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At least 1,099 records · Page 61

Radial Field Piezoelectric Diaphragms

A series of active piezoelectric diaphragms were fabricated and patterned with several geometrically defined Inter-Circulating Electrodes "ICE" and Interdigitated Ring Electrodes "ICE". When a voltage potential is applied to the electrodes, the result is a radially distributed electric field that mechanically strains the piezoceramic along the Z-axis (perpendicular to the applied electric field). Unlike other piezoelectric bender actuators, these Radial Field Diaphragms (RFDs) strain concentrically yet afford high displacements (several times that of the equivalent Unimorph) while maintaining a constant circumference. One of the more intriguing aspects is that the radial strain field reverses itself along the radius of the RFD while the tangential strain remains relatively constant. The result is a Z-deflection that has a conical profile. This paper covers the fabrication and characterization of the 5 cm. (2 in.) diaphragms as a function of poling field strength, ceramic thickness, electrode type and line spacing, as well as the surface topography, the resulting strain field and displacement as a function of applied voltage at low frequencies. The unique features of these RFDs include the ability to be clamped about their perimeter with little or no change in displacement, the environmentally insulated packaging, and a highly repeatable fabrication process that uses commodity materials.

Bryant, R. G.↗

Government and Industry Issues for Expanding Commercial Markets into Space

In 2002, the Foresight and Governance Project at the Woodrow Wilson Center in Washington, D.C, organized a "Global Foresight Workshop" in partnership with NASA and in cooperation with other Federal Agencies to provide integrated consideration of broad challenges for the 2lst century. Many long-range goals for the nation were discussed and selected, among them were space related goals of interest to NASA. During much of the Agency's history, NASA advanced studies have focused consistently on the challenges of science-driven space exploration and operations. However, workshop findings indicate little interest in these goals unless they can also solve national and global issues. Many technologies and space development studies indicate great potential to enable new, important commercial markets in space that could address the many global challenges facing America in this century. But communication of these ideas are lacking. In conclusion, it appears that the commercial development of space could have broad implications on many impending problems, including energy resources, environmental impact, and climate changes. The challenge will be to develop a consistent coordinated effort among the many industries and Agencies that should be involved in opening this new frontier for these new commercial markets.

Smitherman, David V., Jr.↗

A retrospective study of promethazine and its failure to produce the expected incidence of sedation during space flight

Since March 1989, intramuscular (IM) promethazine has been successfully used to treat the symptoms of space motion sickness. The incidence of sedation associated with promethazine administration on the ground is large and may result in operational impact. The authors undertook a retrospective study to quantify the incidence of sedation from promethazine use during Space Shuttle flights. Crew medical debriefings from 14 shuttle missions were reviewed for crew members who had been treated with IM promethazine and their corresponding symptoms were identified. Twenty-one crew members received IM promethazine (25-50 mg), and only one experienced any associated sedation with no operational impact. This sedation incidence of less that 5% is in stark contrast to the 60 to 73% incidence of sedation seen in ground-based studies. The incidence of sedation during space flight from IM promethazine is substantially less than that seen on the ground and does not present an operational problem during Space Shuttle flights. Future investigations of environmental stressors and pharmacodynamic changes associated with space flight may explain the huge disparity between the space-flight and ground-based data.

short duration↗

Alterations in renal stone risk factors after space flight

Exposure to the microgravity environment of space produces a number of physiological changes of metabolic and environmental origin that could increase the potential for renal stone formation. Metabolic, environmental and physicochemical factors that influence renal stone risk potential were examined in 24-hour urine samples from astronauts 10 days before launch and on landing day to provide an immediate postflight assessment of these factors. In addition, comparisons were made between male and female crewmembers, and between crewmembers on missions of less than 6 days and those on 6 to 10-day missions. Results suggest that immediately after space flight the risk of calcium oxalate and uric acid stone formation is increased as a result of metabolic (hypercalciuria, hypocitraturia, pH) and environmental (lower urine volume) derangements, some of which could reflect residual effects of having been exposed to microgravity.

