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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Using TRMM Field Campaign Data for Assessing GEOS Forecast and Assimilation Products

The wealth of in-situ measurements gathered during Tropical Rain Measuring Mission (TRMM) field campaigns over a wide range of tropical conditions constitute an important data source for evaluating the quality of global model forecasts and assimilated datasets. In this study we use selected observations of cloud microphysics and atmospheric sounding from TEFLUN-1998, SCEMEX-1998, and TRMMLBA-1999 to examine the assimilation and forecast fields produced by the operational GEOS-3 (Goddard Earth Observing System - version 3) global data assimilation system (DAS) and a new finite-volume DAS under development at the Data Assimilation Office. Additionally, TRMM field campaign measurements are used to verify the impact of assimilating rainfall and moisture data derived from TRMM Microwave Imager and Special Sensor Microwave/Imager instruments on the GEOS analysis. We will also explore issues concerning the 'error of representativeness' in using in-situ observations of quantities with large spatial and temporal variability such as precipitation for validating gridded global data products.

Hou, Arthur Y.↗
NASA NTRS2013

Use of In Situ and Airborne Multiangle Data to Assess MODIS and Landsat-Based Estimates of Directional Reflectance and Albedo

The quantification of uncertainty in satellite-derived global surface albedo products is a critical aspect in producing complete, physically consistent, and decadal land property data records for studying ecosystem change. A challenge in validating albedo measurements acquired from space is the ability to overcome the spatial scaling errors that can produce disagreements between satellite and field-measured values. Here, we present the results from an accuracy assessment of MODIS and Landsat-TM albedo retrievals, based on collocated comparisons with tower and airborne Cloud Absorption Radiometer (CAR) measurements collected during the 2007 Cloud and Land Surface Interaction Campaign (CLASIC). The initial focus was on evaluating intersensor consistency through comparisons of intrinsic bidirectional reflectance estimates. Local and regional assessments were then performed to obtain estimates of the resulting scaling uncertainties, and to establish the accuracy of albedo reconstructions during extended periods of precipitation. In general, the satellite derived estimates met the accuracy requirements established for the high-quality MODIS operational albedos at 500 m (the greater of 0.02 units or +/-10% of surface measured values). However, results reveal a high degree of variability in the root-mean-square error (RMSE) and bias of MODIS visible (0.3-0.7 μm) and Landsat-TM shortwave (0.3-5.0 μm) albedos; where, in some cases, retrieval uncertainties were found to be in excess of 15%. Results suggest that an overall improvement in MODIS shortwave albedo retrieval accuracy of 7.8%, based on comparisons between MODIS and CAR albedos, resulted from the removal of sub-grid scale mismatch errors when directly scaling-up the tower measurements to the MODIS satellite footprint.

biosphere↗
NASA NTRS1989

Rain attenuation measurements: Variability and data quality assessment

Year to year variations in the cumulative distributions of rain rate or rain attenuation are evident in any of the published measurements for a single propagation path that span a period of several years of observation. These variations must be described by models for the prediction of rain attenuation statistics. Now that a large measurement data base has been assembled by the International Radio Consultative Committee, the information needed to assess variability is available. On the basis of 252 sample cumulative distribution functions for the occurrence of attenuation by rain, the expected year to year variation in attenuation at a fixed probability level in the 0.1 to 0.001 percent of a year range is estimated to be 27 percent. The expected deviation from an attenuation model prediction for a single year of observations is estimated to exceed 33 percent when any of the available global rain climate model are employed to estimate the rain rate statistics. The probability distribution for the variation in attenuation or rain rate at a fixed fraction of a year is lognormal. The lognormal behavior of the variate was used to compile the statistics for variability.

Crane, Robert K.↗
NASA NTRS1978

Use of LANDSAT data to assess waterfowl habitat quality

The author has identified the following significant results. The capability of mapping ponds over a very large area was demonstrated, with multidate, multiframe LANDSAT imagery. A small double sample of aircraft data made it possible to adjust a LANDSAT large area census. Terrain classification was improved by using multitemporal LANDSAT data. Waterfowl production was estimated, using remotely determined pond data, in conjunction with FWS estimates of breeding population. Relative waterfowl habitat quality was characterized on a section by section basis.

