source intended to provide measurements of the strong ground motion, or “near field,” regime. LANL performed an extensive review of the near field measurements from the first two events (SPE-1 and SPE-2) in this series. This review revealed that the near field gauges likely rotated during installation, causing measurement uncertainty. 2 This conclusion led to the installation of new gauges for the third experiment, SPE-3, utilizing new physical design and improved installation practices to achieve better quality control on their orientation. A subsequent analysis of SPE-3 data confirmed that rotations occurred and provided the basis for corrections to those data where the amount of rotation could be credibly determined. The corrected data supports a more confident analysis of the entire near field data set. This report summarizes the data errors and provides results of the analysis of the corrected dataset.

42 ENGINEERING↗
DOE OSTI2014

Uncertainty Estimates for SIRS, SKYRAD, & GNDRAD Data and Reprocessing the Pyrgeometer Data (Presentation)

The National Renewable Energy Laboratory (NREL) and the Atmospheric Radiation Measurement (ARM) Climate Research Facility work together in providing data from strategically located in situ measurement observatories around the world. Both work together in improving and developing new technologies that assist in acquiring high quality radiometric data. In this presentation we summarize the uncertainty estimates of the ARM data collected at the ARM Solar Infrared Radiation Station (SIRS), Sky Radiometers on Stand for Downwelling Radiation (SKYRAD), and Ground Radiometers on Stand for Upwelling Radiation (GNDRAD), which ultimately improve the existing radiometric data. Three studies are also included to show the difference between calibrating pyrgeometers (e.g., Eppley PIR) using the manufacturer blackbody versus the interim World Infrared Standard Group (WISG), a pyrgeometer aging study, and the sampling rate effect of correcting historical data.

14 SOLAR ENERGY↗
DOE OSTI2018

Integrated Multi Scale Data Analytics and Machine Learning for the Grid; Benchmarking Algorithms and Data Quality Analysis

Machine Learning and Data Analytics are terms which are utilized now ubiquitously throughout grid analytics forums, as a potential solution to the growing big data challenges for the distribution grid. As sensors become inexpensive and common, and communication networks grow, the analytics themselves must adapt and enable consumers and providers to visualize and utilize the data provided effectively. Machine learning is one of the techniques proposed in order to manage this data deluge. This report is a subset of the work in this activity, funded through the DOE Grid Modernization Initiative, to summarize the benchmarking and review of data quality impact on the algorithm sets being developed within each use case area.

42 ENGINEERING↗
DOE OSTI2022

Data Report: TurboWave I and II Data Release

The TurboWave I and II infrasound campaigns were conducted to examine short term variability in acoustic propagation at local and regional distances. The tests were conducted in nearly co-located regions at the Energetic Materials Research and Testing Center in Socorro, NM between 2019 and 2020 and recorded across a variety of acoustic microbarometer sensors. This report details the waveform data recorded from the experiment and coincides with data archival at the Incorporated Research Institutions for Seismology. The report includes a description of the experiment along with the types of data and instruments. The data release includes raw waveform data as well as metadata information.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Filling in Subsurface Storage Open Data Gaps - Updates to CCS Data Availability on EDX and EDX Spatial (FWP-1022465)

There is a need to preserve and efficiently access data resources to drive the next generation of research and development while ensuring compliance with DOE regulations. Over the last 10+ years, there has been ongoing efforts by the DOE Carbon Storage Program to ensure that there is effective data curation and preservation of DOE funded research leveraging the NETL-FECM data repository, the Energy Data eXchange (EDX). This talk presents updates about ongoing efforts to continue to support the mission of ensuring that carbon storage data is findable, accessible, interoperable, and reusable to the carbon storage stakeholder community through EDX and EDX Spatial. Presented at the NETL Carbon Management Review Meeting, Pittsburgh, 2024.

