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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Livewire User Guide

The Livewire Data Platform houses a catalog of transportation- and mobility-related project data, as well as a publications database, making it easy to search and share data. It allows transportation researchers, industry, and academic partners to increase the visibility of their projects within the research community, securely share and preserve data, and leverage datasets from other projects. Public data on Livewire are open to anyone with a Livewire account. This guide will help Livewire users understand how to store project data as a data steward, as well as access data as a data consumer.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Critical Infrastructure Decision-Making under Long-Term Climate Hazard Uncertainty: The Need for an Integrated, Multidisciplinary Approach

U.S. critical infrastructure assets are often designed to operate for decades, and yet long-term planning practices have historically ignored climate change. With the current pace of changing operational conditions and severe weather hazards, research is needed to improve our ability to translate complex, uncertain risk assessment data into actionable inputs to improve decision-making for infrastructure planning. Decisions made today need to explicitly account for climate change – the chronic stressors, the evolution of severe weather events, and the wide-ranging uncertainties. If done well, decision making with climate in mind will result in increased resilience and decreased impacts to our lives, economies, and national security. We present a three-tier approach to create the research products needed in this space: bringing together climate projection data, severe weather event modeling, asset-level impacts, and contextspecific decision constraints and requirements. At each step, it is crucial to capture uncertainties and to communicate those uncertainties to decision-makers. While many components of the necessary research are mature (i.e., climate projection data), there has been little effort to develop proven tools for long-term planning in this space. The combination of chronic and acute stressors, spatial and temporal uncertainties, and interdependencies among infrastructure sectors coalesce into a complex decision space. By applying known methods from decision science and data analysis, we can work to demonstrate the value of an interdisciplinary approach to climate-hazard decision making for longterm infrastructure planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

RITE Gen5 KHPS Performance - Period A

Includes Kinetic Hydropower System (KHPS) Turbine performance data from the RITE Gen5 KHPS turbine at the 5 meter size. This data was collected over Period A which was a 39 day span ending on 11/12/2021. This data was collected under the European Marine Energy Centre (EMEC) power performance assessment. Along with performance data, the data includes setup data, project metadata, and characteristic data for RITE Gen5 KHPS (5m) turbine.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

RITE Gen5 KHPS Performance - Period B

Includes Kinetic Hydropower System (KHPS) Turbine performance data from the RITE Gen5 KHPS turbine at the 5 meter size. This data was collected over 16 days in May 2021 during Period B operation. Along with performance data, the data includes setup data, project metadata, and characteristic data for RITE Gen5 KHPS (5m) turbine.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

CROCUS Tipping Bucket Rain Gauge Data at Argonne National Laboratory Prairie Site

The Tipping Bucket Rain Gauge (TBRG) dataset contains data from both the Nova-Lynx 12 inch TBRG and the Met One 8-inch TBRG. The dataset contains one minute measurements for precipitation accumulation measured in that timeframe from both instruments. Each TBRG was equipped with heaters for all-season measurements. These data are helpful for identifying periods of drought, potential flooding, and general input for water budgets. TBRGs can be used to validate optical rain gauge data and disdrometer data collected during the CROCUS project. Data were collected at the Argonne Testbed for Multiscale Observational Science (ATMOS), a 20-acre prairie site at Argonne National Laboratory in Lemont, Illinois. 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 (atmos), 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-min Precipitation Accumulation↗
DOE OSTI2022

DETAIL Component Scaling and Methodology Comparison

The purpose of this study was to develop a process to convert input signals from one facility into another by reflecting geometric and environmental settings. The Dynamic Energy Transport and Integration Laboratory (DETAIL) is a research facility in development. Its aim is to emulate the daily interactions among power production industry systems and receive real-time data from those systems as inputs. To convert signals and ensure that the temporal sequences and magnitudes reflect laboratory settings, the ability to scale and project data is essential. To demonstrate this ability, Dynamical System Scaling (DSS) and Hierarchical Two-Tiered Scaling (H2TS) (methodologies that enable systems to scale and project or extrapolate data sets to desired environments while conserving the observed behavior based on first principles) were applied to DETAIL’s thermocline thermal storage system in the Thermal Energy Distribution System (TEDS) facility and solid-oxide electrolysis cell in the High Temperature Hydrogen Electrolysis (HTHE) facility. Both thermocline and electrolysis cell systems were successfully scaled, and test cases were conducted to generate a doubly accelerated energy charge and discharge in reference to past experimental data from the facilities. The research results represented a case for the thermocline system that required signals to be accelerated without altering the stored energy. To enhance the quality of the accelerated data, error propagation analyses were conducted on DSS post-processing terms to determine the consequences of raw-data-associated errors.

