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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

NASA's Optical Measurement Program 2014

The Optical Measurements Group (OMG) within the NASA Orbital Debris Program Office (ODPO) addresses U.S. National Space Policy goals by monitoring and characterizing debris. Since 2001, the OMG has used the Michigan Orbital Debris Survey Telescope (MODEST) at Cerro Tololo Inter-American Observatory (CTIO) in Chile for general orbital debris survey. The 0.6-m Schmidt MODEST provides calibrated astronomical data of GEO targets, both catalogued and uncatalogued debris, with excellent image quality. The data are utilized by the ODPO modeling group and are included in the Orbital Debris Engineering Model (ORDEM) v. 3.0. MODEST and the CTIO/SMARTS (Small and Moderate Aperture Research Telescope System) 0.9 m both acquire filter photometric data, as well as synchronously observing targets in selected optical filters. This information provides data used in material composition studies as well as longer orbital arc data on the same target, without time delay or bias from a rotating, tumbling, or spinning target. NASA, in collaboration with the University of Michigan, began using the twin 6.5-m Magellan telescopes at Las Campanas Observatory in Chile for deep imaging (Baade) and spectroscopic data (Clay) in 2011. Through the data acquired on Baade, debris have been detected that are ~3 magnitudes fainter than detections with MODEST, while the data from Clay provide better resolved information used in material characterization analyses via selected bandpasses. To better characterize and model optical data, the Optical Measurements Center (OMC) at NASA/JSC has been in operation since 2005, resulting in a database of comparison laboratory data. The OMC is designed to emulate illumination conditions in space using equipment and techniques that parallel telescopic observations and source-target-sensor orientations. Lastly, the OMG is building the Meter Class Autonomous Telescope (MCAT) at Ascension Island. The 1.3-m telescope is designed to observe GEO and LEO targets, using a modified Ritchey-Chrétien configuration on a double horseshoe equatorial mount to allow tracking objects at LEO rates through the dome's keyhole at zenith. Through the data collection techniques employed at these unique facilities, NASA's ODPO has developed a multi-faceted approach to characterize the orbital debris risk to satellites in various altitudes and provide material characterization of debris via photometric and spectroscopic measurements. Ultimately, the data are used in conjunction with in-situ and radar measurements to provide accurate data for models of our space environment and service spacecraft risk assessment.

Cowardin, H.↗
DOE OSTI2024

Informing forest carbon inventories under the Paris Agreement using ground-based forest monitoring data

Human interactions with forests have shaped Earth's climate for millennia and will continue to do so as we target net-zero emission goals. Accurately characterizing these climate impacts requires making reliable forest carbon data available for forest monitoring and planning. Here, we develop a semi-automated process for submitting forest carbon measurements from the largest relevant scientific database to the International Panel on Climate Change's Emission Factor Database, which currently has sparse forest carbon data. Building this bridge from scientific research to international policy is an important step towards managing forests in a net-zero motivated future. Humans have been influencing Earth's climate via transformative impacts on forests for millennia, and forests are now recognized as critical to climate change mitigation under the Paris Agreement. The efficacy of climate change mitigation planning and reporting depends on quality data on forest carbon (C) stocks and changes. The Emission Factor Database (EFDB) of the International Panel on Climate Change (IPCC) is intended to be a definitive source for such data, but needs comprehensive and well-documented data to be so. To facilitate submission of forest C estimates from scientific studies to EFDB, we develop and document a process for semi-automated data submission from the Global Forest C database (ForC v4.0), which is the largest compilation of ground-based forest C estimates. We then assess the data currently available through ForC and provide recommendations for improving forest data collection, analysis, and reporting. As of September 2024, ForC contained ~19,286 records potentially relevant to EFDB, 1068 of which had been submitted and posted to EFDB. These represented 19% of the total EFDB records for forest land. Records were unevenly distributed across variables and geographic regions. ForC records (37%) reviewed could not be submitted because the original publication lacked required information. In the future, ground-based forest C estimates should target gaps in the record, and studies should ensure that they report all information necessary for inclusion in EFDB. Given that climate change is rapidly impacting the world's forests, timely reporting of recent estimates will be critical to accurate forest C inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A machine learning Automated Recommendation Tool for synthetic biology

Abstract Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, fatty acids, and tryptophan. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Uranium Bed Design Parameters for Tritium Plants Supporting Fusion Reactors