Morkner, Paige↗
DOE OSTI2025

Resolving the Discontinuity Between Thermal Scattering Data and Fast Data

The task of resolving discontinuities between thermal cross sections and fast cross sections in AMPX processing is part of a broader project to provide guidance on the potential effect of unknown thermal neutron scattering law (TNSL) data, primarily from the standpoint of criticality safety. The primary goal of this work is to deliver code fixes in AMPX that increase confidence that SCALE libraries provide physically correct cross sections across the full range of neutron energies encountered in transport applications. This report discusses the resolution of two sources of discontinuity. The first issue occurs when the Bragg edges in an evaluated nuclear data file do not extend to 5 eV. The second issue concerns parameters chosen during the processing of the nuclear data library—specifically, the parameter that defines the energy at which the thermal data ends and the fast nuclide data begins. This report also discusses a third issue, which is related to the calculation of probability distributions from a densely gridded double-differential cross section.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Data-driven, Interpretable Photometric Redshifts Trained on Heterogeneous and Unrepresentative Data

We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data-driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift. This conceptually novel approach combines the advantages of both machine learning methods and template fitting methods by building template SEDs directly from the spectroscopic training data. This is made computationally tractable with Gaussian processes operating in flux–redshift space, encoding the physics of redshifts and the projection of galaxy SEDs onto photometric bandpasses. This method alleviates the need to acquire representative training data or to construct detailed galaxy SED models; it requires only that the photometric bandpasses and calibrations be known or have parameterized unknowns. The training data can consist of a combination of spectroscopic and deep many-band photometric data with reliable redshifts, which do not need to entirely spatially overlap with the target survey of interest or even involve the same photometric bands. We showcase the method on the i -magnitude-selected, spectroscopically confirmed galaxies in the COSMOS field. The model is trained on the deepest bands (from SUBARU and HST ) and photometric redshifts are derived using the shallower SDSS optical bands only. We demonstrate that we obtain accurate redshift point estimates and probability distributions despite the training and target sets having very different redshift distributions, noise properties, and even photometric bands. Our model can also be used to predict missing photometric fluxes or to simulate populations of galaxies with realistic fluxes and redshifts, for example.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

47 Tuc in Rubin Data Preview 1. Exploring Early LSST Data and Science Potential

We present analyses of the early data from Rubin Observatory’s Data Preview 1 (DP1) for the field of the globular cluster 47 Tuc. The DP1 data set for 47 Tuc includes four nights of observations from the Rubin Commissioning Camera (LSSTComCam), covering multiple bands (ugriy). We address challenges of crowding in the inner region of the cluster and toward the SMC in DP1, and demonstrate improved star–galaxy separation by fitting fifth-degree polynomials to the stellar loci in color–color diagrams and applying multidimensional sigma clipping. We compile a catalog of 3576 probable 47 Tuc member stars selected via a combination of isochrone, Gaia proper-motion, and color–color space matched filtering. We explore the sources of photometric scatter in the 47 Tuc color–color sequence, evaluating contributions from various potential sources, including differential extinction within the cluster. Finally, of the 72 well-characterized variables in the field, we recover three known variable stars, including two RR Lyrae and one eclipsing binary, in the coadd-based object catalog, and identify 62 in the difference image-based object catalog. Although the DP1 lightcurves have sparse temporal sampling, they appear to follow the patterns of densely sampled literature lightcurves well. Despite some data limitations for crowded-field stellar analysis, DP1 demonstrates the promising scientific potential for future LSST data releases.

Choi, Yumi [NSF National Optical-Infrared Astronom↗
DOE OSTI2025

Nuclear Data Adjustment for Nonlinear Applications in the OECD/NEA WPNCS SG14 Benchmark -- A Bayesian Inverse UQ-based Approach for Data Assimilation

The Organization for Economic Cooperation and Development (OECD) Working Party on Nuclear Criticality Safety (WPNCS) proposed a benchmark exercise to assess the performance of current nuclear data adjustment techniques applied to nonlinear applications and experiments with low correlation to applications. This work introduces Bayesian Inverse Uncertainty Quantification (IUQ) as a method for nuclear data adjustments in this benchmark, and compares IUQ to the more traditional methods of Generalized Linear Least Squares (GLLS) and Monte Carlo Bayes (MOCABA). Posterior predictions from IUQ showed agreement with GLLS and MOCABA for linear applications. When comparing GLLS, MOCABA, and IUQ posterior predictions to computed model responses using adjusted parameters, we observe that GLLS predictions fail to replicate computed response distributions for nonlinear applications, while MOCABA shows near agreement, and IUQ uses computed model responses directly. We also discuss observations on why experiments with low correlation to applications can be informative to nuclear data adjustments and identify some properties useful in selecting experiments for inclusion in nuclear data adjustment. Performance in this benchmark indicates potential for Bayesian IUQ in nuclear data adjustments.

FOS: Computer and information sciences↗