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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

A Tool For Identifying Key Gravity-Assist Trajectories From Broad Search Results

A tool is presented that identifies desirable trajectory candidates from among tens of thousands of gravity-assist trajectories. A broad trajectory search technique creates an exhaustive set of possible trajectories to a given planet. From this dataset, our tool reveals candidate trajectories with user-defined characteristics. Typical discriminating characteristics are launch V-infinity, time-of-flight, and delivered mass. Mission planners evaluate and plot interesting trajectories from within the tool. Our tool generates catalogs of selected trajectories for further evaluation with higher-fidelity trajectory solvers. This paper outlines the key features of the tool and gives examples of typical analyses.

uranus, interplanetary↗
NASA NTRSDate not supplied

Establishing Electrified Aircraft Propulsion Concepts—How AATT identified viable propulsion concepts and established foundational technologies

In the mid 2000’s NASA challenged the aeronautics industry to identify the routes for achieving ambitious improvements in fuel burn, emission, and noise reductions. These so-called “N+3” studies were exploring broad changes three commercial aircraft generations, or nominally 30 years, in the future. Many intriguing propulsion-airframe integrated solutions were proposed and pursued, which incorporated technologies such as high-aspect-ratio wings, boundary-layer ingestion, and hybrid-electric powertrains. When the Advanced Air Transport Technology (AATT) Project introduced “Technical Challenge 5.2-Establish viable concept for 5-10 MW hybrid gas-electric propulsion system for a commercial transport aircraft” in 2014, industry was very skeptical that electrified propulsion could make a significant impact in commercial air transport over the subsequent 30 years. Yet there were many practical reasons why improvements in electric power system and electric powertrains were advancing at a rapid rate and could lead to paradigm changes in aircraft propulsion. The challenge to the aeronautics community was to discover how to harness this power revolution and apply it to aircraft propulsion. This talk summarizes the approaches and achievements from the Hybrid Gas-Electric Propulsion Concept Technical Challenge that concluded in 2019. The balanced portfolio of concepts studies anchored with practical technology development demonstrated that electrified aircraft propulsion is an aircraft revolution whose time has come.

electric propulsion↗
NASA NTRS2020

Multiple Transits during a Single Conjunction: Identifying Transiting Circumbinary Planetary Candidates from TESS

We present results of a study on identifying circumbinary planet candidates that produce multiple transits during one conjunction with eclipsing binary systems. The occurrence of these transits enables us to estimate the candidates’ orbital periods, which is crucial as the periods of the currently known transiting circumbinary planets are significantly longer than the typical observational baseline of the Transiting Exoplanet Survey Satellite (TESS). Combined with the derived radii, it also provides valuable information needed for follow-up observations and subsequent confirmation of a large number of circumbinary planet candidates from TESS. Motivated by the discovery of the 1108 day circumbinary planet Kepler-1647, we show the application of this technique to four of Kepler’s circumbinary planets that produce such transits. Our results indicate that in systems where the circumbinary planet is on a low-eccentricity orbit, the estimated planetary orbital period is within <10%–20% of the true value. This estimate is derived from photometric observations spanning less than 5% of the planet’s period, demonstrating the strong capability of the technique. Capitalizing on the current and future eclipsing binaries monitored by NASA’s TESS mission, we estimate that hundreds of circumbinary planet candidates producing multiple transits during one conjunction will be detected in the TESS data. Such a large sample will enable statistical understanding of the population of planets orbiting binary stars and shed new light on their formation and evolution.

Veselin Kostov↗
NASA NTRS2021

San Diego Urban Development: Utilizing NASA Earth Observations to Identify Drivers of Extreme Urban Heat and Generate a High-Resolution Vulnerability Index for Urban Planning and Climate Resiliency in San Diego, California