NASA Center JSC↗

Target Recognition Using Neural Networks for Model Deformation Measurements

Optical measurements provide a non-invasive method for measuring deformation of wind tunnel models. Model deformation systems use targets mounted or painted on the surface of the model to identify known positions, and photogrammetric methods are used to calculate 3-D positions of the targets on the model from digital 2-D images. Under ideal conditions, the reflective targets are placed against a dark background and provide high-contrast images, aiding in target recognition. However, glints of light reflecting from the model surface, or reduced contrast caused by light source or model smoothness constraints, can compromise accurate target determination using current algorithmic methods. This paper describes a technique using a neural network and image processing technologies which increases the reliability of target recognition systems. Unlike algorithmic methods, the neural network can be trained to identify the characteristic patterns that distinguish targets from other objects of similar size and appearance and can adapt to changes in lighting and environmental conditions.

Ross, Richard W.↗

Data Assimilation of Photosynthetic Light-use Efficiency using Multi-angular Satellite Data: II Model Implementation and Validation

Spatially explicit and temporally continuous estimates of photosynthesis will be of great importance for increasing our understanding of and ultimately closing the terrestrial carbon cycle. Current capabilities to model photosynthesis, however, are limited by accurate enough representations of the complexity of the underlying biochemical processes and the numerous environmental constraints imposed upon plant primary production. A potentially powerful alternative to model photosynthesis through these indirect observations is the use of multi-angular satellite data to infer light-use efficiency (ε) directly from spectral reflectance properties in connection with canopy shadow fractions. Hall et al. (this issue) introduced a new approach for predicting gross ecosystem production that would allow the use of such observations in a data assimilation mode to obtain spatially explicit variations in ε from infrequent polar-orbiting satellite observations, while meteorological data are used to account for the more dynamic responses of ε to variations in environmental conditions caused by changes in weather and illumination. In this second part of the study we implement and validate the approach of Hall et al. (this issue) across an ecologically diverse array of eight flux-tower sites in North America using data acquired from the Compact High Resolution Imaging Spectroradiometer (CHRIS) and eddy-flux observations. Our results show significantly enhanced estimates of ε and therefore cumulative gross ecosystem production (GEP) over the course of one year at all examined sites. We also demonstrate that ε is greatly heterogeneous even across small study areas. Data assimilation and direct inference of GEP from space using a new, proposed sensor could therefore be a significant step towards closing the terrestrial carbon cycle.

Hilker, Thomas↗

Quasi-Linear Circuit

This work involved developing spacequalifiable switch mode DC/DC power supplies that improve performance with fewer components, and result in elimination of digital components and reduction in magnetics. This design is for missions where systems may be operating under extreme conditions, especially at elevated temperature levels from 200 to 300 degC. Prior art for radiation-tolerant DC/DC converters has been accomplished utilizing classical magnetic-based switch mode converter topologies; however, this requires specific shielding and component de-rating to meet the high-reliability specifications. It requires complex measurement and feedback components, and will not enable automatic re-optimization for larger changes in voltage supply or electrical loading condition. The innovation is a switch mode DC/DC power supply that eliminates the need for processors and most magnetics. It can provide a well-regulated voltage supply with a gain of 1:100 step-up to 8:1 step down, tolerating an up to 30% fluctuation of the voltage supply parameters. The circuit incorporates a ceramic core transformer in a manner that enables it to provide a well-regulated voltage output without use of any processor components or magnetic transformers. The circuit adjusts its internal parameters to re-optimize its performance for changes in supply voltage, environmental conditions, or electrical loading at the output

Bradley, William↗

Modeling of Blood Lead Levels in Astronauts Exposed to Lead from Microgravity-Accelerated Bone Loss

Human exposure to lead has been associated with toxicity to multiple organ systems. Studies of various population groups with relatively low blood lead concentrations (<10 μg/dL) have indicated associations of blood lead level with lower cognitive test scores in children, later onset of puberty in girls, and increased blood pressure and cardiovascular mortality rates in adults. Cognitive effects are considered by regulatory agencies to be the most sensitive endpoint at low doses. Although ~95% of the body burden of lead is stored in the bones, the adverse effects of lead correlate with the concentration of lead in the blood better than with that in the bones. NASA has found that prolonged exposure to microgravity during spaceflight results in a significant loss of bone minerals, the extent of which varies from individual to individual and from bone to bone, but generally averages about 0.5% per month. During such bone loss, lead that had been stored in bones would be released along with calcium. The effects on the concentration of lead in the blood (PbB) of various concentrations of lead in drinking water (PbW) and of lead released from bones due to accelerated osteoporosis in microgravity, as well as changes in exposure to environmental lead before, during, and after spaceflight were evaluated using a physiologically based pharmacokinetic (PBPK) model that incorporated exposure to environmental lead both on earth and in flight and included temporarily increased rates of osteoporosis during spaceflight.

