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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Making Common Fund data more findable: catalyzing a data ecosystem

The Common Fund Data Ecosystem (CFDE) has created a flexible system of data federation that enables researchers to discover datasets from across the US National Institutes of Health Common Fund without requiring that data owners move, reformat, or rehost those data. This system is centered on a catalog that integrates detailed descriptions of biomedical datasets from individual Common Fund Programs’ Data Coordination Centers (DCCs) into a uniform metadata model that can then be indexed and searched from a centralized portal. This Crosscut Metadata Model (C2M2) supports the wide variety of data types and metadata terms used by individual DCCs and can readily describe nearly all forms of biomedical research data. We detail its use to ingest and index data from 11 DCCs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Measurements of the center-of-mass energies of $e^+e^-$ collisions at BESIII*

During the 2016-17 and 2018-19 running periods, the BESIII experiment collected 7.5 fb -1 of $e^+e^-$ collision data at center-of-mass energies ranging from 4.13 to 4.44 GeV. These data samples are primarily used for the study of excited charmonium and charmoniumlike states. By analyzing the di-muon process $e^+e^-$ → $e^+e^-$ →, we measure the center-of-mass energies of the data samples with a precision of 0.6 MeV. Through a run-by-run study, we find that the center-of-mass energies were stable throughout most of the data-collection period.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Biofuels Information Center

The purpose of the Biofuels Information Center (BIC) task is to provide relevant data, information, reports, and web-based tools to all bioenergy stakeholders. The BIC task began in FY08 to meet the requirement under Title II, Sec. 229 of the Energy Independence and Security Act of 2007 (EISA) requires DOE to develop a "Biofuels and Biorefinery Information Center". The BIC task supports biofuels pages content on the EERE's most visited website - the Alternative Fuels Data Center (AFDC http://www.afdc.energy.gov) and the Bioenergy Atlas tools (currently archived) (previous address https://maps.nrel.gov). This task results in more than 1.7 million web pageviews (an instance of an internet user visiting a webpage) per year. In FY22, the task completed the final year of the 5 year USDA Biofuels Infrastructure Partnership (BIP). The USDA BIP expanded infrastructure for E15 and/or E85 to approximately 850 stations and NREL received and reviewed data for quality analyzed all infrastructure and sales data collected by USDA. Stations are privately held and previously it was difficult to ascertain infrastructure and sales data. This unique dataset allows insight into infrastructure data (number of pumps and tanks, costs to install new equipment) and sales data (price and volume for E10, E15, E85, and diesel by month). The 2021 USDA BIP National Summary Report is with DOE for review prior to publication. Future work will include the biannual Bioenergy Industry Status Report (4 previous versions have been published) . The task also supports the principal investigator's time to engage stakeholders on infrastructure and deployment of biofuels. This includes leading, membership, and participation in the following roles: member Board of Advisors at the Fuels Institute, voting member for multiple UL standards committees, Co-Chair of the Infrastructure team at Agriculture/Auto/Ethanol, Member of Coordination Research Council's ULSD Corrosion Committee. The Principal investigator routinely responds to industry inquires to assist in deployment of biofuels regularly.

biofuels↗
DOE OSTI2026

Search for HH → bbτ⁺τ⁻ Using Run 3 Scouting Data Analyze b-tagging and tau-tagging Performance with Unified Particle Transformer

B-tagging and tau-tagging performances play an important role in the search for the rare event HH → bbτ⁺τ⁻. A transformer-based neural network, Unified Particle Transformer, is applied for both tagging tasks, and Run 3 proton–proton collision scouting data at center-of-mass energy of 13.6 TeV is used. The scouting data stream accepts events at a much higher rate compared to traditional triggers, but stores only the objects reconstructed in the trigger, no low-level detector information. Therefore, existing taggers trained for the offline event reconstruction cannot be used. Analysis of the SoftMax plots, ROC/AUC curves, confusion matrix, accuracy and losses are used to evaluate model performance. Specifically, the tagging efficiency of the signal and misidentification probability across multiple background processes are compared for varying working points. Different training samples with distinct distributions of jet flavors are utilized and related model performances are analyzed. Interpretability methods, such as Integrated Gradients, may further be applied to study the input features’ influence on the model’s decisions, providing insights into potential improvements.

Chen, Blair [Purdue U., West Lafayette; Fermilab]↗
DOE OSTI2021

Observation of e + e - → ηψ(2S) at center-of-mass energies from 4.236 to 4.600 GeV

Using a total of 5 25 fb -1 of e + e - collision data with center-of-mass energies from 4.236 to 4.600 GeV, we report the first observation of the processe + e - → ηψ(2S) with a statistical significance of 4.9 standard deviations. The data sets were collected by the BESIII detector operating at the BEPCII storage ring. We measure the yield of events integrated over center-of-mass energies and also present the energy dependence of the measured cross section.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

CROCUS Tipping Bucket Rain Gauge Data from Argonne Deployable Mast Deployed at NEIU Carruthers Center for Inner City Studies (CCICS)