Exposure to heat exacerbated by an increase in urbanization as well as increasing global temperatures has become a growing concern for cities and their residents. Excess heat can cause increased heat-related morbidity, mortality, and energy costs. Vulnerability to heat-related illnesses is oftentimes correlated to demographics, socioeconomic status, and pre-existing health conditions. The City of San Diego, California boasts 1.4 million residents and, like many other major cities, has experienced increases in heat-related hospitalizations and mortality. The burden of urban heat is also not equal amongst communities; areas with lower income and communities of color bear a disproportionate burden. In partnership with the City of San Diego, and American Geophysical Union’s (AGU) Thriving Earth Exchange, the DEVELOP team used Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) imagery to identify areas of highest heat based on land surface temperature from 2015-2020. Our analyses showed that health demographics such as obesity and cardiovascular health were the strongest indicators for heat vulnerability. In addition, various inputs (land use/land cover, tree canopy, and building intensity derived from the City of San Diego data along with albedo from Landsat 8) were used in the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) urban cooling model to investigate changes in cooling rates in current and future scenarios for the city. The model results showed that cooling is expected to occur due to a 5% increase in tree canopy. The City of San Diego can use these results to inform the development of the Climate Resilient San Diego plan and prioritize at-risk communities for cooling interventions.

John Dialesandro↗
NASA NTRS2021

Developing a Set of Indicators to Identify, Monitor, and Track Impacts and Change in Forests of the United States

United States forestland is an important ecosystem type, land cover, land use, and economic resource that is facing several drivers of change including climatic. Because of its significance, forestland was identified through the National Climate Assessment(NCA) as a key sector and system of concern to be included in a system of climate indicators as part of a sustained assessment effort. Here, we describe 11 informative core indicators of forests and climate change impacts with metrics available or nearly available for use in the NCA efforts. The recommended indicators are based on a comprehensive conceptual model which recognizes forests as a land use, an ecosystem, and an economic sector. The indicators cover major forest attributes such as extent, structural components such as biomass, functions such as growth and productivity, and ecosystem services such as biodiversity and outdoor recreation. Interactions between humans and forests are represented through indicators focused on the wildland-urban interface, cost to mitigate wildfire risk, and energy produced from forest-based biomass. Selected indicators also include drought and disturbance from both wildfires and biotic agents. The forest indicators presented are an initial set that will need further refinement in coordination with other NCA indicator teams. Our effort ideally will initiate the collection of critical measurements and observations and lead to additional research on forest-climate indicators.

U.S. forests↗
NASA NTRS2021

Identifying Molecules as Biosignatures with Assembly Theory and Mass Spectrometry

The search for evidence of life elsewhere in the universe is hard because it is not obvious what signatures are unique to life. Here we postulate that complex molecules found in high abundance are universal biosignatures as they cannot form by chance. To explore this, we developed the first intrinsic measure of molecular complexity that can be experimentally determined, and this is based upon a new approach called assembly theory which gives the molecular assembly number (MA) of a given molecule. MA allows us to compare the intrinsic complexity of molecules using the minimum number of steps required to construct the molecular graph starting from basic objects, and a probabilistic model shows how the probability of any given molecule forming randomly drops dramatically as its MA increases. To map chemical space, we calculated the MA of ca. 2.5 million compounds, and collected data which showed the complexity of a molecule can be experimentally determined by using three independent techniques including infra-red spectroscopy, nuclear magnetic resonance, and by fragmentation in a mass spectrometer, and this data has an excellent corelation with the values predicted from our assembly theory. We then set out to see if this approach could allow us to identify molecular biosignatures with a set of diverse samples from around the world, outer space, and the laboratory including prebiotic soups. The results show that there is a non-living to living threshold in MA complexity and the higher the MA for a given molecule, the more likely that it had to be produced by a biological process. This work demonstrates it is possible to use this approach to build a life detection instrument that could be deployed on missions to extra-terrestrial locations to detect biosignatures, map the extent of life on Earth, and be used as a molecular complexity scale to quantify the constraints needed to direct prebiotically plausible processes in the laboratory. Such an approach is vital if we are going to find new life elsewhere in the universe or create de-novo life in the lab.

