SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Summary of highlights of the Convective Heating Improvement for Emergency Fire Shelters (CHIEFS) taskunder NASA. CHIEFS was tasked with providing the US Forest Service with an emergency fire shelter forimproved resistance to flame contact. Emphasis is on the final shelter designs at task close-out (end of FY17).
This experimental study examined the effects of transparency (operationalized as increasing levels of explanation) on pilot trust of an automated emergency landing planner. A low-fidelity study was conducted where commercial pilots (N12) interacted with simulated recommendations from NASA's Emergency Landing Planner (ELP). These recommendations varied in their associated levels of transparency. Results indicated that trust in the ELP was influenced by the level of transparency within the human-machine interface of the ELP.
Multiwavelength ultraviolet (UV) observations by the Interface Region Imaging Spectrograph satellite in active region NOAA 12529 have recently pointed out the presence of long-lasting brightenings, akin to UV bursts, and simultaneous plasma ejections occurring in the upper chromosphere and transition region during secondary flux emergence. These signatures have been interpreted as evidence of small-scale, recurrent magnetic reconnection episodes between the emerging flux region (EFR) and the preexisting plage field. Here we characterize the UV emission of these strong, intermittent brightenings and study the surge activity above the chromospheric arch filament system (AFS) overlying the EFR. We analyze the surges and the cospatial brightenings observed at different wavelengths. We find an asymmetry in the emission between the blue and red wings of the Si iv λ1402 Å and Mg ii k λ2796.3 lines, which clearly outlines the dynamics of the structures above the AFS that form during the small-scale eruptive phenomena. We also detect a correlation between the Doppler velocity and skewness of the Si iv λλ1394 and 1402 line profiles in the UV burst pixels. Finally, we show that genuine emission in the Fe xii λ1349.4 line is cospatial to the Si iv brightenings. This definitely reveals a pure coronal counterpart to the reconnection event.
The new Commercial crew vehicle (CCV) Emergency Breathing Air Assembly (CEBAA) is a Composite Overwrapped Pressure Vessel (COPV) filled with high-pressure air, which is designed to provide portable emergency breathing air for up to five crew in the event of an ammonia leak aboard the International Space Station (ISS). Industrially, several common methods of delivering breathing air exist; however, ground processing constraints and flight requirements necessitate the more unusual approach of servicing a COPV with high purity gaseous nitrogen and oxygen serially to achieve a specific mixture of breathing air at approximately 4000 PSIA. A dwell period allows the gases to mix before sampling for composition verification. The novelty of this approach led to concerns about acceptable gas mixing and the ability to deliver accurate gas compositions. Tests examining mixing rate over a series of post-servicing dwell periods were conducted to determine the optimal time for achieving a homogeneous mixture. Further testing was conducted to examine process accuracy using temperature-pressure load criteria to target a specific concentration of oxygen and nitrogen. Test results show that while some stratification of nitrogen and oxygen was observed, a seven-day dwell is adequate time to achieve an acceptable level of mixing, and that existing ground support equipment can accurately service CEBAA COPVs to the required temperature, pressure and composition for flight.
The Scalable Traffic Management for Emergency Response Operations (STEReO) project requires prototyping software to extend existing Airspace Operations Laboratory (AOL) software capabilities to support research in integrating drone flight activities within an emergency response airspace environment. A presentation encompassing project AOLUSS details, communications and network design will be presented at a Workshop.
National databases that collect various kinds of textual threat reports such as ASRS, CERT, and NVD manually process their reports individually. They then offer data products to disseminate the aggregate information, like newsletters, alerts or individual report searching. The goal of this research is to connect these individual reports thematically and temporally to identify emerging or recurring threats, by analyzing large collections of text, source code, collaboration and communication patterns. This capability, I argue, enables us to identify the emergence and recurrence of such themes, and the contexts in which they re-occur, facilitating faster and more capable mitigation. I propose two models to shed light on this goal: An empirical model of vulnerabilities as bugs, the commit flow model, and one of the vulnerabilities and aviation safety threats as topics, the topic flow model. I use as gold standard existing manual workflows in both domains, reflected in the existing data products by these organizations, and empirically evaluate if the automated model scan match or outperform existing manual practices.