08 HYDROGEN↗
DOE OSTI2022

Discriminative Dimensionality Reduction using Deep Neural Networks for Clustering of LIGO Data

In this paper, leveraging the capabilities of neural networks for modeling the non-linearities that exist in the data, we propose several models that can project data into a low dimensional, discriminative, and smooth manifold. The proposed models can transfer knowledge from the domain of known classes to a new domain where the classes are unknown. A clustering algorithm is further applied in the new domain to find potentially new classes from the pool of unlabeled data. The research problem and data for this paper originated from the Gravity Spy project which is a side project of Advanced Laser Interferometer Gravitational-wave Observatory (LIGO). The LIGO project aims at detecting cosmic gravitational waves using huge detectors. However non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of LIGO. This is undesirable as it creates problems for the gravitational wave detection process. Gravity Spy aids in glitch identification with the purpose of understanding their origin. Since new types of glitches appear over time, one of the objective of Gravity Spy is to create new glitch classes. Towards this task, we offer a methodology in this paper to accomplish this.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

A Bayesian nonparametric analysis for zero-inflated multivariate count data with application to microbiome study

High-throughput sequencing technology has enabled researchers to profile microbial communities from a variety of environments, but analysis of multivariate taxon count data remains challenging. Here, we develop a Bayesian nonparametric (BNP) regression model with zero inflation to analyse multivariate count data from microbiome studies. A BNP approach flexibly models microbial associations with covariates, such as environmental factors and clinical characteristics. The model produces estimates for probability distributions which relate microbial diversity and differential abundance to covariates, and facilitates community comparisons beyond those provided by simple statistical tests. We compare the model to simpler models and popular alternatives in simulation studies, showing, in addition to these additional community-level insights, it yields superior parameter estimates and model fit in various settings. The model's utility is demonstrated by applying it to a chronic wound microbiome data set and a Human Microbiome Project data set, where it is used to compare microbial communities present in different environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The EUCLID Experiment and Nuclear Data Library Comparisons

The EUCLID (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) project at Los Alamos National Laboratory (LANL) was a Laboratory Directed Research and Development project which aimed to reduce compensating errors in nuclear data. One major component of the project was a series of critical experiments, both measuring k eff and various other observables with the goal of using these experiments to constrain 239 Pu nuclear data uncertainties and identify compensating errors. This paper details some of the experiment design and how various nuclear data libraries simulate the EUCLID system, including sensitivities compared to Jezebel.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Calibration method for a spectral computerized tomography system

A calibration method for an x-ray computerized tomography system and a method of tomographic reconstruction are provided. The calibration method includes steps of measuring at least one point spread function (PSF) at each of a plurality of points, compressing each PSF, and in one or more storing operations, storing the compressed PSFs in a computer-accessible storage medium. The PSF measurements are made in a grid of calibration points in a field of view (FOV) of the system. In the measuring step, an absorber is positioned at each of the calibration points, and an x-ray projection is taken at least once at each of those absorber positions. In the method of tomographic image reconstruction, projection data from an x-ray tomographic projection system are input to an iterative image reconstruction algorithm. The algorithm retrieves and utilizes a priori system information (APSI) The APSI comprises comprising point spread functions (PSFs) of all voxels in a voxelization of the field of view that are compressed in the form of vectors of parameters. For utilization, each retrieved vector of parameters is decompressed so as to generate a discretized PSF.