Stuart M Marshall↗
NASA NTRS2021

Identifying molecules as biosignatures with assembly theory and mass spectrometry

The search for alien life is hard because we do not know what signatures are unique to life. We show why complex molecules found in high abundance are universal biosignatures and demonstrate the first intrinsic experimentally tractable measure of molecular complexity, called the molecular assembly index (MA). To do this we calculate the complexity of several million molecules and validate that their complexity can be experimentally determined by mass spectrometry. This approach allows us to identify molecular biosignatures from a set of diverse samples from around the world, outer space, and the laboratory, demonstrating it is possible to build a life detection experiment based on MA that could be deployed to extraterrestrial locations, and used as a complexity scale to quantify constraints needed to direct prebiotically plausible processes in the laboratory. Such an approach is vital for finding life elsewhere in the universe or creating de-novo life in the lab.

Stuart M. Marshall↗
NASA NTRS2021

Improving the CERES SYN Cloud and Flux Products by Identifying GOES-17 Scan Anomalies Using a Convolutional Neural Network

The NASA Clouds and the Earth’s Radiant Energy System (CERES) project relies on top-of-atmosphere (TOA) broadband fluxes derived from geostationary (GEO) satellite imagery to account for the diurnal flux variations between the CERES observation intervals, and thereby produce a synoptic gridded (SYN1deg) product based on continuous temporal observations. Consistent broadband flux derivation depends on accurate radiative property measurements and cloud retrievals, which largely determine the radiance-to-flux conversion process. Therefore, it is important to ensure a high quality of cloud property input in order to maintain a reliable broadband flux record. In Edition 4 of the CERES SYN1deg product, a robust automated image anomaly detection algorithm based on inter-line and inter-pixel differences, spatial variance, and 2-D Fourier analysis has been successful in identifying imagery with linear artifacts, but the line-by-line inspection and cleaning process must still be performed by a human. Therefore, further automation of this quality assurance process is warranted, especially considering the excessive amount of additional cleaning necessitated by the GOES-17 Advance Baseline Imager (ABI) cooling system anomaly. As such, this article highlights advancement of the CERES GEO image artifact cleaning approach based on a convolutional neural network (CNN) for classification of bad scanlines. Once trained, the CNN approach is a computationally inexpensive means to ensure greater consistency in cloud retrievals, and therefore broadband flux derivation, based on GOES-17 measurements.

Benjamin Scarino↗
NASA NTRS2021

Delaware Ecological Forecasting: Assessing Land Cover and Soil to Identify Suitable Sites for Tidal Marsh Migration in Delaware

Tidal wetlands provide vital resources for the state of Delaware, crucial not only for maintaining important ecosystem functions, but also for providing human populations with substantial services. Healthy wetland networks offer protection from severe weather, reduce flooding, improve water quality, and provide opportunities for education and recreation. However, human activities in combination with natural events, continue to cause substantial loss of wetland cover and damage wetland health. Over the last thirty years, the state of Delaware has experienced a net loss of roughly 5,000 acres of wetland. In collaboration with the Delaware Department of Natural Resources and Environmental Control (DNREC), the team used NASA Earth observations including Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and Global Precipitation Measurement Integrated Multi-Satellite Retrievals (GPM IMERG) to develop a methodology to monitor recent changes in wetland cover and forecast landward marsh migration due to sea-level rise, changes to climate, and human development. Trend analysis of current and past climate conditions in precipitation and temperature revealed an overall increase in both metrics. Using Land Change Modeler in TerrSet and Suitability Modeler in ArcGIS Pro, the team visualized landcover shifts over the last 20 years, indicating a general pattern of net wetland loss and identified locations where marsh migration could potentially occur in the future. These observations will enable better planning for restoration activities and in form decision-making to preserve wetland health and ecosystem functions.