The Tipping Bucket Rain Gauge (TBRG) dataset contains data from a non-heated Met One 12-inch tipping bucket rain gauge that was mounted on the Argonne Deployable Mast (ADM). The ADM is a rapid deployable meteorological trailer that can be outfitted with instrumentation to measure urban heat island effects, urban flooding or urban flux measurements. During the urban flooding field campaign, the ADM was outfitted with multiple precipitation measurement systems, including the TBRG. This dataset contains one minute measurements for precipitation accumulation during the ADM's deployment at the Northeastern Illinois University (NEIU) Carruthers Center for Inner City Studies (CCICS) campus. These data are helpful for identifying periods of precipitation, leading to potential flooding. TBRGs can be used to validate optical rain gauge data and disdrometer data collected during the CROCUS urban flooding campaign. Data were collected at the CCICS building parking lot, located in the south side of Chicago, IL. The data is presented as daily NetCDF (.nc) files, each containing approximately 24 hours of observations. Files follow the naming convention of: the project (CROCUS), location (ADM-ccics), instrument name (tbrg), data level (raw, a1), and date (year, month, day). The NetCDF format can be accessed using common scientific software such as Python using xarray, netCDF4 or act-doe.

1-minute Precipitation Accumulation↗
DOE OSTI2024

Population-level incidence of human papillomavirus–positive oropharyngeal, cervical, and anal cancers, by smoking status

Abstract We estimated the population-level incidence of human papillomavirus (HPV)–positive oropharyngeal, cervical, and anal cancers by smoking status. We combined HPV DNA genotyping data from the Centers for Disease Control and Prevention’s Cancer Registry Sentinel Surveillance System with data from the Kentucky Cancer Registry and Behavioral Risk Factor Surveillance System across smoking status. During 2004-2005 and 2014-2015 in Kentucky, most cases of oropharyngeal (63.3%), anal (59.7%), and cervical (54.9%) cancer were among individuals who ever smoked. The population-level incidence rate was higher among individuals who ever smoked than among those who never smoked for HPV-positive oropharyngeal (7.8 vs 2.1; adjusted incidence rate ratio = 2.6), cervical (13.7 vs 6.8; adjusted incidence rate ratio = 2.0), and anal (3.9 vs 1.6; adjusted incidence rate ratio = 2.5) cancers. These findings indicate that smoking is associated with increased risk of HPV-positive oropharyngeal, cervical, and anal cancers, and the population-level burden of these cancers is higher among individuals who ever smoked.

Oncology↗
DOE OSTI2022

Pacific Marine Energy Center Laser Doppler Velocimetry Commissioning

First commissioning data for the new laser doppler velocimetry (LDV) system that will be used at the Tyler Flume at the University of Washington. The LDV system can measure three components of velocity at a point. For this dataset the three components were operated in non-coincident mode and data were acquired at the center of the empty facility. Comparisons of freestream turbulence were made with a Vectrino slightly upstream of the LDV measurement location.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

AMReX v2024

The software framework, AMReX, supports the development of block-structured adaptive mesh refinement (AMR) algorithms for solving systems of partial differential equations. AMR reduces the computational cost and memory footprint compared to a uniform mesh while preserving the essential local descriptions of different physical processes in complex multiphysics algorithms. AMR uses a hierarchical representation of the solution at multiple levels of resolution where the solution on each level is defined on the union of data containers at that resolution. These data containers, which represent the solution over a logically rectangular subregion of the domain, can contain field data defined on a mesh, Lagrangian particles or combinations of both. In addition to these basic data types, AMReX supports a multilevel embedded boundary representation of complex geometry; linear solvers for cell-centered and nodal data; asynchronous I/O in a native format readable by ParaView, VisIt and yt; and interfaces to hypre and PETSc solvers. AMReX enables applications to run on distributed memory architectures with multicore CPUs and with GPU accelerators. AMReX uses a lightweight abstraction layer that effectively hides the details of the architecture from the application. The framework currently supports CUDA, HIP and SYCL for GPU acceleration and OpenMP for multi-core CPU architectures.

Almgren, Ann↗
DOE OSTI2022

Luminosities and energies of e + e - collision data taken between $\sqrt{s}$=4.61 GeV and 4.95 GeV at BESIII*

From December 2019 to June 2021, the BESIII experiment collected approximately 5.85 fb -1 of data at center-of-mass energies between 4.61 and 4.95 GeV. This is the highest collision energy BEPCII has reached to date. The accumulated e + e - annihilation data samples are useful for studying charmonium(-like) states and charmed-hadron decays. By adopting a novel method of analyzing the production of $Λ^{+}_{c}$ $\overline{Λ}$ $^{-}_{c}$ pairs in e + e - annihilation, the center-of-mass energies are measured with a precision of ~0.6 MeV. Integrated luminosities are measured with a precision of better than 1% by analyzing the events of large-angle Bhabha scattering. These measurements provide important inputs to analyses based on these data samples.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

SLOPE daily and 250 m gross primary productivity (GPP) for the CONUS, 2000-2019, Carbon Monitoring System (CMS)

