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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Software for Generating Troposphere Corrections for InSAR Using GPS and Weather Model Data

Atmospheric errors due to the troposphere are a limiting error source for spaceborne interferometric synthetic aperture radar (InSAR) imaging. This software generates tropospheric delay maps that can be used to correct atmospheric artifacts in InSAR data. The software automatically acquires all needed GPS (Global Positioning System), weather, and Digital Elevation Map data, and generates a tropospheric correction map using a novel algorithm for combining GPS and weather information while accounting for terrain. Existing JPL software was prototypical in nature, required a MATLAB license, required additional steps to acquire and ingest needed GPS and weather data, and did not account for topography in interpolation. Previous software did not achieve a level of automation suitable for integration in a Web portal. This software overcomes these issues. GPS estimates of tropospheric delay are a source of corrections that can be used to form correction maps to be applied to InSAR data, but the spacing of GPS stations is insufficient to remove short-wavelength tropospheric artifacts. This software combines interpolated GPS delay with weather model precipitable water vapor (PWV) and a digital elevation model to account for terrain, increasing the spatial resolution of the tropospheric correction maps and thus removing short wavelength tropospheric artifacts to a greater extent. It will be integrated into a Web portal request system, allowing use in a future L-band SAR Earth radar mission data system. This will be a significant contribution to its technology readiness, building on existing investments in in situ space geodetic networks, and improving timeliness, quality, and science value of the collected data

Moore, Angelyn W.↗
DOE OSTI2023

Assessing carbon storage capacity and saturation across six central US grasslands using data–model integration

Abstract. Future global changes will impact carbon (C) fluxes and pools in most terrestrial ecosystems and the feedback of terrestrial carbon cycling to atmospheric CO2. Determining the vulnerability of C in ecosystems to future environmental change is thus vital for targeted land management and policy. The C capacity of an ecosystem is a function of its C inputs (e.g., net primary productivity – NPP) and how long C remains in the system before being respired back to the atmosphere. The proportion of C capacity currently stored by an ecosystem (i.e., its C saturation) provides information about the potential for long-term C pools to be altered by environmental and land management regimes. We estimated C capacity, C saturation, NPP, and ecosystem C residence time in six US grasslands spanning temperature and precipitation gradients by integrating high temporal resolution C pool and flux data with a process-based C model. As expected, NPP across grasslands was strongly correlated with mean annual precipitation (MAP), yet C residence time was not related to MAP or mean annual temperature (MAT). We link soil temperature, soil moisture, and inherent C turnover rates (potentially due to microbial function and tissue quality) as determinants of carbon residence time. Overall, we found that intermediates between extremes in moisture and temperature had low C saturation, indicating that C in these grasslands may trend upwards and be buffered against global change impacts. Hot and dry grasslands had greatest C saturation due to both small C inputs through NPP and high C turnover rates during soil moisture conditions favorable for microbial activity. Additionally, leaching of soil C during monsoon events may lead to C loss. C saturation was also high in tallgrass prairie due to frequent fire that reduced inputs of aboveground plant material. Accordingly, we suggest that both hot, dry ecosystems and those frequently disturbed should be subject to careful land management and policy decisions to prevent losses of C stored in these systems.

58 GEOSCIENCES↗
DOE OSTI2021

Modeling atmospheric data and identifying dynamics Temporal data-driven modeling of air pollutants

Atmospheric modelling has recently experienced a surge with the advent of deep learning. Most of these models, however, predict concentrations of pollutants following a data-driven approach in which the physical laws that govern their behaviors and relationships remain hidden. Furthermore, with the aid of real-world air quality data collected hourly in different stations throughout Madrid, we present a case study using a series of data-driven techniques with the following goals: (1) Find systems of ordinary differential equations that model the concentration of pollutants and their changes over time; (2) assess the performance and limitations of our model using stability analysis; (3) reconstruct the time series of chemical pollutants not measured in certain stations using delay coordinate embedding results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Global and regional variability and change in terrestrial ecosystems net primary production and NDVI: A model-data comparison