McKenna Brahler↗
NASA NTRSDate not supplied

The Application of Remote Sensing using NASA Earth Observations paired with Sociodemographic Indicators to Identify Communities Most Susceptible to Urban Heat Exposure in Austin, Texas

In recent years, Austin, Texas has experienced an increase in population and urban development. Additionally, the City’s climate—already characterized by periods of extreme heat and drought—continues to change. As temperatures and demand for utilities and cooling resources rise, the number of heat-related deaths and illnesses in socially vulnerable populations (e.g., older, lower-income populations) is expected to increase. The City of Austin, The University of Texas at Austin (UT Austin), and The University of Texas Health Science Center at Houston (UT Health) partnered with NASA DEVELOP to examine the distribution of urban heat throughout the City. This project used land surface temperature, greenness, plant water content, and urban surface material analysis parameters derived from NASA Earth observations from Landsat 8 Operational Land Imager (OLI), Landsat 8 Thermal Infrared Sensor (TIRS), and Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS). The project team produced three different indices to create a spatial analysis for the study area including a social vulnerability index (SoVI), heat exposure index (HEI), and an overall heat priority index (HPI) score. This overall score was determined with a weighted analysis of heat-related environmental variables from NASA Earth observations and socioeconomic data from the 2019 American Community Survey. Based on the HPI score, the project team identified 121 census block groups out of 605 total that are designated as being most at risk of adverse impacts from extreme heat events. To test the sensitivity of the HPI, the team used a Monte Carlo analysis using different approaches for geographic scale, variable inclusion, census uncertainty, and index aggregation. Based on the sensitivity analysis, the resulting HPI score showed the metric was consistent with the baseline HPI. This provided increased confidence the score can be used as a tool to make informed infrastructure improvement plans in targeted areas (e.g., siting of cooling centers) and ensure equitable sustainable development.

Ryan Hammock↗
NASA NTRSDate not supplied

Using Earth Observations to Analyze Vegetation Phenology and Climatology in Bhutan to Identify Forest Disturbance

Changes in climate in the Himalayan region cause variability in temperature, precipitation, and phenology. It can also impact the health of coniferous forest ecosystems including increased damage due to aggressive forest pests. Forest disturbance from bark beetle is a major concern in Bhutan, sometimes causing extensive tree mortality to pine and spruce forests. Climatological trends and changes in vegetation phenology were analyzed and incorporated into a tool in Google Earth Engine that identified patches of forest disturbance in Bhutan. Preprocessed phenology and meteorological data from the Advanced Very High-Resolution Radiometer (AVHRR) and Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), along with Climate Hazards Center Infrared Precipitation with Station (CHIRPS), Famine Early Warning System Network Land Data Assimilation System (FLDAS), Sentinel-2 Multispectral Instrument (MSI), and Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM)+, and Landsat 8 Operational Land Imager (OLI) were used within the tool. Changes in temperature, precipitation, and phenology were analyzed throughout Bhutan, and forest disturbance caused by bark beetle was investigated in two districts of the country

Tashi Choden↗
NASA NTRSDate not supplied

Identifying Common Use Cases across Extensible Traffic Management (xTM) for Interactions with Air Traffic Controllers

NASA’s Extensible Traffic Management (xTM) builds on the foundation and the architecture of Unmanned Aircraft Systems (UAS) Traffic Management (UTM) concept and extends it broadly to other domains, such as Advanced / Urban Air Mobility (AAM/UAM) and Upper Class E Traffic Management (ETM). These xTM concepts assume the ability to fly in airspace that is authorized to operate solely under xTM services and mostly without any air traffic control (ATC) support. However, they also assume circumstances in which the xTM vehicles would need to operate in conventional ATC-managed airspace, both during nominal and off-nominal scenarios. Due to the vast differences in the xTM vehicle performances and missions, there is a concern that ATC may have difficulty in safely managing the xTM traffic and providing appropriate services to all vehicles, unless a consistent set of roles, procedures, and data exchange requirements are defined across the diverse set of xTM vehicle operations. In this paper, we describe a set of use cases that have been identified in UTM, AAM/UAM, and ETM operations that are related to ATC interactions, and we propose to categorize these use cases across xTM domains based on common trigger events. Organizing the use cases from the perspective of ATC roles per each trigger event is expected to provide the first step in discovering common procedures and data requirements across xTM domains that could help ease the controllers’ cognitive task load and allow them to manage these interactions more safely.

