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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 OSTI2017

PANSOPHY, a JLAB SRF engineering data management system, supporting data collection, retrieval and analysis utilized by LCLS-II

Pansophy is an Engineering Data Management System that provides a comprehensive solution for managing information in the production and testing of cryomodules. It is especially suited to supporting the Data & Quality Management Systems for large projects like LCLS-II. With extensive amounts of data collected for an individual project, data retrieval to facilitate feedback and enhancement of production and processing activities is a high priority. The priority shares importance with the needs of managing the project, including production status, NCR, and Quality Management reports. Recent Pansophy enhancements have been to Data and Quality management reports and statistical analysis. Such enhancements include a database driven menu system, extended MSWord macro and preprocessing of travelers, and an extensive reporting system. The reporting system allows managers and group leaders to quickly respond to the needs of the project in areas of cavity and cryomodule production, data collection, NCR, Quality Management and schedule. Extensions include integration with the SRF inventory system PRIMeS, allowing traceability from receiving of manufactured parts to final cryomodule product.

Bookwalter, Valerie D.↗
DOE OSTI2022

Efficient Data Query for Gaussian Process Compressed Data through Value Range Estimation [Slides]

When the resolution of the data increases, data reduction methods are applied to simulation output, including Gaussian process, neural representation and compression algorithms. Lots of data analysis/visualization techniques requires data query, but data query from reduced representation is still challenging. This report will provide examples and provide possible answers to why data query from reduced representation is still challenging.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Automated analysis of unlabeled PV data with Solar Data Tools software: Overview and feature updates

Distributed rooftop PV systems: ubiquitous, yet commonly have unlabeled data Difficult or impossible to form a performance index We developed Solar Data Tools (SDT), an open-source Python library for analyzing PV power (and irradiance) time-series data SDT enables analysis of unlabeled PV data—no model, no meteorological data, no performance index required Takes a statistical signal processing approach Data processing steps are largely pre-defined and automatic regardless of system type—from utility tracking systems to multi-pitch rooftop systems

Meyers-Im, Bennet E↗
DOE OSTI2024

FY 2024 Multidimensional Data Correlation Platform Data Management Infrastructure Progress: Materials Laboratory

This report provides an inventory of the equipment available at the ORNL Manufacturing Demonstration Facility (MDF) for sample preparation and material characterization, including both destructive and non-destructive techniques that generate critical data to support the development of the Multi-Dimensional Data Correlation (MDDC) framework. The success of the MDDC framework depends heavily on the quality and completeness of the data it can access. Therefore, it is essential to establish a comprehensive inventory of the technologies available to the Advanced Materials and Manufacturing Technologies (AMMT) multi-laboratory team. This starts by gathering information about the types of data they produce, the data collection and transfer protocols used, file formats, and data storage requirements for experiments. This information is then carefully evaluated to create the operations and trackables elements of the Damara Tern platform, which is the foundation of the MDDC framework.

36 MATERIALS SCIENCE↗
DOE OSTI2025

FY 2025 Multidimensional Data Correlation Platform: Unified Software Architecture for Advanced Materials and Manufacturing Technologies Data Management and Processing

The Advanced Materials and Manufacturing Technologies (AMMT) program continues to advance a data-driven approach to demonstrate the utility of additive manufacturing for fabricating components for nuclear applications. A key scientific goal is to leverage data to better understand manufacturing outcomes and thereby improve the performance, reliability, and lifespan of nuclear components. Ultimately, this effort supports the development of standards for certification and qualification of additively manufactured components, enabling broader industry adoption. In support of this objective, the AMMT program is building and deploying a data management platform to record, index, analyze, and make available the manufacturing data generated across the AMMT program. In FY 2023, the team conceptualized the architecture of the platform and, in FY 2024, deployed the first functional version at the Oak Ridge National Laboratory (ORNL) Manufacturing Demonstration Facility (MDF). In FY 2025, the platform was officially opened to all AMMT members. To enable this expansion, core modifications and enhancements were developed, including improvements to the user interface and workflows for data entry and retrieval. Most notably, robust security and access control mechanisms were implemented to protect data and manage information sharing. This effort featured a logging system, protected views, and controlled access mechanisms. This report documents these enhancements and the transition of the platform into program-wide use.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Data Quality Assessment of Optiwatt Vehicle Telematics Data