The net primary productivity (NPP) is commonly used for understanding the dynamics of terrestrial ecosystems and their role in carbon cycle. We used a combination of the most recent NDVI and model–based NPP estimates (from five models of the TRENDY project) for the period 1982-2012, to study the role of terrestrial ecosystems in carbon cycle under the prevailing climate conditions. We found that 80% and 67% of the global land area showed positive NPP and NDVI values, respectively, for this period. The global NPP was estimated to be about 63 Pg C y -1 , with an increase of 0.214 Pg C y -1 y -1 . Similarly, the global mean NDVI was estimated to be 0.33, with an increasing trend of 0.00041 y-1. The spatial patterns of NPP and NDVI demonstrated substantial variability, especially at the regional level, for most part of the globe. However, on temporal scale, both global NPP and NDVI showed a corresponding pattern of increase (decrease) for the duration of this study except for few years (e.g. 1990 and 1995-98). Generally, the Northern Hemisphere showed stronger NDVI and NPP increasing trends over time compared to the Southern Hemisphere; however, NDVI showed larger trends in Temperate regions while NPP showed larger trends in Boreal regions. Among the five models, the maximum and minimum NPP were produced by JULES (72.4 Pg C y -1 ) and LPJ (53.72 Pg C y -1 ) models, respectively. At latitudinal level, the NDVI and NPP ranges were ~0.035 y -1 to ~-0.016 y -1 and ~0.10 Pg C y -1 y -1 to ~-0.047 Pg C y -1 y -1 , respectively. Overall, the results of this study suggest that the modeled NPP generally correspond to the NDVI trends in the temporal dimension. Lastly, the significant variability in spatial patterns of NPP and NDVI trends points to a need for research to understand the causes of these discrepancies between molded and observed ecosystem dynamics, and the carbon cycle.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Human Spaceflight Architecture Model (HSFAM) Data Dictionary

HSFAM is a data model based on the DoDAF 2.02 data model with some for purpose extensions. These extensions are designed to permit quantitative analyses regarding stakeholder concerns about technical feasibility, configuration and interface issues, and budgetary and/or economic viability.

architecture definition↗
NASA NTRS2008

Using an Informative Missing Data Model to Predict the Ability to Assess Recovery of Balance Control after Spaceflight

Astronauts show degraded balance control immediately after spaceflight. To assess this change, astronauts' ability to maintain a fixed stance under several challenging stimuli on a movable platform is quantified by "equilibrium" scores (EQs) on a scale of 0 to 100, where 100 represents perfect control (sway angle of 0) and 0 represents data loss where no sway angle is observed because the subject has to be restrained from falling. By comparing post- to pre-flight EQs for actual astronauts vs. controls, we built a classifier for deciding when an astronaut has recovered. Future diagnostic performance depends both on the sampling distribution of the classifier as well as the distribution of its input data. Taking this into consideration, we constructed a predictive ROC by simulation after modeling P(EQ = 0) in terms of a latent EQ-like beta-distributed random variable with random effects.

Feiveson, Alan H.↗
DOE OSTI2022

Nuclide Inventory Benchmark for BWR Spent Nuclear Fuel: Challenges in Evaluation of Modeling Data Assumptions and Uncertainties

This work discusses challenges and approaches to uncertainty analyses associated with the development of a nuclide inventory benchmark for fuel irradiated in a boiling water reactor. The benchmark under consideration is being developed based on experimental data from the SFCOMPO international database. The focus herein is on how to address missing data in fuel design and operating conditions that are important for adequately simulating the time-dependent changes in fuel during irradiation in the reactor. The effects of modeling assumptions and uncertainties in modeling parameters on the calculated nuclide inventory were analyzed and quantified through computational models developed using capabilities in the SCALE code system. Particular attention was given to the impact of the power history and water coolant density on the calculated nuclide inventory, as well as to the effect of geometry modeling considerations not usually addressed in a nuclide inventory benchmark. These considerations include gap closure, channel bow, and channel corner radius, which do not usually apply to regular reactor operation but are relevant for assessing impacts of potential anomalous operating scenarios.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2003