This work investigates two machine learning techniques: Support Vector Machine (SVM) and Autoencoders (AE)with SVM layer for classification of radar trajectories as General Aviation (GA), fixed-wing small Unmanned Aerial System (sUAS), or not-an-aircraft using radar data recorded from sUAS. Onboard identification of intruder aircraft type is useful for planning avoidance maneuvers and is necessary to provide autonomous systems to meet or exceed the avoidance capability of a human pilot. Aircraft classification can identify intruder aircraft that are not part of the team and may be violating a Temporary Flight Restriction. Aircraft classification is needed in monitoring an airspace where multiple aircraft are teaming on a shared task. Scalable Traffic Management for Emergency Response Operations (STEReO) is a NASA project aimed at improving disaster response by enabling large scale aircraft operations through the teaming of manned aircraft with sUAS to maximize emergency response resources. To this end, this work uses trajectories and radar derived features to classify aircraft from a multirotor sUAS. The AE + SVM generated the strongest classification overall accuracy of 93.5% using the first 4 seconds of radar track data for tracks that activated the avoidance system. Subsampling the available track data increased the available training data with the maximum aircraft recall of 0.94 achieved using the SVM with 1 second track data.
Diurnal cycling of plant carbon uptake and water use, and their responses to water and heat stresses, provide direct insight into assessing ecosystem productivity, agricultural production and management practices, carbon and water cycles, and feedbacks to the climate. Temperature, light, atmospheric water demand, soil moisture and leaf water potential vary over the course of the day, leading to diurnal variations in stomatal conductance, photosynthesis and transpiration. Earth observations from polar-orbiting satellites are incapable of studying these diurnal variations. Here, we review the emerging satellite observations that have the potential for studying how plant functioning and ecosystem processes vary over the course of the diurnal cycle. The recently launched ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and Orbiting Carbon Observatory-3 (OCO-3) provide land surface temperature, evapotranspiration (ET), gross primary production (GPP) and solar-induced chlorophyll fluorescence data at different times of day. New generation operational geostationary satellites such as Himawari-8 and the GOES-R series can provide continuous, high-frequency data of land surface temperature, solar radiation, GPP and ET. Future satellite missions such as GeoCarb, TEMPO and Sentinel-4 are also planned to have diurnal sampling capability of solar-induced chlorophyll fluorescence. We explore the unprecedented opportunities for characterizing and understanding how GPP, ET and water use efficiency vary over the course of the day in response to temperature and water stresses, and management practices. We also envision that these emerging observations will revolutionize studies of plant functioning and ecosystem processes in the context of climate change and that these observations and findings can inform agricultural and forest management and lead to improvements in Earth system models and climate projections.
The use of unmanned aircraft systems (UAS) to support disaster management is becoming more widespread. Uninhibited by many of the operational constraints imposed on crewed aircraft, UAS have the potential to augment the aerial response in a number of ways, including supporting situation awareness, thereby accelerating disaster management efforts. The intent of the Scalable Traffic management for Emergency Response Operations (STEReO) activity is to build an ecosystem that will allow small UAS (sUAS) operations to fly safely in the airspace during disaster management events. The first step toward this goal was to develop a small, single-user tool (the UASP-kit) that will enhance an sUAS pilot’s situation awareness of the airspace. In a subsequent project (Advanced Capabilities for Emergency Response Operations (ACERO)) the UASP-kit will be expanded and built upon to develop and airspace management tool.