In October 2024, the Idaho National Laboratory (INL) received data from Optiwatt (Compass Global, Inc.) describing the driving and charging behavior of electric vehicle (EV) drivers. The data shared had been collected from approximately 10,000 vehicles and included vehicle specifications, driving information like odometer readings at the beginning and end of origin-destination pairs (i.e., trips with identification of home for trip start and end for Tesla vehicles), and charging information such as charging energy consumed per charge session and if the charge occurred at home. The vehicle data were provided from 9 EV makes and 18 EV models, with production years ranging from 2012–2024, but more than 9,500 of the vehicles were Tesla EVs. The data includes more than six million trips and more than three million charging events that occurred between June 2023 to Aug 2024 and collected from California and the Eastern United States. The purpose of this report is to review the quality of the data received from Optiwatt and the feedback INL received from Optiwatt after data concerns were shared with them.

33 - ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

California - Quality-Controlled Reanalysis and Observational Data for Buoy (130), Morro Bay / Derived Data

This collection provides spatiotemporally paired reanalysis and satellite data to supplement the lidar buoy observations during the California deployments. Point time series of observed and reanalysis data are provided, using inverse distance weighting to geolocate the reanalysis data to the buoy location. Gridded time series of reanalysis data are provided, covering the California coast and extending into the Pacific Ocean. Satellite data in the coastal California region are also provided. The reanalysis collection includes the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), the Climate Forecast System version 2 (CFSv2), the North American Regional Reanalysis (NARR), the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and the analysis system of the Rapid Refresh (RAP). The satellite data collection is that of Ribal and Young (2019). All datasets are publicly available with the sources provided in the References section.

17 WIND ENERGY↗
DOE OSTI2023

California - Quality-controlled Reanalysis and Observational Data for Buoy (120), Humboldt / Derived Data

This collection provides spatiotemporally paired reanalysis and satellite data to supplement the lidar buoy observations during the California deployments. Point time series of observed and reanalysis data are provided, using inverse distance weighting to geolocate the reanalysis data to the buoy location. Gridded time series of reanalysis data are provided, covering the California coast and extending into the Pacific Ocean. Satellite data in the coastal California region are also provided. The reanalysis collection includes the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), the Climate Forecast System version 2 (CFSv2), the North American Regional Reanalysis (NARR), the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and the analysis system of the Rapid Refresh (RAP). The satellite data collection is that of Ribal and Young (2019). All datasets are publicly available with the sources provided in the References section.

17 WIND ENERGY↗
DOE OSTI2021

Fine-Root Ecology Database (FRED): A Global Collection of Root Trait Data with Coincident Site, Vegetation, Edaphic, and Climatic Data, Version 3.

To address the need for a centralized root trait database, we compiled the Fine-Root Ecology Database (FRED) from published and unpublished data sources. We have continued to add to the FRED database since the release of FRED 2.0 in 2018, and a new version of FRED is now available. FRED 3.0 has more than 150,000 observations of more than 330 root traits, with data collected from more than 1400 data sources. FRED 3.0 has 45% more root trait observations than FRED 2.0, particularly in the categories of root anatomy, morphology, and microbial associations; ancillary data on associated site, vegetation, edaphic, and climatic conditions from across the globe have also increased concurrently. FRED is focused on fine roots (traditionally defined as roots less than 2 mm in diameter), as coarse roots are studied using different methodology, often at very different scales, and have different traits and trait interpretations. However, FRED accepts data collected from roots of all sizes, and already contains several observations of coarse roots. Data collection will continue for the foreseeable future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Data to Accompany: Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration

Wearable silicone wristbands are a rapidly growing exposure assessment technology that offer researchers the ability to study previously inaccessible cohorts and have the potential to provide a more comprehensive picture of chemical exposure within diverse communities. However, there are no established best practices for analyzing the data within a study or across multiple studies, thereby limiting impact and access of these data for larger meta-analyses. We utilize data from three studies, from over 600 wristbands worn by participants in New York City and Eugene, Oregon, to present a first-of-its-kind manuscript detailing wristband data properties. We further discuss and provide concrete examples of key areas and considerations in common statistical modeling methods where best practices must be established to enable meta-analyses and integration of data from multiple studies. Finally, we detail important and challenging aspects of machine learning, meta-analysis, and data integration that researchers will face in order to extend beyond the limited scope of individual studies focused on specific populations.