Colwell, J. E.↗
NASA NTRS1981

Evaluation of HCMM data for assessing soil moisture and water table depth

Soil moisture in the 0-cm to 4-cm layer could be estimated with 1-mm soil temperatures throughout the growing season of a rainfed barley crop in eastern South Dakota. Empirical equations were developed to reduce the effect of canopy cover when radiometrically estimating the soil temperature. Corrective equations were applied to an aircraft simulation of HCMM data for a diversity of crop types and land cover conditions to estimate the soil moisture. The average difference between observed and measured soil moisture was 1.6% of field capacity. Shallow alluvial aquifers were located with HCMM predawn data. After correcting the data for vegetation differences, equations were developed for predicting water table depths within the aquifer. A finite difference code simulating soil moisture and soil temperature shows that soils with different moisture profiles differed in soil temperatures in a well defined functional manner. A significant surface thermal anomaly was found to be associated with shallow water tables.

Moore, D. G.↗
NASA NTRSDate not supplied

Artemis I Flight Instrumentation Data Quality Assessment and Processing

This paper is in support of the SciTech 2024 Space Launch System Aerosciences Special Sessions being organized by Brent Pomeroy and Jeremy Pinier. On November 16th, 2022, NASA launched an inaugural test flight of the Space Launch System (SLS) carrying the Orion capsule around the moon. The vehicle was instrumented with thousands of Developmental Flight Instrumentation (DFI) sensors, which monitored aerodynamic, acoustic, structural, and thermal environments. These data are used to validate experimental and numerical tools used to predict conditions the vehicle experiences during ascent. Prior to launch, a set of tests were performed to quantify the expected quality of the DFI flight data. These included testing of individual components, such as data acquisition units, at the Systems Integration Laboratory (SIL) and testing of integrated vehicle components at the Vehicle Assembly Building (VAB). This paper summarizes these tests. Some aspects of post processing of data are also discussed.

Developmental Flight Instrumentation↗
NASA NTRS2020

Synergistic Use of SMAP and OCO-2 Data in Assessing the Responses of Ecosystem Productivity to the 2018 U.S. Drought

Soil moisture and gross primary productivity (GPP) estimates from the Soil Moisture Active Passive (SMAP) and solar-induced chlorophyll fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) provide new opportunities for understanding the relationship between soil moisture and terrestrial photosynthesis over large regions. Here we explored the potential of the synergistic use of SMAP and OCO-2 based data for monitoring the responses of ecosystem productivity to drought. We used complementary observational information on root-zone soil moisture and GPP (9 km) from SMAP and fine-resolution SIF (0.05°; GOSIF) derived from OCO-2 SIF soundings. We compared the spatial pattern and temporal evolution of anomalies of these variables over the conterminous U.S. during the 2018 drought, and examined to what extent they could characterize the drought-induced variations of flux tower GPP and crop yield data. Our results showed that SMAP GPP and GOSIF could well capture the spatial extent and dynamics of drought indicated by the U.S. Drought Monitor maps and the SMAP root-zone soil moisture deficit. Over the U.S. Southwest, monthly anomalies of soil moisture showed significant positive correlations with those of SMAP GPP (R2=0.44, p<0.001) and GOSIF (R2=0.76, p<0.001), demonstrating strong water availability constraints on plant productivity across dryland ecosystems. We further found that SMAP GPP and GOSIF captured the impact of drought on tower GPP and crop yield. Our results suggest that synergistic use of SMAP and OCO-2 data products can reveal the drought evolution and its impact on ecosystem productivity and carbon uptake at multiple spatial and temporal scales, and demonstrate the value of SMAP and OCO-2 for studying ecosystem function, carbon cycling, and climate change.

Soil Moisture Active Passive↗
DOE OSTI2025

Using satellite data to assess Hurricane Helene’s impact on vegetation by land use in the CSRA

Many regions within Georgia, South Carolina, and North Carolina experienced record-breaking rainfall and catastrophic winds due to Hurricane Helene. Helene made landfall on Florida’s big bend on September 26 th , 2024, as a category 4 hurricane, and tracked northward through Georgia and the southern Appalachian Mountains before dissipating on September 29 th , 2024. One significant impact of the hurricane was severe damage to tree canopies across the southeastern United States. This study utilizes satellite-based remotely sensed data provided by the National Aeronautical and Space Administration (NASA) to examine the resilience of these tree canopies following the hurricane. Specifically, the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Cover Type, Soil Moisture Active Passive (SMAP), and the Global Precipitation Measurement (GPM) mission datasets were employed to study the tree canopy’s response to such events in the Central Savannah River Area (CSRA). A significant increase was observed in the LAI in November of 2024. This increase in LAI was likely influenced by warmer-than-average temperatures throughout October of 2024, along with a second record-breaking rainfall event in the CSRA on November 6th, 2024. The LAI increase was then broken down into land cover type to understand which areas contributed most. The complex nature and resilience of trees became evident, offering opportunities for further exploration and application in urban planning, emergency response, and environmental management.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