How did chemicals first become organized into systems capable of self-propagation and adaptive evolution? One possibility is that the first evolvers were chemical ecosystems localized on mineral surfaces and composed of sets of molecular species that could catalyze each other’s formation. We used a bottom-up experimental framework; chemical ecosystem selection (CES) to evaluate this perspective and search for surface-associated; and mutually catalytic chemical systems based on the changes in chemistry they are expected to induce. Here, we report the results of preliminary CES experiments conducted using a synthetic “prebiotic soup” and pyrite grains, which yield dynamical patterns that are suggestive of the emergence of mutual catalysis. While more research is needed to better understand the specific patterns observed here and determine whether they are reflective of self-propagation, these results illustrate the potential power of CES to test competing hypotheses for the emergence of protobiological chemical systems.
Potential climate-related impacts on future crop yield are a major societal concern first surveyed in a harmonized multi-model effort in 2014. We report here on new 21st-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 +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 CO2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts — when the change signal emerges from the noise — consistently occurs earlier in the new projections for several main producing regions before 2040. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.
With continued fossil-fuel dependence, anthropogenic aerosols over South Asia are projected to increase until the mid-21st century along with greenhouse gases (GHGs). Using the Community Earth System Model (CESM1) Large Ensemble, we quantify the influence of aerosols and GHGs on South Asian seasonal precipitation patterns over the 21st century under a very high-emissions (RCP 8.5) trajectory. We find that increasing local aerosol concentrations could continue to suppress precipitation over South Asia in the near-term, delaying the emergence of precipitation increases in response to GHGs by several decades in the monsoon season and a decade in the post-monsoon season. Emergence of this wetting signal is expected in both seasons by the mid-21st century. Our results demonstrate that the trajectory of local aerosols together with GHGs will shape near-future precipitation patterns over South Asia. Therefore, constraining precipitation response to different trajectories of both forcers is critical for informing near-term adaptation efforts.
The development of an accurate forecast for solar eruptive activity has become increasingly important in order to prevent any potential impact on activities in space and the Earth's environment. It is therefore crucial to detect active regions before they appear on the solar surface and create early warning capabilities for upcoming Space Weather disturbances. In this work, 9TB of solar data (SDO/HMI dopplergrams, magnetograms and continuum intensity maps) involving the emergence of 61 NOAA solar active regions since 2010 were processed using the NASA HECC capabilities. An acoustic power maps time-series dataset was created (for four different frequency ranges and processed to take into account the solar sphere geometric effect ) which can be used for understanding the dynamics of the solar surface and train a variety of ML models. The calculated acoustic power maps carry precursor information associated with the decrease in continuum intensity on the solar surface, verifying older helioseismology research. Our results show that a Long Short-Term Memory (LSTMs) model, with a modest layer depth and the right hyperparameters tuned, when trained on this solar acoustic power maps dataset can predict without false negatives a drop in intensity (associated with the emergence of the active region), up to 18 hours in advance.
The air-to-ground network has the goal of advancing the capabilities and versatility of communications in wildfire and emergency scenarios. To ensure compatibility and keep costs low for emergency services interoperability between hardware is an important step in development of the mesh network. For the purposes of this evaluation interoperability of hardware is described as the ability for hardware from different vendors to be interchangeable within the network with minimal initial setup and no functional change from an operator’s viewpoint. This report will also focus on the features and limitations in addition to the interoperability of the major wireless hardware systems core to the function of the proposed air-to-ground mesh network. The hardware systems involved are ADS-B receivers, 3GPP Sidelink devices, mesh radios, and commercial ISM band radios. For early testing the mesh radios will be in the 900 MHz frequency range using the DoodleLabs RM-1700-22W Mesh Rider Radio as the lab reference. This report will consider and categorize desirability of features, the amount of setup required before deployment, the effect on performance characteristics from nominal and maximum load, and the ability to interface with other vender devices.
