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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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NASA NTRS2010

First Earth-Based Detection of a Superbolide on Jupiter

Cosmic collisions can planets cause detectable optical flashes that range from terrestrial shooting stars to bright fireballs. On 2010 June 3 a bolide in Jupiter's atmosphere was simultaneously observed from the Earth by two amateur astronomers observing Jupiter in red and blue wavelengths, The bolide appeared as a flash of 2 s duration in video recording data of the planet. The analysis of the light carve of the observations results in an estimated energy of the impact of (0.9-4,0) x 10(exp 15) J which corresponds to a colliding body of 8-13 m diameter assuming a mean density of 2 g/cu cm. Images acquired a few days later by the Hubble Space Telescope and other large ground-based facilities did not show any signature of aerosol debris, temperature, or chemical composition anomaly, confirming that the body was small and destroyed in Jupiter's upper atmosphere. Several collisions of this size may happen on Jupiter on a yearly basis. A systematic study of the impact rate and size of these bolides can enable an empirical determination. of the flux of meteoroids in Jupiter with implications for the populations of small bodies in the outer solar system and may allow a better quantification of the threat of impacting bodies to Earth. The serendipitous recording of this optical flash opens a new window in the observation of Jupiter with small telescopes.

Hueso, R.↗
NASA NTRS2020

Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland

As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term. The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.

Disasters↗
NASA NTRS2020

Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland

As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission TRMM Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.

Disasters↗
NASA NTRS2011

What Threats to Human Health Does Space Radiation Pose in Orbit

The Space Shuttle program spanned more than the entire length of a solar cycle. Investigations aimed towards understanding the health risks of the astronauts from exposures to space radiation involved mostly physical measurements of the dose and the linear energy transfer (LET) spectrum. Measurement of the dose rate on the Shuttle provided invariable new data for different periods of the solar cycle, whereas measurement of the LET spectrum using the tissue equivalent proportional counter (TEPC) produced the most complete mapping of the radiation environment of the low Earth orbits (LEO). Exposures to the Shuttle astronauts were measured by the personal dosimeter worn by the crewmembers. Analysis of over 300 personal dosimeter readings indicated a dependence on the mission duration, the altitude and inclination of the orbit, and the solar cycle, with the crewmembers on the launch and repair of the Hubble telescope receiving the highest doses due to the altitude of the mission. Secondary neutrons inside the Shuttle were determined by recoil protons or with Bonner spheres, and may contribute significantly to the risks of the crewmembers. In addition, the skin dose and the doses received at different organs were compared using a human phantom onboard a Shuttle mission. A number of radiobiology investigations wer e also performed. The biological doses were determined on six astronauts/cosmonauts on long-duration Shuttle/Mir missions and on two crewmembers on a Hubble repair mission by analyzing the damages in the chromosomes of the crewmembers? white blood cells. Several experiments were also conducted to address the question of possible synergistic effects of spaceflight, microgravity in particular, on the repair of radiation-induced DNA damages. The experimental design included exposure of cells before launch, during flight, or after landing. These physical and biological studies were invaluable in predicting the health risks for astronauts on ISS and future exploration missions. Educational Objectives: A group of high school students flew color negative films on tw o Shuttle missions to detect the radiation environment in orbit. This and other experiments onboard of the Shuttle were aimed at educating the general public of the space program.