Using Satellite Remote Sensing and Household Survey Data to Assess Human Health and Nutrition Response to Environmental Change

Climate change and degradation of ecosystem services functioning may threaten the ability of current agricultural systems to keep up with demand for adequate and inexpensive food and for clean water, waste disposal and other broader ecosystem services. Human health is likely to be affected by changes occurring across multiple geographic and time scales. Impacts range from increasing transmissibility and the range of vector-borne diseases, such as malaria and yellow fever, to undermining nutrition through deleterious impacts on food production and concomitant increases in food prices. This paper uses case studies to describe methods that make use of satellite remote sensing and Demographic and Health Survey data to better understand individual-level human health and nutrition outcomes. By bringing these diverse datasets together, the connection between environmental change and human health outcomes can be described through new research and analysis.

Health↗
NASA NTRSDate not supplied

Leveraging High Resolution Remotely Sensed Evapotranspiration Data to Assess Water Availability and Vulnerability in the Memphis Aquifer Area in West Tennessee

The Memphis aquifer is an indispensable source of water for municipal, agricultural, and industrial use across west Tennessee. The aquifer's water is thousands of years old and in pristine condition due to the natural geological filtering by the aquifer’s confining clay layers. To sustain the aquifer’s water supply, recharge must happen at a rate efficient enough to keep up with pumping from the aquifer. In partnership with Protect Our Aquifer and the University of Memphis Center for Applied Earth Science and Engineering Research, this project examined multiple factors to discover thriving areas of recharge in west Tennessee. The team used Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation data, ECOsystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS) evapotranspiration data, Landsat 8 and National Land Cover Database (NLCD) to track landcover change, and the North American Land Data Assimilation System (NLDAS) Noah land surface model to estimate monthly runoff. These factors were averaged seasonally from 2019 to 2022 to understand seasonal relationships within the study area. Precipitation and evapotranspiration data were used to create water balance maps and with the evaporative stress index (ESI), landcover, and runoff maps, the team pinpointed locations that are most conducive to recharge. The team determined that areas where the water balance is low, ESI is high, and where there is sufficient pervious landcover are locations where recharge happens most effectively. Identification of effective recharge locations may be used by the partners to prioritize areas in need of protection before they become impacted by industrialization and urbanization.

Lauren Webster↗
NASA NTRS2024

Bridgeport Urban Development: Leveraging NASA Earth Observations and Sociodemographic Data to Assess Urban Heat Vulnerability and Inform Cool Corridors in Bridgeport, Connecticut

Urban environments face hotter temperatures than suburban and rural areas due to higher concentrations of impervious surfaces, heat-retaining buildings, and lack of green space. Bridgeport, Connecticut, which was formerly a national manufacturing hub, is now the densest and most populous city in the state. Bridgeport experiences hotter temperatures, exposing its residents to more extreme temperatures than the surrounding affluent suburbs. Extreme heat affects the health of those exposed to it and intensifies energy demands. Understanding temperature differences is the first step in effectively directing mitigation efforts. Our partner, Groundwork Bridgeport, along with the Yale Urban Design Workshop, are planning a “cool corridors” project, implementing cooling infrastructure to combat urban heat. We used Landsat 8 Thermal Infrared Sensor and Landsat 9 Thermal Infrared Sensor-2 data to conduct a Land Surface Temperature analysis in Google Earth Engine for the county of Fairfield. A Principal Component Analysis was performed to identify indicators of social vulnerability in Bridgeport. We used the SOlar and LongWave Environmental Irradiance Geometry model to identify felt heat on the block level to inform where the partner should locate their cooling interventions to ensure they are most effective and equitable. We focused on the East Side of Bridgeport, which we found was 10 degrees hotter than other areas of Bridgeport and the neighboring town of Fairfield. We integrated our findings using Earth observations and additional sociodemographic and climate data into final communication products for our partners which will facilitate their selection of candidate locations for their Cool Corridors project.