Garcia, H.↗

Nitrogen Dioxide Trend over the United States: the View from the Ground, the View from Space

Emissions of nitrogen oxides (NOx) are decreasing over the US due to environmental policies and technological change. We use observations of tropospheric nitrogen dioxide (NO2) columns from the Ozone Monitoring Instrument (OMI) satellite instrument and surface NO2 in-situ measurements from the air quality system (AQS) to quantify the trends, and to establish the relationship between the trends in tropospheric column and surface concentration. Both observations show substantial downward trends from 2005 to 2013, with an average reduction of 35 percent according to OMI and 38 percent according to AQS. The annual reduction rates are largest in 2005-2009: -6.2 percent per year and -7 percent per year observed by OMI and AQS, respectively. We examine various factors affecting the estimated trend in OMI NO2 columns and in-situ NO2 observations. An improved understanding of trend offers valuable insights about effectiveness of emission reduction regulations on state and federal level.

Air pollution↗

Automated Segmentation and Classification of Coral using Fluid Lensing from Unmanned Airborne Platforms

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the world's coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85%. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

algorithms segments classifications↗

Health and Climate Impacts of Ocean-Going Vessels in East Asia

East Asia has the most rapidly growing shipping emissions of both CO2 and traditional air pollutants, but the least in-depth analysis. Full evaluation of all pollutants is needed to assess the impacts of shipping emissions. Here, using an advanced method based on detailed dynamic ship activity data, we show that shipping emissions in East Asia accounted for 16% of global shipping CO2 in 2013, compared to only 4-7% in 2002-2005. Increased emissions lead to large adverse health impacts, with 14,500-37,500 premature deaths per year. Global mean radiative forcing from East Asian shipping is initially negative, but would become positive after approximately eight years for constant current emissions. As a large fraction of vessels are registered elsewhere, joint efforts are necessary to reduce emissions and mitigate the climate and health impacts of shipping in the region.

climate change impacts↗

Automated Segmentation and Classification of Coral Using Fluid Lensing from Unmanned Airborne Platforms

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the worlds coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

algorithms segments classifications↗

Evaluation of a High-Resolution Numerical Weather Prediction Model’s Simulated Clouds Using Observations from CloudSat, GOES-13 and in Situ Aircraft

This study aimed to assess tropical cloud properties predicted by Environment and Climate Change Canada’s Global Environmental Multiscale (GEM)modelwhen run with the Milbrandt–Yau double-moment cloud microphysical scheme and one-way nesting that culminated at a (∼300 km)2 inner domain with 0.25 km horizontal grid spacing. The assessment utilized satellite and in situ data collected during the High Ice Water Content (HIWC) and High Altitude Ice Crystals (HAIC) projects for a mesoscale convective system on 16May 2015 over French Guiana.Data fromCloud- Sat’s cloud-profiling radar and GOES-13’s imager were compared to data either simulated directly by GEM or produced by operating on GEM’s cloud data with both the CFMIP (Cloud Feedback Model Intercomparison Project) Observation Simulator Package (COSP) instrument simulator and a three-dimensional Monte Carlo solar radiative transfer model. In situ observations were made from research aircraft – Canada’s National Research Council Convair-580 and the French SAFIRE Falcon-20 – whose flight paths were aligned with CloudSat’s ground-track. Spatial and temporal shifts of clouds simulated by GEM compared well to GOES-13 imagery. There are, however, differences between simulated and observed amounts of high and low cloud. While GEM did well at predicting ranges of ice-water content (IWC) near 11 km altitude (Falcon-20), it produces too much graupel and snow near 7 km (Convair-580). This produced large differences between CloudSat’s and COSP-generated radar reflectivities and two-way attenuations. On the other hand, CloudSat’s inferred values of IWC agree well with in situ samples at both altitudes. Generally, GEM’s visible reflectances exceeded GOES-13’s on account of having produced too much low-level liquid cloud. It is expected that GEM’s disproportioning of cloud hydrometeors will improve once it includes a better representation of secondary ice production.