Extensible Traffic Management (xTM)↗
NASA NTRS2021

Southeast Michigan Health & Air Quality: Identifying Trends of Ground-level Ozone Precursors in Southeast Michigan and Northern Ohio

Pollutants resulting from industrial activity can react with sunlight to create ground-level ozone, a harmful pollutant that can exacerbate respiratory health issues such as asthma. Due to a history of heavy industrialization, residents of southeast Michigan and northern Ohio are especially susceptible to ground-level ozone. NASA DEVELOP, in partnership with the Michigan Department of Environment, Great Lakes, and Energy’s (EGLE) Air Quality Division and the Lake Michigan Air Directors Consortium (LADCO), investigated the effectiveness of Earth observations (EO) in monitoring pollutants that contribute to ground-level ozone. The team used the European Space Agency’s TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5P, and NASA’s Ozone Monitoring Instrument (OMI) aboard Aura, to measure nitrogen dioxide (NO2), formaldehyde (HCHO), and methane (CH4) from 2019 to 2021 (May-September). The team oversampled the EO data on monthly, yearly, and 3-year time scales where possible to enhance more localized pollutant trends. Specifically, TROPOMI effectively monitored NO2 from space, indicating sub-city distributions on a monthly timescale. Contrastingly, the measurements of HCHO were predominately noisy, and therefore failed to show any distribution trends. Lastly, CH4 trends were identifiable yet coarse, hinting that EO monitoring in the case of CH4 is not beneficial for our partners. The end products provide the partners with insight on the utility of EO in measuring certain ozone precursors and can be used to guide ground-level ozone reduction strategies throughout the region.

Mariam Moeen↗
NASA NTRS2022

Non-Protein Amino Acids Identified in Carbon-Rich Hayabusa Particles

Amino acid abundances in acid-hydrolyzed hot water extracts of gold foils containing five Category 3 (carbon-rich) Hayabusa particles were studied using liquid chromatography with tandem fluorescence and accurate mass detection. Initial particle analyses using field emission scanning electron microscopy with energy-dispersive X-ray spectrometry indicated that the particles were composed mainly of carbon. Prior to amino acid analysis, infrared and Raman microspectroscopy showed some grains possessed primitive organic carbon. Although trace terrestrial contamination, namely L-protein amino acids, was observed in all Hayabusa extracts, several terrestrially uncommon non-protein amino acids were also identified. Some Hayabusa particles contained racemic (D≈L) mixtures of the non-protein amino acids β-aminoisobutyric acid (β-AIB) and β-amino-n-butyric acid (β-ABA) at low abundances ranging from 0.09 to 0.31 nmol g −1 . Larger abundances of the non-protein amino acid β-alanine (9.2 nmol g −1 , ≈4.5 times greater than background levels) were measured in an extract of three Hayabusa particles. This β-alanine abundance was ≈6 times higher than that measured in an extract of a CM2 Murchison grain processed in parallel. The comparatively high β-alanine abundance is surprising as asteroid Itokawa is similar to amino acid-poor LL ordinary chondrites. Elevated β-alanine abundances and racemic β-AIB and β-ABA in Hayabusa grains suggested these compounds have non-biological and plausibly non-terrestrial origins. These results are the first evidence of plausibly extraterrestrial amino acids in asteroid material from a sample-return mission and demonstrate the capabilities of the analytical protocols used to study asteroid Ryugu and Bennu samples returned by the JAXA Hayabusa2 and NASA OSIRIS-REx missions, respectively.

Eric T. Parker↗
NASA NTRS2022

Identifying the Wide Diversity of Extraterrestrial Purine and Pyrimidine Nucleobases in Carbonaceous Meteorites

The lack of pyrimidine diversity in meteorites remains a mystery since prebiotic chemical models and laboratory experiments have predicted that these compounds can also be produced from chemical precursors found in meteorites. Here we report the detection of nucleobases in three carbonaceous meteorites using state-of-the-art analytical techniques optimized for small-scale quantification of nucleobases down to the range of parts per trillion (ppt). In addition to previously detected purine nucleobases in meteorites such as guanine and adenine, we identify various pyrimidine nucleobases such as cytosine, uracil, and thymine, and their structural isomers such as isocytosine, imidazole-4-carboxylic acid, and 6-methyluracil, respectively. Given the similarity in the molecular distribution of pyrimidines in meteorites and those in photon-processed interstellar ice analogues, some of these derivatives could have been generated by photochemical reactions prevailing in the interstellar medium and later incorporated into asteroids during solar system formation. This study demonstrates that a diversity of meteoritic nucleobases could serve as building blocks of DNA and RNA on the early Earth.