Silas Kirsch↗
NASA NTRS2022

Western Tennessee Water Resources: Leveraging High Resolution Remotely Sensed Data to Assess Water Availability and Vulnerability in the Memphis Aquifer Area in West Tennessee

The Memphis Aquifer (MA) is located in the Mississippi Embayment that extends 250,000 square kilometers across eight states. Fayette and Haywood counties in West Tennessee are situated within the recharge zone of the MA and include the forthcoming Ford “mega campus” named Blue Oval City (BOC), which will consist of a vehicle-production facility and battery assembly division. Increased water demand and land cover change resulting from urban development, such as BOC in the MA’s narrow recharge zone, threaten the aquifer’s groundwater storage and recharge rate. Groundwater recharge factors that influence the narrow recharge zone of the MA include precipitation, evapotranspiration, runoff, and land cover type. In partnership with Protect Our Aquifer (POA) and the Center for Applied Earth Science and Engineering Research (CAESAR) at the University of Memphis, the team used data from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM IMERG), and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). The team also used ancillary data from the National Land Cover Database (NLCD) and the North American Land Data Assimilation System (NLDAS) Noah Land Surface Model. These results identified “thriving” recharge locations, which are areas most conducive to aquifer recharge in Fayette County. The partners may use the results to prioritize specific areas in need of protection before they become susceptible to the effects of urbanization and industrialization.

precipitation↗
NASA NTRSDate not supplied

Illuminating Albedo: Using MOSAiC Data to Assess the CERES Cloud Radiative Swath (CRS) Albedo Quantification Process

Increasing surface and lower tropospheric air temperatures as a result of rising greenhouse gases are expected to be most pronounced over the Arctic. Such rapid changes alter the surface climate of the region, and impacts can be observed atmospherically, oceanographically, and biogeophysically. Accurately quantifying the impact of decreasing surface albedo on the surface energy budget with satellite observations alone is complicated by a lack of shortwave radiation during winter and seasonal/spatial heterogeneity of surface type and associated spectral albedo. NASA’s Clouds and the Earth’s Radiant Energy System (CERES) project features the Cloud Radiative Swath (CRS) product, which builds upon the Single Scanner Footprint (SSF) product by using the NASA Langley Fu-Liou radiative transfer model to calculate a robust and high-quality array of surface and atmospheric radiative fluxes on an instantaneous, footprint-level scale. This study aims to use MOSAiC and CRS data to illuminate potential uncertainties in the CERES albedo production process, with goals of determining 1) spectral albedo under clear sky conditions when stratified by ice concentration, 2) the uncertainty associated with CERES surface albedo “history maps” when compared against observations captured during MOSAiC, and 3) the magnitude of variation between meteorological inputs compared to those from MOSAiC.

Emily Monroe↗
NASA NTRS1990

Distributed systems status and control

Concepts are investigated for an automated status and control system for a distributed processing environment. System characteristics, data requirements for health assessment, data acquisition methods, system diagnosis methods and control methods were investigated in an attempt to determine the high-level requirements for a system which can be used to assess the health of a distributed processing system and implement control procedures to maintain an accepted level of health for the system. A potential concept for automated status and control includes the use of expert system techniques to assess the health of the system, detect and diagnose faults, and initiate or recommend actions to correct the faults. Therefore, this research included the investigation of methods by which expert systems were developed for real-time environments and distributed systems. The focus is on the features required by real-time expert systems and the tools available to develop real-time expert systems.

Kreidler, David↗
NASA NTRS2014

Data Mining of Historical Human Data to Assess the Risk of Injury due to Dynamic Loads

The NASA Occupant Protection Group is charged with ensuring crewmembers are protected during all dynamic phases of spaceflight. Previous work with outside experts has led to the development of a definition of acceptable risk (DAR) for space capsule vehicles. The DAR defines allowable probability rates for various categories of injuries. An important question is how to validate these probabilities for a given vehicle. One approach is to impact test human volunteers under projected nominal landing loads. The main drawback is the large number of subject tests required to attain a reasonable level of confidence that the injury probability rates would meet those outlined in the DAR. An alternative is to mine existing databases containing human responses to impact. Testing an anthropomorphic test device (ATD) at the same human‐exposure levels could yield a range of ATD responses that would meet DAR. As one aspect of future vehicle validation, the ATD could be tested in the vehicle's seat and suit configuration at nominal landing loads and compared with the ATD responses supported by the human data set. This approach could reduce the number of human‐volunteer tests NASA would need to conduct to validate that a vehicle meets occupant protection standards. METHODS: The U.S. Air Force has recorded hundreds of human responses to frontal, lateral, and spinal impacts at many acceleration levels and pulse durations. All of this data are stored on the Collaborative Biomechanics Data Network (CBDN), which is maintained by the Wright Patterson Air Force Base (WPAFB). The test device for human occupant restraint (THOR) ATD was impact tested on WPAFB's horizontal impulse accelerator (HIA) matching human‐volunteer exposures on the HIA to 5 frontal and 3 spinal loading conditions. No human injuries occurred as a result of these impact conditions. Peak THOR response variables for neck axial tension and compression, and thoracic‐spine axial compression were collected. Maximal chest deflection was determined from motion capture video of the impact test. HIC‐ 15 and BRIC were calculated from head acceleration responses. Given the number of human subjects for each test condition a confidence interval of injury probability will be obtained. RESULTS: Results will be discussed in terms of injury‐risk probability estimates based on the human data set evaluated. Also, gaps in the data set will be identified. These gaps could be one of two types. One is areas where additional THOR testing would increase the comparable human data set, thereby improving confidence in the injury probability rate. The other is where additional human testing would assist in obtaining information on other acceleration levels or directions. DISCUSSION: The historical human data showed validity of the THOR ATD for supplemental testing. The historical human data are limited in scope, however. Further data are needed to characterize the effects of sex, age, anthropometry, and deconditioning due to spaceflight on risk of injury