Wu, Honglu↗
NASA NTRSDate not supplied

Planetary Protection at Marshall Space Flight Center

Introduction: NASA Marshall Space Flight Center (MSFC) is historically known for its role in propulsion. While this is still the mainstay of MSFC’s expertise, many unique capabilities exist at MSFC which pertain to Planetary Protection (PP), including 1) identifying PP threats, and 2) developing novel methods to neutralize those threats. Furthermore, because these capabilities exist among diverse groups at MSFC, this work promotes collaboration both within and outside MSFC to expand and develop PP studies related to a full spectrum of NASA research, design, manufacture, and test interests. This abstract describes the PP research ongoing at MSFC and describes how it contributes to NASA’s overall PP objectives. Microbial Identification in Cleanrooms: One of the greatest threats to successful implementation of PP requirements is recontamination post bioburden reduction. One method to prevent recontamination is to keep the spacecraft in clean environments (i.e. cleanrooms) as much as possible during assembly and integration. However, cleanrooms are not without their own sources of contamination, which is why NASA is interested in monitoring the cleanliness of cleanrooms and characterizing the microbial species present. Such information allows a greater understanding of the resistance of these microbes to cleaning methods, as well as the risk of their contaminating the targeted planetary body of a given mission. MSFC has multiple cleanrooms of various ISO cleanliness levels onsite. We sampled the air and surfaces of three of these rooms, isolated microbes, and then sequenced the 16S rRNA gene or ITS region of the 18S rRNA gene for bacterial and fungal isolates, respectively. This has resulted in a microbial library which currently includes nearly 100 isolates. Microbial Enumeration of Spacecraft Materials: Currently, there are only a couple bioburden reduction methods approved by NASA, and often the harshness of these methods presents additional concerns or risks related to material properties. The goal of this research is to assess the microbial content of solid rocket motor (SRM) materials potentially used for lander missions. This work aims to more accurately define the risk of planetary contamination by providing empirical data associated with commonly used SRM raw materials. In this study, we pulverized nonmetallic SRM materials using a cryogenic grinder, then analyzed the resulting substrate for microbial colony forming units (CFU). We found that many SRM nonmetallic materials do not harbor detectable bioburden, though a range existed depending on the material. The results from this work provide quantitative data to potentially reduce concerns of contamination, while also providing a foundation for follow up studies into additional sterilization methods and molecular identification of contaminating microbes. Space Environmental Effects on Microbial Survival: One potential area of microbial reduction is the space environment. Understanding the survivability of hardy microbes in space-like conditions is a crucial first step in answering how space may reduce bioburden and if it can be relied upon for adherence to PP requirements. This work studied the effects of ultraviolet (UV) and ionizing radiation on survival of Bacillus atrophaeus spores. Microbes were dried on relevant polymeric materials then exposed to space environmental stressors. Coupons were submerged in water, diluted, and plated to determine survival compared with controls. We found that both UV and ionizing radiation were capable of reducing viability by nearly 99%, but there were still survivors, some with changed morphology indicating resistance mechanisms within certain cells. Manufacturing credit: Finally, given the above-mentioned limitations of the NASA-approved bioburden reduction methods, there is interest in understanding if manufacturing processes may provide enough bioburden reduction without additional PP-specific bakeouts. For instance, some material additives may be antimicrobial. Given this, we investigated the effects of several commonly used rubber additives on the growth of B. atrophaeus spores. We found that some of the materials inhibited growth of the spores, possibly supporting the use of these additives on missions with PP constraints. Future work into manufacturing credit for bioburden reduction includes inoculation of green insulation with B. atrophaeus spores, followed by a typical cure. Thermal profiles will be verified for appropriate temperature and durations to meet PP requirements, and cured samples will be analyzed using a cryogenic grinder to determine survivability of spores.