Effects of a Scanning Reflectarray Antenna on Modulated Data Modeled

The ferroelectric reflectarray is a new type of scanning antenna under development for space and ground communications and radar applications at the NASA Glenn Research Center. The reflectarray offers tremendous cost, gain, and efficiency advantages in comparison to the state-of-the-art phased arrays, but there is an inherent intersymbol interference (ISI) problem with digital communication data streams processed through the array because of the way the reflectarray is operated. This interference stems from the fact that the antenna beam is formed by the superposition of reflected waves from the array elements. The delays from the elements to the observation point of each wave differ. Ferroelectric phase shifters are designed to compensate for the modulo-2p phase differences so that the waves will constructively interfere at the sinusoidal carrier level; however, the delays of the integer-number multiple of the carrier period are not compensated for. This lack of compensation causes ISI in digital modulated signals. ISI has not been a problem with analog modulations and low-rate digital modulations since the distortion (or ISI) introduced is negligible. The problem becomes acute when the data rate gets higher, and the electrical size of the array gets bigger. Moreover, there are intrinsic transient effects associated with the phase shifters and the controller that executes the algorithm to direct the beam. That is, phase shifter updates to redirect the beam may be initiated simultaneously, but nonlinearity will cause different settling times. The ISI problem and other effects ultimately limit the size (gain) and data rate of the reflectarray. An ISI analysis was required to prove or disprove the suitability of the reflectarray antenna for high-rate digitally modulated signals and to give guidance for determining antenna parameters. In this research, the nature of the ISI of the reflectarray has been mathematically modeled.

Romanofsky, Robert R.↗
NASA NTRS1981

Observational, reference and model data on solar EUV, from measurements on AE-E

Information on solar irradiance at wavelengths below 185 nm, observed by the EUVS experiment on the AE-E satellite over the entire development of the present sunspot cycle 21, is important to a variety of investigations of planetary thermospheres and ionospheres. Since strictly observational information is generally lacking in both the completeness and the spectral detail required by the more advanced programs, it has been necessary to develop computer models in connection with fully detailed compilations of an appropriate reference spectrum. The period of July 13-18, 1976, is selected as an observationally reliable AE-E data reference period reflecting solar conditions of minimum activity for solar cycle 21. It is pointed out that the assignments of absolute irradiance-reference values have been drawn for all available sources of information other than that provided by AE-E.

Hinteregger, H. E.↗
NASA NTRS2015

Winter-to-Summer Precipitation Phasing in Southwestern North America: A Multi-Century Perspective from Paleoclimatic Model-Data Comparisons

The phasing of winter-to-summer precipitation anomalies in the North American monsoon (NAM) region 2 (113.25 deg W-107.75 deg W, 30 deg N-35.25 deg N-NAM2) of southwestern North America is analyzed in fully coupled simulations of the Last Millennium and compared to tree ring reconstructed winter and summer precipitation variability. The models simulate periods with in-phase seasonal precipitation anomalies, but the strength of this relationship is variable on multidecadal time scales, behavior that is also exhibited by the reconstructions. The models, however, are unable to simulate periods with consistently out-of-phase winter-to-summer precipitation anomalies as observed in the latter part of the instrumental interval. The periods with predominantly in-phase winter-to-summer precipitation anomalies in the models are significant against randomness, and while this result is suggestive of a potential for dual-season drought on interannual and longer time scales, models do not consistently exhibit the persistent dual-season drought seen in the dendroclimatic reconstructions. These collective findings indicate that model-derived drought risk assessments may underestimate the potential for dual-season drought in 21st century projections of hydroclimate in the American Southwest and parts of Mexico.