Jimenez, Jr., Edward Steven↗
DOE OSTI2025

GLBRC Soil Yearlong Incubation 13C-SIP-Lipidomics

Data package for Lipids represent a dynamic, yet stable pool of microbially-derived soil carbon This data is published under a CC0 license. The authors encourage data reuse and request attribution by referencing the below citations for the data packages and associated manuscript. Please cite as: Rempfert KR, Bell SL, Kasanke CP, Kyle JE, Hofmockel KS. 2025. GLBRC Soil Yearlong Incubation 13C-SIP-Lipidomics. [Data Set] PNNL DataHub. doi: Rempfert KR, Bell SL, Kasanke CP, Kyle JE, Hofmockel KS. 2025. MSV000097435: GLBRC soil yearlong incubation 13C-SIP-Lipidomics [Data Set] MassIVE. doi:10.25345/C57659T3K Rempfert KR, Bell SL, Kasanke CP, Kyle JE, Hofmockel KS. 2025. Lipids represent a dynamic, yet stable pool of microbially-derived soil carbon. In Prep This data package consists of compound-specific 13C SIP-lipidomics data from a yearlong tracer incubation experiment designed to investigate microbial lipid persistence in switchgrass bioenergy crop soils. In order to explore how lipid structure may modulate the persistence of C in soil lipids, we leveraged soils from two sites (Michigan - sandy texture, Wisconsin - silty texture) operated by the U.S. Department of Energy-funded Great Lakes Bioenergy Research Center (GLBRC). These sites had comparable climates, identical management practices, but contrasting soil textures, allowing us to assess the variability of lipid accrual or degradation in soils as well as provide insight regarding the degree to which edaphic properties may regulate the retention of soil lipids. Untargeted lipidomics analyses were performed to identify 13C-labeled lipids in the soil microbiome after long-term incubation. Soils were supplemented with 100 micrograms glucose per gram dry soil (99 atom % 13C or natural abundance for paired control) and incubated; samples were collected two months and one year after glucose addition. Lipid extracts (MPLEx) were analyzed by LC-MS/MS and identified using LIQUID. Calculation of isotopic enrichment of lipids was performed by targeted approach using TarMet to quantify lipid isotopologues and IsoCorrectoR to correct for natural abundance isotopes. Contents: Data package contents reported here are the first version and contain downstream analysis files for the raw LC-MS mass spectrometry files (.mzXML) deposited at the MassIVE database repository under accession MSV000097435 (80 experimental runs; 5.85 GB) | MassIVE DOI: 10.25345/C57659T3K. Support files include the additional data download 'Read Me' file containing data descriptor information. Reported data download contents are structured for compliance with project data sharing guidelines, community standards initiatives, and sponsor stakeholder policies supporting FAIR data principles. Data processing software, analysis tools, and data workflows are listed below corresponding to the host repository long-term location. Available Data Downloads (0.3 GB): "GLBRC soil yearlong incubation 13C-SIP-Lipidomics_readme.txt" - 'Read Me' data package content file (txt) "GLBRC_DataPackage_analysis files" - Data processing files (Rmd) and saved intermediate data processing outputs (rds, csv, xlsx) "GLBRC_13C_lipidomics_dataset.xlsx" - processed data in tabular format (xlsx) Linked Software: LIQUID LC-MS Analysis Software | 10.5281/zenodo.6459462 Lipid Mini-On Software Tools | 10.5281/zenodo.1492803 pmartR Omics Statistical Software | 10.5281/zenodo.6108667 xcms (v4.3.3) TarMet (v1.1.1) IsoCorrectoR (1.24.0) Funding Acknowledgments: This research was supported by an Early Career Research Program award funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (OBER) Genomic Science program under FWP 68292, FWP 07880 and EMSL Exploratory Research Project 51095. A portion of this work was performed in the William R. Wiley Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by OBER and located at Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated by Battelle for the DOE under Contract DE-AC05-76RLO1830.

Rempfert, Kaitlin R [Pacific Northwest National La↗
DOE OSTI2022

Joint iterative reconstruction and 3D rigid alignment for X-ray tomography

X-ray tomography is widely used for three-dimensional structure determination in many areas of science, from the millimeter to the nanometer scale. The resolution and quality of the 3D reconstruction is limited by the availability of alignment parameters that correct for the mechanical shifts of the sample or sample stage for the images that constitute a scan. In this paper we describe an algorithm for marker-free, fully automated and accurately aligned and reconstructed X-ray tomography data. Our approach solves the tomographic reconstruction jointly with projection data alignment based on a rigid-body deformation model. We demonstrate the robustness of our method on both synthetic phantom and experimental data and show that our method is highly efficient in recovering relatively large alignment errors without prior knowledge of a low resolution approximation of the 3D structure or a reasonable estimate of alignment parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Analysis of Slow Spill Data for the Mu2e Experiment

The execution of the Mu2e experiment requires a stable, low-intensity proton beam from the Delivery Ring to produce clean data and protect equipment. This is done by performing a “slow extraction,” which is the gradual contraction of the stable region within the accelerator’s beam pipe. The Delivery Ring is currently unable to perform slow extraction with the stability required by Mu2e. To resolve this, the FAN-C team is training machine learning models with the purpose of replacing the Delivery Ring’s current PID controllers with AI-powered controllers. Training these models requires clean, processed data from slow spills. Over the course of this project, data from previous slow spills were processed and analyzed, and the clean data, graphs, and insights gained from the process were provided to the FAN-C team to assist them in their efforts.

Osborn, Thomas [Purdue U., West Lafayette]↗
DOE OSTI2021

Illinois State Geological Survey (ISGS), Illinois Basin - Decatur Project (IBDP) Seismic Data, July 7, 2021. Midwest Geological Sequestration Consortium (MGSC) Phase III Data Sets. DOE Cooperative Agreement No. DE-FC26-05NT42588.