Chelsi D. Cassilly↗
NASA NTRS2011

Comparing the Earth Impact Flux from Comets and Near-Earth Asteroids

Long-period comets (LPC), defined here as those active comets with orbital periods greater than 200 years, are the most difficult objects to mitigate should one be found on an Earth threatening trajectory. The arrival of these objects from the distant Oort cloud cannot be predicted and the impact warning time would be measured in a few months - not years. At the distance of Jupiter, an inactive cometary nucleus with a diameter of one kilometer and a geometric albedo equal to 0.04 would have an apparent magnitude fainter than 25 near opposition and, hence would be well outside the detection capability of current NEO search telescopes. In general LPCs do not become active, and hence discoverable, until inside the orbit of Jupiter and it takes but nine months for a LPC to travel the distance from Jupiter's orbit to that of the Earth.Estimating the impact energy for long-period comets is particularly difficult because the sizes and masses of these objects are not well known. Their solid nuclei are hidden from ground-based telescopes by their gas and dust atmospheres and even when nearby spacecraft observations can determine a size and shape for a cometary nucleus, there are no direct determinations of any cometary mass or bulk density to date. However, a variety of indirect bulk density determinations consistently provide values below one gram per cubic centimeter, the value for water. From measurements made by the Deep Impact spacecraft during its encounter with comet Tempel 1 in July 2005, the bulk density was estimated to be 0.6 grams per cubic centimeter. The mean impact velocity of a LPC is about 51 km/s, three times the 17 km/s value for a typical near-Earth asteroid (NEA) so the impact energy for an LPC would be 9 times that of a NEA of similar mass. However, the bulk density of a comet (approx. 0.6 g/cu cm) is several times less than the density of a stony NEA (approx. 2.6 g/ cu cm) so for a LPC and a stony NEA of the same size, the LPC's impact energy would be about twice that of the NEA.To properly allocate the limited resources available for NEO surveys and physical characterization, it is important to understand the relative threat from long-period comets versus the threat posed by near-Earth asteroids.

near Earth objects↗
NASA NTRS2007

Geostationary Lightning Mapper for GOES-R

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 11 year data record of global lightning activity. Instrument formulation studies begun in January 2006 will be completed in March 2007, with implementation expected to begin in September 2007. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite, airborne science missions (e.g., African Monsoon Multi-disciplinary Analysis, AMMA), and regional test beds (e.g, Lightning Mapping Arrays) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data now being provided to selected forecast offices will lead to improved understanding of the application of these data in the severe storm warning process and accelerate the development of the pre-launch algorithms and Nowcasting applications. Proxy data combined with MODIS and Meteosat Second Generation SEVERI observations will also lead to new applications (e.g., multi-sensor precipitation algorithms blending the GLM with the Advanced Baseline Imager, convective cloud initiation and identification, early warnings of lightning threat, storm tracking, and data assimilation).

Goodman, Steven↗
NASA NTRS1999

The MSP 2001 Mars Environmental Compatibility Assessment (MECA)