Uranium hydride is commonly used to store hydrogen or its isotopes in a solid state. The Self-Assaying Tritium Accountancy and Containment Unit for ITER (STACI) is a 5.2-kg bed of depleted uranium (dU) capable of holding up to 33 mol of hydrogen or its isotopes. Here we summarize the data analysis of past experimental campaigns with STACI, with the aim of describing the kinetics and thermodynamics of the hydriding process. Computed tomography imaging was performed on STACI both before and after its experimental campaign, and a high degree of swelling was observed in the dU. Literature on studies in regard to the swelling of large (multikilogram) quantities of uranium hydride for storage applications was not identified during this study. Data from the experimental campaign, as well as data on the formation reaction, are presented. The authors hope to create an analytical model of STACI based on these data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Optimal hybrid power plants for electric vehicle charging demand

Transmission constraints, increasing motivations to decarbonize, and concerns over peak electric vehicle (EV) load impacts on local grids have driven electric customers to consider behind-the-meter, hybrid power plant generation and storage at the distributed-grid level for EV charging. In this study, we develop capabilities to optimize hybrid power plant component capacities for EV charging. We then demonstrate these capabilities in a case study for Boulder, Colorado, using public EV charging data as well as wind and solar resource data. Our results show system designs that balance the cost of energy with load-meeting and peak shaving performance. Within the case study, systems designed for wind, solar photovoltaic (PV), and storage resulted in lower cost of energy than those optimized for PV and storage only. This indicates that in areas where wind resource exists, hybrid power plants that include wind, PV, and battery assets can better meet EV charging loads (including peak loads that are prone to overloading local grids) than PV and battery assets alone. Future work to address limitations in this paper include extending cost modeling to include performance losses (e.g., based on operations or weather) and charging station costs to estimate levelized cost of charging, and quantifying uncertainty and error in our aggregation methods for estimating EV charging loads at the hourly timescale.

14 SOLAR ENERGY↗
DOE OSTI2022

Canted antiferromagnetic phases in the candidate layered Weyl material EuMnSb 2

EuMnSb 2 is a candidate topological material which can be tuned towards a Weyl semimetal, but there are differing reports for its antiferromagnetic (AFM) phases. The coupling of bands dominated by pure Sb layers hosting topological fermions to Mn and Eu magnetic states provides a potential path to tune the topological properties. In this study we present single-crystal neutron diffraction, magnetization, and heat-capacity data as well as polycrystalline 151 Eu Mössbauer data which show that three AFM phases exist as a function of temperature, and we present a detailed analysis of the magnetic structure in each phase. The Mn magnetic sublattice orders into a C-type AFM structure below T N Mn = 323 (1) K with the ordered Mn magnetic moment μ Mn lying perpendicular to the layers. AFM ordering of the Eu sublattice occurs below T N Eu1 = 23 (1) K with the ordered Eu magnetic moment μ Eu canted away from the layer normal and μ Mn retaining its higher temperature order. μ Eu is ferromagnetically aligned within each Eu layer but exhibits a complicated AFM layer stacking. Both of these higher-temperature phases are described by magnetic space group (MSG) $\mathcal{Pn'm'a'}$ with the chemical and magnetic unit cells having the same dimensions. Cooling below T N Eu2 =9 (1) K reveals a third AFM phase where μ Mn remains unchanged but μ Eu develops an additional substantial in-plane canting. This phase has MSG $\mathcal{P11}$ $\frac{2_1}{a′}$. We also find some evidence of short-range magnetic correlations associated with the Eu between 12 K≲ T≲ 30 K . Using the determined magnetic structures, we postulate the signs of nearest-neighbor intralayer and interlayer exchange constants and the magnetic anisotropy within a general Heisenberg model. We then discuss implications of the various AFM states in EuMnSb 2 and their potential for tuning topological properties.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Flow-based likelihoods for non-Gaussian inference

We investigate the use of data-driven likelihoods to bypass a key assumption made in many scientific analyses, which is that the true likelihood of the data is Gaussian. In particular, we suggest using the optimization targets of flow-based generative models, a class of models that can capture complex distributions by transforming a simple base distribution through layers of nonlinearities. We call these flow-based likelihoods (FBL). We analyze the accuracy and precision of the reconstructed likelihoods on mock Gaussian data, and show that simply gauging the quality of samples drawn from the trained model is not a sufficient indicator that the true likelihood has been learned. We nevertheless demonstrate that the likelihood can be reconstructed to a precision equal to that of sampling error due to a finite sample size. We then apply FBLs to mock weak lensing convergence power spectra, a cosmological observable that is significantly non-Gaussian (NG). We find that the FBL captures the NG signatures in the data extremely well, while other commonly used data-driven likelihoods, such as Gaussian mixture models and independent component analysis, fail to do so. This suggests that works that have found small posterior shifts in NG data with data-driven likelihoods such as these could be underestimating the impact of non-Gaussianity in parameter constraints. By introducing a suite of tests that can capture different levels of NG in the data, we show that the success or failure of traditional data-driven likelihoods can be tied back to the structure of the NG in the data. Here, unlike other methods, the flexibility of the FBL makes it successful at tackling different types of NG simultaneously. Because of this, and consequently their likely applicability across datasets and domains, we encourage their use for inference when sufficient mock data are available for training.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