Variability↗
DOE OSTI2024

Chromium Poisoning ERMINE Model Data

This is the data from the paper "Systematic and Predictive Trends to Chromium Poisoning in Solid Oxide Fuel Cell Cathodes" by Hokon Kim et al., appearing in the Journal of Power Sources. The files here are the microstructures, mesh files, and outputs from the ERMINE finite element model of chromium poisoning in SOFC cathodes. Further description can be found in Readme.rtf and in the paper..

Chromium,SOFC↗
NASA NTRSDate not supplied

From Regolith to Living Off the Land: Formulating a Data Model to Catalog Lunar Construction Materials

Artemis Program objectives for sustainable, long-term presence on the Moon and more distant planetary surfaces will require learning to “Live off the Land”, relying on in-situ resource utilization to produce infrastructure and building materials from lunar regolith, icy subsurface deposits, and residual waste materials. Meeting demand for consumables while scaling development with resources found within the landing zone will require detailed data on the geology and environment of the lunar surface. Lunar infrastructure development will generate vast amounts of new engineering data regarding availability of processed feedstocks and their performance in building materials. Lunar engineering data accessible to program partners, research institutions and industry may help situate processes and specifications within the in-situ GIS context. Lunar missions to date have generated geological and ice favorability maps of the lunar surface, and recent technology studies have tested automated construction systems and novel material formulations using regolith simulants and binders. Current discussions focus on identifying key feedstocks, quantities required for nominal mission scenarios and infrastructure plans, and mapping the value chain from regolith to feedstock to consumables and construction materials.

lunar construction↗
NASA NTRSDate not supplied

From Regolith to Living Off the Land: Formulating a Data Model to Catalog Lunar Construction Materials

Artemis Program objectives for sustainable, long-term presence on the Moon and more distant planetary surfaces will require learning to “Live off the Land”, relying on in-situ resource utilization to produce infrastructure and building materials from lunar regolith, icy subsurface deposits, and residual waste materials. Meeting demand for consumables while scaling development with resources found within the landing zone will require detailed data on the geology and environment of the lunar surface. Lunar infrastructure development will generate vast amounts of new engineering data regarding availability of processed feedstocks and their performance in building materials. Lunar engineering data accessible to program partners, research institutions and industry may help situate processes and specifications within the in-situ GIS context. Lunar missions to date have generated geological and ice favorability maps of the lunar surface, and recent technology studies have tested automated construction systems and novel material formulations using regolith simulants and binders. Current discussions focus on identifying key feedstocks, quantities required for nominal mission scenarios and infrastructure plans, and mapping the value chain from regolith to feedstock to consumables and construction materials.

lunar construction↗
DOE OSTI2012

Updated Application of Spatial Data Modeling and Geographical Information Systems (GIS) for Identification of Potential Siting Options for Small Modular Reactors

Beginning in late 2008, Oak Ridge National Laboratory (ORNL) responded to ongoing internal and external studies addressing key questions related to our national electrical energy supply. This effort has led to the development and refinement of Oak Ridge Siting Analysis for power Generation Expansion (OR-SAGE), a tool to support power plant siting evaluations. The objective in developing OR-SAGE was to use industry-accepted approaches and/or develop appropriate criteria for screening sites and employ an array of geographic information systems (GIS) data sources at ORNL to identify candidate areas for a power generation technology application. The basic premise requires the development of exclusionary, avoidance, and suitability criteria for evaluating sites for a given siting application such as siting small modular reactors (SMRs). For specific applications of the tool, it is necessary to develop site selection and evaluation criteria (SSEC) that encompass a number of key benchmarks that essentially form the site environmental characterization for that application. These SSEC might include population density, seismic activity, proximity to water sources, proximity to hazardous facilities, avoidance of protected lands and floodplains, susceptibility to landslide hazards, and others.