The SatelLite Only Photosynthesis Estimation (SLOPE) GPP product is a daily, 250 m GPP dataset covering the Contiguous United States (CONUS) from 2000 to present with 1 day latency. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time, and observation-based GPP products. This product has been developed to address this critical gap, leveraging a number of MODIS land and atmosphere products. There are three distinct features of the SLOPE GPP production algorithm. (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRv (SANIRv) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Crop Data Layer (CDL) product to predict dynamic C4 crop fraction. PAR, SANIRv and C4 fraction are used to drive a parsimonious model with only two slope parameters to estimate GPP along with a quantitative uncertainty on a per-pixel and daily basis. The slope GPP product has a R2 = 0.84 and a root-mean-square error (RMSE) of 1.65 gC m-2 d-1, evaluated against from 50 AmeriFlux eddy covariance sites (332 site-years). Archived data from 2000 to 2019 are publicly available at the NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). Data from 2020 are available from the authors upon request. All data are projected in the standard MODIS Land Integerized Sinusoidal tile map projection. Each processing tile is in size of 4800 pixels by 4800 pixels, representing approximately 1200 km by 1200 km land region. In addition to the GPP product, SLOPE PAR, SANIRV, and C4 fraction, along with their uncertainties, are also released.

Jiang, Chongya↗
DOE OSTI2020

INL High Performance Computing Overview

INL High Performance Computing Overview presentation by Eric Whiting for the National Science Users Facility 2020 Annual Program Review on November 10, 2020; data covering our Collaborative Computing Center (C3), the supercomputers and their data.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

A measurement of the Higgs boson mass in the diphoton decay channel in proton-proton collisions at $\sqrt{s}$ = 13 TeV

A measurement of the Higgs boson mass in the diphoton decay channel is performed using proton-proton collision data at a center-of-mass energy of 13 TeV. The data set recorded with the CMS detector between 2016 and 2018 is used, corresponding to an integrated luminosity of 138 fb$^{-1}$. A refined detector calibration and new analysis techniques are employed to improve the precision of the results compared to earlier measurements. The Higgs boson mass is measured to be $m_\mathrm{H}$ = 125.13 $\pm$ 0.15 GeV = 125.13 $\pm$ 0.10 (stat) $\pm$ 0.12 (syst) GeV. In addition, a combination with the mass measurement at center-of-mass energies of 7 and 8 TeV in the diphoton final state is performed resulting in $m_\mathrm{H}$ = 125.06 $\pm$ 0.14 GeV = 125.06 $\pm$ 0.09 (stat) $\pm$ 0.11 (syst) GeV.

FOS: Physical sciences↗
DOE OSTI2024

Operational Analytics Studies for ATLAS Distributed Computing: Data Popularity Forecast and Utilization of the WLCG Centers

Operational analytics is the direction of research related to the analysis of the current state of computing processes and the prediction of future states in order to anticipate imbalances and take timely measures to stabilize a complex system. There are two relevant areas in ATLAS Distributed Computing that are currently the focus of studies: user physics analysis including the forecast of popularity of data samples among users, and evaluating WLCG centers for their readiness to process user analysis payloads. Studying these areas is challenging due to the complexity involved, as it requires a comprehensive understanding of numerous boundary conditions typically found in large-scale distributed computing infrastructures. Forecasts of data popularity are problematic without the categorization of user tasks by their types (data transformation or physics analysis), which do not always appear on the surface but may induce noise, which introduces significant distortions for predictive analysis. Evaluating the WLCG resources by their analysis workloads is also a challenging task as it is necessary to find a balance between the workload of the resource, its performance, the waiting time for jobs on it, as well as the volume of jobs that it processes. This is especially difficult in a heterogeneous computing environment, where legacy resources are used along with modern high-performance machines. We will look at these areas of research in detail and discuss what tools and methods are used in our work, demonstrating results already obtained.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

ExaFEL: extreme-scale real-time data processing for X-ray free electron laser science

ExaFEL is an HPC-capable X-ray Free Electron Laser (XFEL) data analysis software suite for both Serial Femtosecond Crystallography (SFX) and Single Particle Imaging (SPI) developed in collaboration with the Linac Coherent Lightsource (LCLS), Lawrence Berkeley National Laboratory (LBNL) and Los Alamos National Laboratory. ExaFEL supports real-time data analysis via a cross-facility workflow spanning LCLS and HPC centers such as NERSC and OLCF. Our work therefore constitutes initial path-finding for the US Department of Energy's (DOE) Integrated Research Infrastructure (IRI) program. We present the ExaFEL team's 7 years of experience in developing real-time XFEL data analysis software for the DOE's exascale supercomputers. We present our experiences and lessons learned with the Perlmutter and Frontier supercomputers. Furthermore we outline essential data center services (and the implications for institutional policy) required for real-time data analysis. Finally we summarize our software and performance engineering approaches and our experiences with NERSC's Perlmutter and OLCF's Frontier systems. This work is intended to be a practical blueprint for similar efforts in integrating exascale compute resources into other cross-facility workflows.

59 BASIC BIOLOGICAL SCIENCES↗