Bramer, Lisa M↗
DOE OSTI2016

CLASH: JOINT ANALYSIS OF STRONG-LENSING, WEAK-LENSING SHEAR, AND MAGNIFICATION DATA FOR 20 GALAXY CLUSTERS

We present a comprehensive analysis of strong-lensing, weak-lensing shear and magnification data for a sample of 16 X-ray-regular and 4 high-magnification galaxy clusters at 0.19≲z≲0.69 selected from Cluster Lensing And Supernova survey with Hubble (CLASH). Our analysis combines constraints from 16-band Hubble Space Telescope observations and wide-field multi-color imaging taken primarily with Suprime-Cam on the Subaru Telescope, spanning a wide range of cluster radii (10″–16′). We reconstruct surface mass density profiles of individual clusters from a joint analysis of the full lensing constraints, and determine masses and concentrations for all of the clusters. We find the internal consistency of the ensemble mass calibration to be ≤5% ± 6% in the one-halo regime (200–2000 kpc h{sup −1}) compared to the CLASH weak-lensing-only measurements of Umetsu et al. For the X-ray-selected subsample of 16 clusters, we examine the concentration–mass (c–M) relation and its intrinsic scatter using a Bayesian regression approach. Our model yields a mean concentration of c|{sub z=0.34}=3.95±0.35 at M{sub 200c} ≃ 14 × 10{sup 14} M{sub ⊙} and an intrinsic scatter of σ(lnc{sub 200c})=0.13±0.06, which is in excellent agreement with Λ cold dark matter predictions when the CLASH selection function based on X-ray morphological regularity and the projection effects are taken into account. We also derive an ensemble-averaged surface mass density profile for the X-ray-selected subsample by stacking their individual profiles. The stacked lensing signal is detected at 33σ significance over the entire radial range ≤4000 kpc h{sup −1}, accounting for the effects of intrinsic profile variations and uncorrelated large-scale structure along the line of sight. The stacked mass profile is well described by a family of density profiles predicted for cuspy dark-matter-dominated halos in gravitational equilibrium, namely, the Navarro–Frenk–White (NFW), Einasto, and DARKexp models, whereas the single power-law, cored isothermal and Burkert density profiles are disfavored by the data. We show that cuspy halo models that include the large-scale two-halo term provide improved agreement with the data. For the NFW halo model, we measure a mean concentration of c{sub 200c}=3.79{sub −0.28}{sup +0.30} at M{sub 200c}=14.1{sub −1.0}{sup +1.0}×10{sup 14} M{sub ⊙}, demonstrating consistency between the complementary analysis methods.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Extending the Search for Muon Neutrinos Coincident with Gamma-Ray Bursts in IceCube Data

We present an all-sky search for muon neutrinos produced during the prompt γ-ray emission of 1172 gamma-ray bursts (GRBs) with the IceCube Neutrino Observatory. The detection of these neutrinos would constitute evidence for ultra-high-energy cosmic-ray (UHECR) production in GRBs, as interactions between accelerated protons and the prompt γ-ray field would yield charged pions, which decay to neutrinos. A previously reported search for muon neutrino tracks from northern hemisphere GRBs has been extended to include three additional years of IceCube data. A search for such tracks from southern hemisphere GRBs in five years of IceCube data has been introduced to enhance our sensitivity to the highest energy neutrinos. No significant correlation between neutrino events and observed GRBs is seen in the new data. Combining this result with previous muon neutrino track searches and a search for cascade signature events from all neutrino flavors, we obtain new constraints for single-zone fireball models of GRB neutrino and UHECR production.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

SoDaH: the SOils DAta Harmonization database, an open-source synthesis of soil data from research networks, version 1.0