The Mars Environmental Compatibility Assessment (MECA) will evaluate the Martian environment for soil and dust-related hazards to human exploration as part of the Mars Surveyor Program 2001 Lander. Sponsored by the Human Exploration and Development of Space (HEDS) enterprise, MECA's goal is to evaluate potential geochemical and environmental hazards that may confront future Martian explorers, and to guide HEDS scientists in the development of high fidelity Mars soil simulants. The integrated MECA payload contains a wet-chemistry laboratory, a microscopy station, an electrometer to characterize the electrostatics of the soil and its environment, and arrays of material patches to study the abrasive and adhesive properties of soil grains. The instrument will acquire soil samples with a robotic arm equipped with a camera. MECA will examine surface and subsurface soil and dust in order to characterize particle size, shape, hardness, and also physical characteristics that may provide clues to mineralogy. MECA will characterize soil/water mixtures with respect to pH, redox potential, total dissolved ions, and trace toxins. MECA will determine the nature of electrostatic charging associated with excavation of soil, and the influence of ionizing radiation on material properties. It will also observe natural dust accumulation on engineering materials. To accomplish these objectives, MECA is allocated a mass of 10 kg within an enclosure of 35 x 25 x 15 cm. The Wet Chemistry Laboratory (WCL) consists of four identical cells that will accept samples from surface and subsurface regions accessible to the Lander's robotic arm, mix them with water, and perform extensive analysis of the solution. Ion-selective electrodes and related sensors will evaluate total dissolved solids, redox potential, pH, and the concentration of many soluble ions and gases in wet Martian soil. These electrodes can detect potentially dangerous heavy-metal ions, emitted pathogenic gases, and the soil's corrosive potential. Experiments will include cyclic voltammetry and anodic stripping voltammetry. Complementary to the Viking experiments, the chemical laboratory will characterize the water-soil solution rather than emitted gases. Nonetheless, through analysis of dissolved gases it will be able to replicate many of the Viking observations related to oxidants. MECA's microscopy station combines optical and atomic-force microscopy (AFM) in an actively focused, controlled illumination environment to image particles from millimeters to nanometers in size. Careful selection of substrates allows controlled experiments in adhesion, abrasion, hardness, aggregation, magnetic and other properties. Special tools allow primitive manipulation (brushing and scraping) of samples. Soil particle properties including size, shape, color, hardness, adhesive potential (electrostatic and magnetic), will be determined using an array of sample receptacles and collection substrates. The simple, rugged atomic-force microscope will image in the submicron size range and has the capability of performing a particle-by-particle analysis of the dust and soil. On Earth, the earliest forms of life are preserved as microfossils. The atomic-force microscope will have the required resolution to image down to the scale of terrestrial microfossils and beyond. Mounted on the end of the robot arm, MECA's electrometer actually consists of four types of sensors: an electric field meter, several triboelectricity monitors, an ion gauge, and a thermometer. Tempered only by ultraviolet-light-induced ions and a low-voltage breakdown threshold, the dry, cold, dusty martian environment presents an imposing electrostatic hazard to both robots and humans. The field meter will measure the ambient field on nearby objects while the triboelectric sensors, using identical circuitry, will measure the charge accumulated on test substances as they are dragged through the soil by the arm. The ion chamber, open to the environment, will sense both charged dust and free ions in the air. Over and above the potential threat to electronics, the electrostatic environment holds one of the keys to transport of dust and, consequently, Martian meteorology. Viewed with the robot arm camera, the abrasion and adhesion plates are strategically placed to allow direct observation of the interaction between materials and soils on a macroscopic scale. Materials of graded hardness are placed directly under the robot arm scoop to sense wear and soil hardness. A second array, placed on the lander deck, is deployed after the dust plume of landing has settled. It can be manipulated in a primitive fashion by the arm, first having dirt deposited on it from the scoop and subsequently shaken clean. A third array will passively collect dust from the atmosphere. In addition to objectives related to human exploration, the MECA data set will be rich in information relevant to basic geology, paleoclimate, and exobiology issues. To understand both contemporaneous and ancient processes on Mars, the mineralogy, petrology, and reactivity of Martian surface materials should be constrained. The MECA experiment will shed light on these quantities through its combination of chemistry and microscopy. MECA will be capable of measuring the composition of ancient surface water environments, observing microscopic evidence of geological (and biological?) processes, inferring soil and dust transport, comminution and weathering mechanisms, and characterizing soil horizons that might be encountered during excavation.

Hecht, M. H.↗
NASA NTRS2022

Using airborne and DESIS imaging spectroscopy to map plant diversity across the largest contiguous tract of tallgrass prairie on earth