Wells, Jesica↗
NASA NTRS2019

Analysis of Experimental Ice Accretion Data and Assessment of a Thermodynamic Model During Ice Crystal Icing

This paper evaluates a thermodynamic ice crystal icing model that has been previously presented to describe the possible mechanisms of icing within the core of a turbofan jet engine. The model functions between two distinct ice accretions based on a surface energy balance: freeze-dominated icing and melt-dominated icing. Freeze-dominated icing occurs when liquid water (from melted ice crystals) freezes and accretes on a surface along with the existing ice of the impinging water and ice mass. This freeze-dominated icing is characterized as having strong adhesion to the surface. The amount of ice accretion is partially dictated by a freeze fraction, which is the fraction of impinging liquid water that freezes. Melt-dominated icing occurs as unmelted ice on a surface accumulates. This melt-dominated icing is characterized by weakly bonded surface adhesion. The amount of ice accumulation is partially dictated by a melt fraction, which is the fraction of impinging ice crystals that melts. Experimentally observed ice growth rates suggest that only a small fraction of the impinging ice remains on the surface, implying a mass loss mechanism such as splash, runback, bounce, or erosion. The fraction of mass loss must be determined in conjunction with the fraction of freezing liquid water or fraction of melting ice on an icing surface for a given ice growth rate. This mass loss parameter, however, along with the freeze fraction and melt fraction, are the only experimental parameters that are currently not measured directly. Using icing growth rates from ice crystal icing experiments, a methodology that has been previously proposed is used to determine these unknown parameters. This work takes ice accretion data from tests conducted by the National Aeronautics and Space Administration (NASA) at the Glenn Research Center in 2018 that examined the fundamental physics of ice crystal icing. This paper continues evaluation of the thermodynamic model from a previous effort, with additions to the model that account for sub-freezing temperatures that have been observed at the leading edge of the airfoil during icing. The predicted temperatures were generally in good agreement with measured temperatures. Other key findings include the total wet-bulb temperature being a good first order indicator of whether icing is freeze-dominated (sub-freezing values) or melt-dominated (above freezing). Maximum sticking efficiency values, the fraction of impinging mass that adheres to a surface, was calculated to be about 0.2, and retained this maximum value for a range of melt ratios (0.3 to 0.65 and possibly higher), which is defined as the ratio of liquid water content to total water content. Higher air velocities reduced the maximum sticking efficiency and shifted the icing regime to higher melt ratio values. Finally, the leading edge ice accretion angle was found to be related to ice growth (lower growth rates for smaller angles) and melt ratio (smaller melt ratios resulted in smaller angles, likely due to erosion effects).

Ice-Crystal Icing↗
DOE OSTI2025

Critical statistical assessment of data in metal additive manufacturing

Obtaining high quality data reflecting the relationships between the additive manufacturing (AM) process parameters, material microstructure and mechanical properties is crucial for the use of machine learning in AM. A database of over 4,000 data entries of metal AM was created thanks to a large number of literature studies on key process parameters and indicators of build quality. Meta-analysis reveals critical biases in the literature. Firstly, majority of studies report only high quality builds, these imbalances in reporting result in weak correlation between process parameters, properties and consolidation, limiting the ability of machine learning models to generalize beyond optimized conditions. Nevertheless, the trained models accurately predict yield strength ($R^2 = 0.85$), suggesting that certain process–property relationships are effectively captured within these models. Secondly, quantitative microstructural data are largely absent, limiting the learning of the microstructure-mechanical properties relationships. Finally, current process window identification is based largely on the consolidation, despite significant uncertainty in its measurement. It is important to identify the process map on the basis of not only the consolidation, but also mechanical behaviour under loading. Such a identification shows that 316 L and Inconel have much larger process map (i.e. highly printable) in comparison to the AlSi10Mg and Ti6Al4V.

Additive manufacturing↗