Novel machine learning-based anomaly detection Level 1 (L1) triggers are currently under development at CMS, namely AXOL1TL and CICADA. The former employs a variational autoencoder, while the latter utilizes a convolutional autoencoder. These triggers aim to balance rate reduction with model independence, enabling the selection of potentially significant events that might be overlooked by traditional triggers relying on basic kinematic variable selections. Consequently, they have the potential to enhance signals indicative of physics beyond the Standard Model, such as those associated with emerging jets. Such signals are predicted by models featuring a composite dark sector where long-lived particles decay into Standard Model jets with displaced tracks and numerous vertices. This study evaluates the efficiency of these anomaly detection triggers in selecting events with emerging jets produced via the s-channel production of two dark quarks.
Novel machine learning-based anomaly detection Level 1 (L1) triggers are currently under development at CMS, namely AXOL1TL and CICADA. The former employs a variational autoencoder, while the latter utilizes a convolutional autoencoder. These triggers aim to balance rate reduction with model independence, enabling the selection of potentially significant events that might be overlooked by traditional triggers relying on basic kinematic variable selections. Consequently, they have the potential to enhance signals indicative of physics beyond the Standard Model, such as those associated with emerging jets. Such signals are predicted by models featuring a composite dark sector where long-lived particles decay into Standard Model jets with displaced tracks and numerous vertices. This study evaluates the efficiency of these anomaly detection triggers in selecting events with emerging jets produced via the s-channel production of two dark quarks.
Studying the emergent properties of hybrid quantum material platforms holds great promise for advancing quantum technologies and transforming our ability to control quantum mechanical interactions. A key development in recent years is the recognition that magnons – the elementary quanta of spin waves – can serve as fundamental building blocks in quantum systems. Understanding the mechanisms behind the generation and control of hybrid quasiparticles based on magnons could pave the way for engineering new materials for quantum coherent processing and quantum computing. This research project focused on the precise control of light-matter interactions in magnetic hybrid systems and nanostructures, where light is carried by microwave photons and matter by magnons in engineered magnetic metamaterials. Furthermore, we investigated hybridized magnon interactions in magnetic nanostructures and devices. To this end, the project has developed new measurement techniques and systematically studied novel material systems for efficient magnon-photon coupling. A key focus was uncovering the fundamental mechanisms governing magnon-photon and magnon-phonon interactions – critical for utilizing magnons as coherent information transducers between carriers. Specifically, this research addressed: 1. Investigate the dispersion and collective properties of magnon hybrid systems in new material systems. 2. Develop magnonic hybrids with non-zero wavevectors and achieve effective control of magnon-polariton properties through engineered magnonic behaviors. 3. Determine how spin-orbit torques interact with electromagnetic fields in the strong coupling regime. This work generated new insights into the physics of magnonic hybrid systems and their emergent properties by broadening the range of material platforms and measurement techniques used to study magnon-photon and magnon-magnon coupling. The findings deepen our understanding of magnon-polaritons and lay the groundwork for novel spintronic devices with potential applications in quantum information science.
Asthma is a chronic disease that can result in exacerbations leading to urgent care in emergency departments (EDs) and hospitals. We examined seasonal and temporal trends in pediatric asthma ED (1997-1999) and hospital (1986-1999) admission data so as to identify periods of increased risk of urgent care by age group, gender, and race. All pediatric ED and hospital admission data for Maryland residents occurring within the state of Maryland were evaluated. Distinct peaks in pediatric ED and hospital asthma admissions occurred each year during the winter-spring and autumn seasons. Although the number and timing of these peaks were consistent across age and racial groups, the magnitude of the peaks differed by age and race. The same number, timing, and relative magnitude of the major peaks in asthma admissions occurred statewide, implying that the variables affecting these seasonal patterns of acute asthma exacerbations occur statewide. Similar gross seasonal trends are observed worldwide. Although several environmental, infectious, and psychosocial factors have been linked with increases in asthma exacerbations among children, thus far they have not explained these seasonal patterns of admissions. The striking temporal patterns of pediatric asthma admissions within Maryland, as described here, provide valuable information in the search for causes.