Seismic data from the IBDP, included primarily under folders: Active_Seismic_Data and Passive_Seismic_Events_Monitoring. The data included under IBDP_Located_Microseismic_Event_Data are a subset of microseismic (Passive Seismic Events Monitoring) data acquired throughout the project from pre-injection to shut-in. These data represent the “located” microseismic events and the two datasets, located in folders Downhole_Geophone_Data and Surface_Seismometer_Data, are correlated.

3D Seismic,Carbon Sequestration,Decatur,Illinois B↗
DOE OSTI2021

Investigation of Delayed Neutron Sensitivities for Several ICSBEP Benchmarks using MCNP

The effective delayed neutron (β$_{eff}$) is a very important parameter for reactor and criticality applications. This parameter is equal to the difference in reactivity between delayed critical ($k_{eff}$ = 1, which requires both prompt and delayed neutrons to achieve criticality) and prompt critical ($k_p$ = 1, which requires only prompt neutrons to achieve criticality). This is often referred to as the delayed critical "window" (the region of criticality between delayed and prompt critical). β$_{eff}$ is a reactor kinetics parameters and depends on the nuclides in the system that undergo fission as well as the spectral characteristics of the system. Measurements of β$_{eff}$ have been performed for many criticality experiments. The EUCLID (Experiments Underpinned by Computational Learning for Improvements in nuclear Data) project at Los Alamos National Laboratory (LANL) aims to constrain nuclear data by using a suite of measurement types beyond $k_{eff}$. Our team has recently investigated the use of pulsed spheres for nuclear data validation. Several other measurement methods are also of interest, including β$_{eff}$ (investigated here) and reactivity coefficients (investigated in a separate work at this same meeting). One focus of our work is to determine if other methods are complimentary to the critical experiments already utilized for nuclear data validation. This is important because if a method has similar sensitivities then it will not be particularly useful for nuclear data validation as it will provide the same information as the critical experiments already being used. Here "similar" could refer to several characteristics, one being the shape of a sensitivity profile over energy. In the future, these methods will be utilized (with both existing and new experiments) in machine learning algorithms for nuclear validation, similar to what is currently done for criticality experiments. In order to use a measurement type for nuclear validation, it is necessary to obtain cross-section sensitivities for that parameter. This work looks at one approach to estimate β$_{eff}$ sensitivities by utilizing $k_{eff}$ sensitivities within Monte Carlo N-Particle ® Code Version 6.2. This is applied to several criticality benchmarks. Results are compared and the benefits and limitations of this approach are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Investigating Fission Reaction Rate Ratio Sensitivities [Abstract]

Reaction rate ratios are a measurable parameter for reactor and criticality applications. A number of foil irradiations and fission chamber measurements have been performed for critical assemblies at Los Alamos National Laboratory starting in the 1950’s including (i) Godiva, a bare HEU spherical assembly; (ii) Flattop-25, a spherical assembly consisting of an HEU core and a natural uranium reflector; (iii) Jezebel, a bare 239 Pu assembly; and (iv) Flattop-Pu, a spherical assembly consisting of a 239Pu core and a natural uranium reflector. Fission ratio data for 238 U(n,f)/ 235 U(n,f), 237 Np(n,f)/ 235 U(n,f), 233 U(n,f)/ 235 U(n,f) and 239 Pu(n,f)/ 235 U(n,f) were obtained and reported. The EUCLID (Experiments Underpinned by Computational Learning for Improvements in nuclear Data) project at Los Alamos National Laboratory (LANL) aims to constrain nuclear data by using a suite of measurement types beyond k-effective. Recent investigations include the use of pulsed spheres for nuclear data validation and other measurement methods of interest. One focus of the work is to determine if other methods are complimentary to the critical experiments utilized for nuclear data validation. It is anticipated the investigations will help inform methods that may be utilized in machine learning algorithms for nuclear validation. In order to use a measurement type for nuclear validation, it is necessary to obtain cross-section sensitivities for parameters. This work looks at reaction rate ratio sensitivities with SENSMG and Monte Carlo N-Particle R Code Version 6.21.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Scientific data from precipitation driver response model intercomparison project

This data descriptor reports the main scientific values from General Circulation Models (GCMs) in the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). The purpose of the GCM simulations has been to enhance the scientific understanding of how changes in greenhouse gases, aerosols, and incoming solar radiation perturb the Earth’s radiation balance and its climate response in terms of changes in temperature and precipitation. Here we provide global and annual mean results for a large set of coupled atmospheric-ocean GCM simulations and a description of how to easily extract files from the dataset. The simulations consist of single idealized perturbations to the climate system and have been shown to achieve important insight in complex climate simulations. We therefore expect this data set to be valuable and highly used to understand simulations from complex GCMs and Earth System Models for various phases of the Coupled Model Intercomparison Project.

54 ENVIRONMENTAL SCIENCES↗