Qu, Zhipeng↗

Follow-up Water Quality Analysis at the Little Patuxent River in Anne Arundel County, Maryland andImpact to the Health of the Chesapeake Bay

In accordance with the federal Clean Water Act, it is of utmost importance to identify impaired water bodies and subsequently implement Total Maximum Daily Loads (TMDL) as needed to achieve desirable water quality standards. One of these water bodies include the Little Patuxent River (LPR) in the Anne Arundel and Howard Counties in Maryland, which is the subject of this report. In the 1996, 1998, and then 2006 Maryland Integrated Report, the LPR was found to be impaired by nutrients, bacteria, suspended sediments, and cadmium (Cd). As a result, TMDLs were created to limit factors that were contributing to these problems (Maryland Department of the Environment 2011, December). But later in 2008 and 2009, water quality analyses (WQAs) found that the LPR’s condition had improved. From a Category 5 Cd and phosphorus classification in 1996 it was reduced to Category 2 in 2008 for Cd and in 2009 for phosphorus (Maryland Department of the Environment 2011, December). However, it has been more than a decade since the last WQA and the environmental conditions may have changed. This lack of testing could result in untracked, accumulated damages to the overall health of the LPR. We present testing results for the 13 site locations on the LPR to evaluate turbidity, pH, phosphates, nitrates, water temperature, dissolved oxygen (D.O.), coliform bacteria, and cadmium concentrations in sediments. We will also present next steps including remediation strategies.

Aaban Syed↗

Climate Impacts on Global Agriculture Emerge Earlier in New Generation of Climate and Crop Models

Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project’s Global Gridded Crop Model Intercomparison based on the Coupled Model Intercomparison Project Phase 5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new twenty-first-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to −6% (SSP126) and from +1% to −24% (SSP585)—explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO 2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts consistently occurs earlier in the new projections—before 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

crop model↗

GeoNEX-ML: A Machine Learning System for Earth Observations

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Geostationary↗

Discrete anisotropic radiative transfer modelling of solar-induced chlorophyll fluorescence: Structural impacts in geometrically explicit vegetation canopies

Solar-induced fluorescence (SIF) is a subtle but informative optical signal of vegetation photosynthesis. Remotely sensed SIF integrates environmental, physiological and structural changes that alter photosynthesis at leaf, plant and canopy scales. Radiative transfer models are ideally suited to investigate the complex sources of variability in the SIF signal to guide the interpretation of SIF retrievals from airborne and space-borne platforms. Here, we coupled the Fluspect-Cx model of leaf optical properties and chlorophyll-a fluorescence with the Discrete Anisotropic Radiative Transfer (DART) model to upscale SIF from individual leaves to three-dimensional (3D) structurally explicit canopies. For one-dimensional homogeneous (turbid-like) canopies, DART-SIF was nearly identical to SIF simulated in two existing models, SCOPE and mSCOPE (RMSE <0.221 W./sq.m.μm.sr). DART simulations in geometrically explicit 3D canopies offered four important insights regarding the influence of vegetation structure on the multi-angular top-of-canopy SIF signal. First, changes in the 3D canopy architecture of maize crops, represented by leaf density (leaf area index), and plant clumping (canopy closure) had a larger impact on SIF than the modelled photosynthetic efficiency distinction between sun-adapted and shade-adapted foliage. Second, clumping of leaves at the crop and stand levels was identified as one of the key driving factors of multi-angular anisotropy of red and far-red SIF (686 and 740 nm) for both maize and eucalyptus canopies. Third, non-photosynthetic woody material had a significant impact on top-of-canopy SIF in modelled 3D forest stands. Wood shadowing decreased the photosynthetically active radiation absorbed by green leaves, and consequently the SIF emissions, by 10% in sparse and 17% in dense eucalyptus stands. The wood obstruction (blocking) effect, quantified as a relative difference of SIF escape probabilities from canopies with and without wood in the nadir viewing direction, decreased far-red SIF by 4–6% but it had a smaller and sometimes positive influence (by less than 2%) on red SIF. Fourth, DART 3D radiative budget profiles revealed that the majority of the SIF signal from a dense eucalyptus stand originated from the top 25% of the simulated canopy. Interestingly, the introduction of bark-covered woody elements did not alter the simulated balance and omnidirectional escape factor of red SIF in this upper canopy part but did raise significantly both of them in case of far-red SIF. These results demonstrate the importance of 3D radiative transfer and radiative budget simulations for investigating SIF interactions in structurally complex plant canopies and for a better understanding of spatiotemporal and multi-angular remote sensing SIF observations.

Zbyněk Malenovský↗

Atmospheric winds with deep optical flow

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Atmospheric winds↗