2014-2015 Puget Sound Regional Travel Study

The 2014-2015 Puget Sound Regional Travel Study collected information about household and individual travel patterns for residents throughout a four-county region in Washington State. Study results were used to update the region's travel and land-use models and to calibrate local traffic and travel models. The study also helped the Puget Sound Regional Council (PSRC) and its regional partners develop plans that accommodate the diverse travel needs and preferences of residents. The Resource Systems Group administered the study on behalf of PSRC. Global positioning system (GPS)-equipped smartphones were used to provide data pertaining to the daily travel of 547 individual participants. Because the region's university students may have been underrepresented in the initial 2014 household travel study, the PSRC added a college-population travel survey in fall 2014. In spring 2015, a second household data collection effort was conducted to increase the frequency of data collection and to collect GPS data as well as a sample of longitudinal data from households that completed the 2014 survey.

1Hz data↗
DOE OSTI2023

AmeriFlux FLUXNET-1F US-xCP NEON Central Plains Experimental Range (CPER)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xCP NEON Central Plains Experimental Range (CPER). This is the FLUXNET version of the carbon flux data for the site US-xCP NEON Central Plains Experimental Range (CPER) produced by applying the standard ONEFlux (1F) software. Site Description - Central Plains Experimental Range (CPER) site in north central Colorado. CPER served as part of the Shortgrass Steppe LTER from 1982-2014 and is now home to a fully instrumented NEON site and a wealth of relevant historical data as well as ongoing collections of complementary data(i.e. airborne remote sensing, soil temperature and moisture, phenology measurements, plant biomass and more)

Network), NEON (National Ecological Observatory↗
DOE OSTI2020

NCSP IER 476 CED-4A Documentation

This document is a memo to the National Criticality Safety Program (NCSP) management that details the KRUSTY high temperature run for the KRUSTY experiment, which took place in March of 2018. The goal for writing this memo was to collect all of the available information into one document, so that researchers can use this document for modeling and analysis. One important part to this document is the data package, which is referenced in the document and contains all the available experimental data for the KRUSTY high temperature run. The data package itself is quite large (1.6 GB) and is not included in the document itself. However, sample files have been provided in the document, in addition to plots of the data as well as a description of each data set.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

In Situ Data Analysis Through Physics-informed Tensor Decompositions (LDRD Final Report)

We introduce a new low-dimensional model of high-dimensional numerical simulation data based on low-rank tensor decompositions. Our new model aims to minimize differences between the model data and simulation data as well as functions of the model data and functions of the simulation data. This novel approach to dimensionality reduction of simulation data provides a means of directly incorporating quantities of interests and invariants associated with conservation principles associated with the simulation data into the low-dimensional model, thus enabling more accurate analysis of the simulation without requiring access to the full set of high-dimensional data. Computational results of applying this approach to two standard low-rank tensor decompositions of data arising from simulation of combustion and plasma physics are presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Community Requirements Meta-Analysis: Characterizing Needs and Opportunities for HPDF

This High Performance Data Facility (HPDF) Project is creating a new scientific user facility to provide advanced infrastructure for data-intensive science, supporting the DOE’s Office of Science (SC) community. HPDF’s mission is to enable and accelerate scientific discovery by delivering state-of-the-art data management infrastructure, capabilities, and tools. This meta-analysis examines the needs of the breadth of the SC community, captured in publicly available community reports or mission documents. The meta-analysis identifies and provides initial characterization of fifteen core requirements for the HPDF Project team to consider during the conceptual design phase. The fifteen requirements illustrate how scientific work among SC communities requires modern, seamless user experiences across the ASCR Ecosystem to advance the use of large volumes of heterogeneous data. The scientific community requires support for the missing middle of compute between local and HPC to interactively and collaboratively use growing datasets. Data producers and end users will benefit from enhanced data catalogs and portals that improve data access through advanced search of well curated data. The fifteen requirements are examined here organized across five themes for discussion. Examples in each theme illustrate the array of scientific needs that convey the important role that the fully realized and operational High Performance Data Facility will be able to play as an integral part of the evolving ASCR Ecosystem. Our amalgamated data tables from ESnet reports demonstrate ranges to the volumes of data HPDF must be concerned with, but limitations are inherent to this meta-analysis (see Key Challenges & Limitations). Feedback and validation of these requirements along with additional details and emergent community requirements will be gathered through user research and design activities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Optimizing Alabama’s CO 2 Storage in Shelby County (Project OASIS) Milestone 6.0: Evaluation of Class VI Readiness