GIS↗
DOE OSTI2012

Application of Spatial Data Modeling and Geographical Information Systems (GIS) for Identification of Potential Siting Options for Various Electrical Generation Sources

Oak Ridge National Laboratory (ORNL) initiated an internal National Electric Generation Siting Study, which is an ongoing multiphase study addressing several key questions related to our national electrical energy supply. This effort has led to the development of a tool, OR-SAGE (Oak Ridge Siting Analysis for power Generation Expansion), to support siting evaluations. The objective in developing OR-SAGE was to use industry-accepted approaches and/or develop appropriate criteria for screening sites and employ an array of Geographic Information Systems (GIS) data sources at ORNL to identify candidate areas for a power generation technology application. The initial phase of the study examined nuclear power generation. These early nuclear phase results were shared with staff from the Electric Power Research Institute (EPRI), which formed the genesis and support for an expansion of the work to several other power generation forms, including advanced coal with carbon capture and storage (CCS), solar, and compressed air energy storage (CAES). Wind generation was not included in this scope of work for EPRI. The OR-SAGE tool is essentially a dynamic visualization database. The results shown in this report represent a single static set of results using a specific set of input parameters. In this case, the GIS input parameters were optimized to support an economic study conducted by EPRI. A single set of individual results should not be construed as an ultimate energy solution, since US energy policy is very complex. However, the strength of the OR-SAGE tool is that numerous alternative scenarios can be quickly generated to provide additional insight into electrical generation or other GIS-based applications. The screening process divides the contiguous United States into 100 x 100 m (1-hectare) squares (cells), applying successive power generation-appropriate site selection and evaluation criteria (SSEC) to each cell. There are just under 700 million cells representing the contiguous United States. If a cell meets the requirements of each criterion, the cell is deemed a candidate area for siting a specific power generation form relative to a reference plant for that power type. Some SSEC parameters preclude siting a power plant because of an environmental, regulatory, or land-use constraint. Other SSEC assist in identifying less favorable areas, such as proximity to hazardous operations. All of the selected SSEC tend to recommend against sites. The focus of the ORNL electrical generation source siting study is on identifying candidate areas from which potential sites might be selected, stopping short of performing any detailed site evaluations or comparisons. This approach is designed to quickly screen for and characterize candidate areas. Critical assumptions supporting this work include the supply of cooling water to thermoelectric power generation; a methodology to provide an adequate siting footprint for typical power plant applications; a methodology to estimate thermoelectric plant capacity while accounting for available cooling water; and a methodology to account for future ({approx}2035) siting limitations as population increases and demands on freshwater sources change. OR-SAGE algorithms were built to account for these critical assumptions. Stream flow is the primary thermoelectric plant cooling source evaluated in this study. All cooling was assumed to be provided by a closed-cycle cooling (CCC) system requiring makeup water to account for evaporation and blowdown. Limited evaluations of shoreline cooling and the use of municipal processed water (gray) cooling were performed. Using a representative set of SSEC as input to the OR-SAGE tool and employing the accompanying critical assumptions, independent results for the various power generation sources studied were calculated.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRSDate not supplied

Application of Artificial Intelligence for Surface PM2.5 Estimations from Geostationary Satellite and Atmospheric Numerical Model Data

PM2.5, particulate matter (PM) with a diameter less than or equal to 2.5 μm, is emitted from anthropogenic fuel combustion and forest fires. Due to their small size, PM2.5 can penetrate into respiratory systems and cause or exacerbate serious illness. The US Environmental Protection Agency (EPA) regulates the levels of surface PM2.5 but surface monitoring has spatial and temporal limitations. The Aerosol Optical Depth (AOD) retrievals from the Geostationary Operational Environmental Satellite (GOES) missions and meteorological factors can be utilized as an alternative technique to estimate surface PM2.5 levels at a higher spatial and temporal resolution compared to surface monitors. Traditional estimation approaches rely on linear regression techniques and have limitations modeling the nonlinear relationship between the meteorological factors, AOD retrievals, and surface PM2.5. We compare different machine learning techniques and identify the best-suited model that can represent the nonlinearity between the factors affecting PM2.5 levels

Manisha Khatri↗