Grassland ecosystems are under threat globally, primarily due to land-use and land-cover changes that have adversely affected their biodiversity. Given the negative ecological impacts of biodiversity loss in grasslands, there is an urgent need for developing an operational biodiversity monitoring system that functions in these ecosystems. In this paper, we assessed the capability of airborne and spaceborne imaging spectroscopy (also known as hyperspectral imaging) to capture plant α-diversity in a large naturally-assembled grassland while considering the impact of common management practices, specifically prescribed fire. We collected a robust in-situ plant diversity data set, including species composition and percent cover from 2500 sampling points with different burn ages, from recently-burned to transitional and pre-prescribed fire at the Joseph H. Williams Tallgrass Prairie Preserve in Oklahoma, USA. We expressed in-situ plant α-diversity using the first three Hill numbers, including species richness (number of observed species in a plant community), exponential Shannon entropy index (hereafter Shannon diversity; effective number of common species, where species are weighed proportional to their percent cover), and inverse Simpson concentration index (hereafter Simpson diversity; effective number of dominant species, where more weight is given to dominant species) at four different plot sizes, including 60 m × 60 m, 120 m × 120 m, 180 m × 180 m, and 240 m × 240 m. We collected full-range airborne hyperspectral data with fine spatial resolution (1 m) and visible and near-infrared spaceborne hyperspectral data from DESIS sensor with coarse spatial resolution (30 m), and used the spectral diversity hypothesis—i.e., that the variability in spectral data is largely driven by plant diversity—to estimate α-diversity remotely. In recently-burned plots and those at the transitional stage, both airborne and spaceborne data were capable of capturing Simpson diversity—a metric that calculates the effective number of dominant species by emphasizing abundant species and discounting rare species—but not species richness or Shannon diversity. Further, neither airborne nor spaceborne hyperspectral data sets were capable of capturing plant α-diversity of 60 m × 60 m or 120 m × 120 m plots. Based on these results, three main findings emerged: (1) management practices influence grassland biodiversity patterns that can be remotely detected, (2) both fine- and coarse-resolution remotely-sensed data can detect the effective number of dominant species (e.g., Simpson diversity), and (3) attention should be given to site-specific plant diversity field data collection to appropriately interpret remote sensing results. Findings of this study indicate the feasibility of estimating Simpson diversity in naturally-assembled grasslands using forthcoming spaceborne imagers such as National Aeronautics and Space Administration's Surface Biology and Geology mission.

Hamed Gholizadeh↗
NASA NTRS2021

Development and Analysis of a Thick Cloud Layers Database for Lightning Launch Commit Criteria Improvement

Lightning can pose a potential threat to space launch vehicles. In response to this, rules were created called the Lightning Launch Commit Criteria (LLCC) that help weather personnel evaluate the potential for natural and rocket-triggered lightning. One of the ten LLCC with the least research is called the Thick Cloud Layers rule. To further understand electrification of thick cloud layers and potentially improve the Thick Cloud Layers rule, a database of thick cloud layers that occurred over the Eastern Range was created. This database is then used to create an algorithm for identifying and differentiating thick cloud layers from other cloud types based on radar characteristics, temperature levels in reference to cloud height, and the surface electric field. By analyzing and identifying thick cloud events, this project could help narrow down when thick clouds are occurring and potentially minimize unnecessary launch delays. Events that caused LLCC violations involving the Thick Cloud Layers rule were analyzed by hand using Level-2 NEXRAD radar data from the National Weather Service WSR-88D radar in Melbourne with the program GR2Analyst. Cases that were found to be isolated and not involved with convection were recorded (date, start/end time, location) in a database. Radar data associated with these cases was collected and gridded using Python radar packages. Once gridded, I calculated and recorded for each radar scan the following radar reflectivity driven parameters within an 11x11 km bin centered on each 1 square km grid point: the mean reflectivity colder than 0 degrees Celsius, Maximum Radar Reflectivity (MRR) colder than 0 degrees Celsius, Volume Averaged Height Integrated Radar Reflectivity (VAHIRR), Hydrometeor Identification (HID), the difference between the maximum and mean reflectivity, the cloud depth colder than 0 degrees Celsius, the overall cloud depth, the cloud top, and the cloud bottom. Soundings for each event were used to determine cloud temperature levels, and where the cloud is in relation to the freezing level. Electric field mill data collected over the Eastern Range was used to determine surface electric fields below each cloud. All parameters were analyzed in depth for several thick cloud cases to gain an understanding of typical thick cloud characteristics. Cases of thick clouds and other isolated cloud types were also recorded for training purposes to see if enough differences exist between cloud types to differentiate them with an algorithm. Each case along with its corresponding characteristics was recorded in a database, and this database was used to compare differing cloud types, as well as train the algorithm to detect thick clouds.

Lightning↗