Data collected from research networks present opportunities to test theories and develop models about factors responsible for the long-term persistence and vulnerability of soil organic matter (SOM). Synthesizing datasets collected by different research networks presents opportunities to expand the ecological gradients and scientific breadth of information available for inquiry. Synthesizing these data is challenging, especially considering the legacy of soil data that have already been collected and an expansion of new network science initiatives. To facilitate this effort, here we present the SOils DAta Harmonization database (SoDaH; https://lter.github.io/som-website, last access: 22 December 2020), a flexible database designed to harmonize diverse SOM datasets from multiple research networks. SoDaH is built on several network science efforts in the United States, but the tools built for SoDaH aim to provide an open-access resource to facilitate synthesis of soil carbon data. Moreover, SoDaH allows for individual locations to contribute results from experimental manipulations, repeated measurements from long-term studies, and local- to regional-scale gradients across ecosystems or landscapes. Finally, we also provide data visualization and analysis tools that can be used to query and analyze the aggregated database. The SoDaH v1.0 dataset is archived and available at https://doi.org/10.6073/pasta/9733f6b6d2ffd12bf126dc36a763e0b4 (Wieder et al., 2020).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5

Abstract. There is growing interest in data-driven weather prediction (DDWP), e.g., using convolutional neural networks such as U-NET that are trained on data from models or reanalysis. Here, we propose three components, inspired by physics, to integrate with commonly used DDWP models in order to improve their forecast accuracy. These components are (1) a deep spatial transformer added to the latent space of U-NET to capture rotation and scaling transformation in the latent space for spatiotemporal data, (2) a data-assimilation (DA) algorithm to ingest noisy observations and improve the initial conditions for next forecasts, and (3) a multi-time-step algorithm, which combines forecasts from DDWP models with different time steps through DA, improving the accuracy of forecasts at short intervals. To show the benefit and feasibility of each component, we use geopotential height at 500 hPa (Z500) from ERA5 reanalysis and examine the short-term forecast accuracy of specific setups of the DDWP framework. Results show that the spatial-transformer-based U-NET (U-STN) clearly outperforms the U-NET, e.g., improving the forecast skill by 45 %. Using a sigma-point ensemble Kalman (SPEnKF) algorithm for DA and U-STN as the forward model, we show that stable, accurate DA cycles are achieved even with high observation noise. This DDWP+DA framework substantially benefits from large (O(1000)) ensembles that are inexpensively generated with the data-driven forward model in each DA cycle. The multi-time-step DDWP+DA framework also shows promise; for example, it reduces the average error by factors of 2–3. These results show the benefits and feasibility of these three components, which are flexible and can be used in a variety of DDWP setups. Furthermore, while here we focus on weather forecasting, the three components can be readily adopted for other parts of the Earth system, such as ocean and land, for which there is a rapid growth of data and need for forecast and assimilation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

SITCOMTN-161: PSF assessment in the field of Abell 360 and shapeHSM shear profile using LSSTComCam data

The Rubin LSSTComCam on-sky campaign performed at the end of 2024 provided observations of the Abell 360 galaxy cluster; these data allow a preliminary study of cluster weak lensing analysis using Rubin Data Preview 1 (DP1) data. Among all the steps required for such analyses, accurate modeling of the PSF is essential. This work uses several diagnostics, mostly based on the residuals between the second moments of stars and the PSF model, to characterize the accuracy of the PSF modeling in the A360 field. We find the level of the residuals to be sufficiently low not to hinder the measurement of the tangential shear profile around A360. With a simple source selection process, we demonstrate that outputs of the LSST Science Pipelines can be used to detect the tangential shear profile in Abell 360 at the 3.6σ level, and our analysis indicates that contamination from PSF modeling systematics is negligible.

Dell'Antonio, Ian [Brown University]↗
DOE OSTI2025

SITCOMTN-165: Surface brightness profiles around massive galaxies in LSSTComCam data

Low surface brightness photometry of massive galaxies can probe the extended stellar halos and intracluster light but is often limited by background subtraction and masking systematics. Here we present radial surface brightness profiles for the brightest cluster galaxy of Abell 360 and 2dFGRS TGS323Z113 using Rubin Observatory Commissioning Camera (LSSTComCam) Data Preview 1 in the g, r, and z bands. Our results show that the LSSTComCam data can go down to ~28 mag arcsec^-2 in the r band. Comparison to DECaLS measurements processed with Legacyhalos shows good agreement of surface brightness profiles at small radii while the LSSTComCam data extend deeper at larger radii.

79 ASTRONOMY AND ASTROPHYSICS↗