Yasuhiro Oba↗
NASA NTRS2022

Maine Ecological Forecasting II: Identifying Forest Cover and Assessing Federally Endangered Atlantic Salmon Habitat in Maine Using Earth Observations

Major declines in Atlantic salmon (Salmo salar) populations have occurred alongside dam construction, shifting temperatures, and changing land use and land cover (LULC), restricting the last remaining wild population in the United States to Maine. In collaboration with the Maine Department of Marine Resources and the Downeast Salmon Federation, the NASA DEVELOP team utilized Earth observations to assess changes within critical salmon habitat. The team used the Landsat 5 Thematic Mapper (TM), Landsat 8 Operation Land Imager (OLI), Sentinel-2 MultiSpectral Instrument (MSI), and National Land Cover Database (NLCD) to refine historical LULC maps from 1985 to 2021. The team also used elevation data from the Shuttle Radar Topography Mission (SRTM) and IDRISI TerrSet Land Change Modeler to forecast LULC change to 2040. From Terra Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data, the team derived a time series, summer averages, and anomaly maps between 2000 and 2021. The team analyzed LST in relation to LULC classes, specifically forest cover type, and in-situ stream temperature from the Spatial Hydro-Ecological Decision System (EcoSHEDS). LULC trends from 1985 to 2021 revealed a net transition of coniferous forest to other classes, such as deciduous forest and developed land. The team found an association between warmer temperatures and a greater presence of developed and mixed forest classes per 10,000 acres across Maine. These visualizations and analyses will aid partners in identifying riparian locations with ideal or unfavorable habitat conditions to inform Atlantic salmon population recovery efforts.

Tony Bowman↗
NASA NTRS2022

Using Trajectory Smoothness Metrics to Identify Drones in Radar Track Data

The identification of unmanned aircraft systems (UAS) using trajectory data is considered. Specifically, a number of smoothness metrics are proposed, which can be used to distinguish UAS from other aerial objects even when they are engaged in accelerative maneuvers (non-constant-velocity flight). The metrics are evaluated on a data set from a UAS sense-and-avoid field test, which contains track data of aerial objects recorded by a vehicle-board radar system during a flight test. The metrics are found to effectively differentiate UAS from other objects such as birds for this data set. In addition, an initial statistical performance analysis of one of the smoothness metrics is undertaken, using 15 data sets deriving from multiple flight tests. The smoothness metric is shown to identify the target UAS with 95% accuracy (95% true positive rate), while achieving a false positive rate of less than 9%.

Sandip Roy↗
NASA NTRS2022

Identifying Shocked Feldspar on Mars Using Perseverance Spectroscopic Instruments: Implications for Geochronology Studies on Returned Samples

The Perseverance rover (Mars 2020) mission, the first step in NASA’s Mars Sample Return (MSR) program, will select samples for caching based on their potential to improve understanding Mars’ astrobiological, geological, geochemical, and climatic evolution. Geochronologic analyses will be among the key measurements planned for returned samples. Assessing a sample’s shock history will be critical because shock metamorphism could influence apparent sample age. Shock effects in one Mars-relevant mineral class, plagioclase feldspar, have been well- documented using various spectroscopy techniques (thermal infrared reflectance, emission, and transmission spectroscopy, Raman, and luminescence). A subset of these data will be obtained with the SuperCam and SHERLOC (Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals) instruments onboard Perseverance to inform caching decisions for MSR. Here, we review shock indicators in plagioclase feldspar as revealed in Raman, luminescence, and IR spectroscopy lab data, with an emphasis on Raman spectroscopy. We consider how this information may inform caching decisions for selecting optimal samples for geochronology measurements. We then identify challenges and make recommendations for both in situ measurements performed with SuperCam and SHERLOC and for supporting lab studies to enhance the success of geochronologic analyses after return to Earth.

mars↗