Introduction. Project OASIS is approximately 30 miles southeast of Birmingham, Alabama, and approximately 5 miles north-northwest of Alabama Power Company's Plant Gaston. Geologically, the Project area is in the Alabama fold and thrust belt province. This work builds on the initiatives of the Southeast Regional Carbon Utilization and Storage Acceleration Partnership (SECARB-USA, DE-FE0031830) that identified nearly 500 million metric tonnes of CO 2 emitted on an annual basis that is not collocated with prospective storage geology (the Coastal Plain of the Southeastern US in this context). This observation suggests costly investments in connective infrastructure (e.g., pipelines) or exploratory well drilling campaigns to identify CO 2 storage opportunities in under explored areas. While not traditionally thought of for saline storage, these studies suggest that storage prospects in the Valley and Ridge Province occur in relatively flat lying structural panels between thrust faults. For the Project OASIS region, available geologic studies related to hydrocarbon exploration suggest that Cambro-Ordovician carbonates and Cambrian clastic units offer multiple potential storage intervals, and that regional confining systems are present, such as the tectonically thickened Floyd-Parkwood Shale. The Project OASIS surface property is owned by a timber and land stewardship company, The Westervelt Company, Inc., who worked with the Project Team to select and prepare adequate sites for geologic assessment. The purpose of drilling the Westover Stratigraphic Test Well #2 was to collect geologic data to model the feasibility of commercial scale CO 2 injection and storage in an under explored region. This initiative benefits the regions emitters as the data generated from this study can inform their own internal decision making. The field program included geological and geophysical evaluations, reservoir engineering analyses, and risk assessments. This report evaluates existing data, as well as a variety of modeling scenarios to evaluate project readiness. Importantly, the impact of this study is not limited to Alabama as there are numerous large emitters throughout Appalachia, in similar geologic settings, contemplating their decarbonization options.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Learning thermodynamic master equations for open quantum systems

The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and other applications. Scientific machine learning techniques have been applied to this problem in a variety of ways, including by modeling with deep neural networks. However, the majority of mathematical models describing open quantum systems are linear, and the natural nonlinearities in learnable models have not been incorporated using physical principles. We present a data-driven model for open quantum systems that includes learnable, thermodynamically consistent terms. The trained model is interpretable, as it directly estimates the system Hamiltonian and linear components of coupling to the environment. We validate the model on synthetic two and three-level data, as well as experimental two-level data collected from a quantum device at Lawrence Livermore National Laboratory.

Mathematics and Computing↗
NASA NTRS1972

Feasibility of microwave holography for imaging the sea surface

The possibility of imaging the sea surface in three dimensions by means of microwave holography from a low-flying aircraft is considered. Data cover a brief feasibility study and a review of some computer experiments in which it was demonstrated that it is possible to compute three-dimensional images of objects from raw holographic data recorded on magnetic tape. These experiments used synthetic data.

Wells, W.↗
NASA NTRS1975

The Texas Remote Sensing Training Project

The project was designed to train federal, state and regional agency managers, scientists and engineers. A one-week seminar was designed and implemented to build vocabulary, introduce technical subject areas and give students enough training to allow them to relate remote sensing technology to operational agency projects. The seminar was designed to perform the dual function of conveying enough remote sensing information to be of value as a stand-alone and preparing students for detailed pattern recognition training. The LARSYS III portion of the training project was executed exactly as designed in the LARSYS training materials package; the LARSYS package did not contain a LANDSAT training module. Two LANDSAT training modules were developed using Texas LANDSAT data. One module contained central Texas data and the second module contained coastal zone data.

Wells, J. B.↗
NASA NTRS1978

Detailed gravity anomalies from GEOS-3 satellite altimetry data

A technique for deriving mean gravity anomalies from dense altimetry data was developed. A combination of both deterministic and statistical techniques was used. The basic mathematical model was based on the Stokes' equation which describes the analytical relationship between mean gravity anomalies and geoid undulations at a point; this undulation is a linear function of the altimetry data at that point. The overdetermined problem resulting from the excessive altimetry data available was solved using Least-Squares principles. These principles enable the simultaneous estimation of the associated standard deviations reflecting the internal consistency based on the accuracy estimates provided for the altimetry data as well as for the terrestrial anomaly data. Several test computations were made of the anomalies and their accuracy estimates using GOES-3 data.

Gopalapillai, G. S.↗