Search NASA⌕ Search

SEARCH · Search NASA

Results for “missing information”

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.

At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Seismic Event Characterization Using Full Moment Tensors on the Hypersphere

Moment tensor solutions provide insights into the deformation that has occurred in the source region of a seismic event and are therefore of great value in identifying different types of seismic sources, such as when monitoring for underground nuclear tests. Despite this utility, inversion of waveforms recorded by seismometers for their full seismic moment tensor is not yet routine, and development of robust methods to classify events based on this information is in its infancy. Here, we assemble an inventory of 1405 full moment tensor solutions that include explosive, earthquake, and collapse events, and investigate the use of anisotropic probability distribution functions on the 5D hypersphere to discriminate between these sources. Using a Bayesian classifier, we obtain optimal success rates of 98.4% across all events and demonstrate that modification of the prior probabilities provides a natural way to alter the balance between not missing desirable events (such as explosions) versus misclassifying large numbers of undesired events (such as earthquakes). The approach is specifically designed to progress from traditional, bipolar event screening metrics to more generalized event identification across multiple types of seismic sources. Despite current databases containing insufficient numbers of events to definitively demonstrate at present, we also find intriguing evidence of subgroupings within individual source populations on the hypersphere, for example, between chemical and nuclear explosions, raising the potential possibility of discriminating between these event types in the future.

Geosciences↗
DOE OSTI2024

Efficient species identification for Pacific salmon genetic monitoring programs

Abstract Genetic monitoring of Pacific salmon in the Columbia River basin provides crucial information to fisheries managers that is otherwise challenging to obtain using traditional methods. Monitoring programs such as genetic stock identification (GSI) and parentage‐based tagging (PBT) involve genotyping tens of thousands of individuals annually. Although rare, these large sample collections inevitably include misidentified species, which exhibit low genotyping success on species‐specific Genotyping‐in‐Thousands by sequencing (GT‐seq) panels. For laboratories involved in large‐scale genotyping efforts, diagnosing non‐target species and reassigning them to the appropriate monitoring program can be costly and time‐consuming. To address this problem, we identified 19 primer pairs that exhibit consistent cross‐species amplification among salmonids and contain 51 species informative variants. These genetic markers reliably discriminate among 11 salmonid species and two subspecies of Cutthroat Trout and have been included in species‐specific GT‐seq panels for Chinook Salmon, Coho Salmon, Sockeye Salmon, and Rainbow Trout commonly used for Pacific salmon genetic monitoring. The majority of species‐informative amplicons (16) were newly identified from the four existing GT‐seq panels, thus demonstrating a low‐cost approach to species identification when using targeted sequencing methods. A species‐calling script was developed that is tailored for routine GT‐seq genotyping pipelines and automates the identification of non‐target species. Following extensive testing with empirical and simulated data, we demonstrated that the genetic markers and accompanying script accurately identified species and are robust to missing genotypic data and low‐frequency, shared polymorphisms among species. Finally, we used these tools to identify Coho Salmon incidentally caught in the Columbia River Chinook Salmon sport fishery and used PBT to determine their hatchery of origin. These molecular and computing resources provide a valuable tool for Pacific salmon conservation in the Columbia River basin and demonstrate a cost‐effective approach to species identification for genetic monitoring programs.

Robinson, Zachary L.↗
DOE OSTI2019

Time-Sensitive Value of Efficiency: Use Cases in Electricity Sector Planning and Programs

Most energy efficiency measures produce energy savings that vary over the course of a year. The value of the hourly electricity savings also varies over the course of a year because the cost of generating, transmitting and distributing electricity during peak demand periods may be significantly higher than during off-peak, or lower load, hours. But many planning and program activities across the United States are missing this important element. A variety of trends are changing the electricity system, including increased adoption of other distributed energy resources, electrification of buildings and vehicles, and relative costs for natural gas-fired and renewable energy generation. Knowing when energy efficiency occurs and the value of the energy or demand savings to the electricity system—the time-sensitive value of efficiency—provides public utility commissions, utilities and other decision makers with key information needed to procure the optimal amount of energy efficiency for their system. The report begins with a discussion of our approach to determining use cases and examples, and then examines the need for time-sensitive efficiency data. Next, it examines five use cases in which states, independent system operators/regional transmission operators (ISOs/RTOs), utilities, and efficiency program administrators consider the TSV-EE: (1) energy efficiency program planning and evaluation, (2) distribution system planning, (3) electricity resource planning (4) electricity rate design, and (5) state and local government activities. The report concludes with areas for future research on the time-sensitive value of efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Risk-Informed Systems Analysis (RISA) Dynamic Fire PRA Roadmap

Modeling and implementing fire safety for nuclear power plants is a costly activity. Because of the complexity of fire phenomenon and multiple operational procedures, it is difficult to computationally provide assurance that the mitigation methods are adequate for critical areas using current analysis methods. An economical method to provide more accurate modeling and optimize mitigation methods is needed to improve nuclear power viability. This report describes the initial investigation into modeling and simulation tools for application of fire as part of the Risk-Informed Systems Analysis (RISA), formerly Risk-Informed Safety Margin Characterization (RISMC). The report provides a framework of how 3D modeling and simulation techniques could be combined with a dynamic Probabilistic Risk Analysis (PRA) to reduce compounding conservatism present in current Fire PRA methods. Electrical Power Research Institute has analyzed current Fire PRA practices and identified the most significant contributors to risk and areas for improving analysis. This framework describes how to apply dynamic PRA and simulation methods for key contributors/scenarios, credit missing factors of manual fire suppression, and applying conditional probabilities.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2006

Validation of Ultrasound Imaging to Rule-out Thoracic Trauma on the International Space Station

Introduction: Aboard the International Space Station (ISS) an intra-thoracic injury may be disastrous to the crew member if the diagnosis is missed or even delayed. Pneumothorax and hemothorax commonly seen in trauma patients; the diagnosis is usually confirmed by chest X-ray or computed tomography. In this study, the ability of ultrasound to rule out pneumothorax by the presence "lung sliding" and hemothorax by the absence of pleural fluid was validated. Methods: The research activities were approved by the NASA Johnson Space Center Committee for the Protection of Human Subjects, and the participating crewmembers signed informed consent prior to the activity. ISS crewmembers received 2-hours of "hands on" ultrasound training 8 months prior to the on-orbit ultrasound exam. Baseline ultrasound images of the thorax were acquired on the crewmebers of Increment 8 and 9 prior to launch from Bakonur, Russia. Ultrasound examination of the thorax were performed on crewmembers at 30 day intervals (n=??) throughout their flight. Post flight images were acquired on or about landing day 10. Ultrasound images were acquired using the ISS Health Research Facility ultrasound system and examined by experts on the ground to rule out the presence of pneumothorax and hemothorax. Results: The presence of "lung sliding" which excludes pneumothorax, was seen in all subjects. The absence of pleural fluid, which excludes hemothorax was seen in all subjects. The optimal position between sonographer and patient under microgravity conditions and the amount and type of training for a non-physician crew medical officer for these procedures was also established for this procedure. Conclusion: Ultrasound can be performed on orbit under microgravity condition to rule thoracic trauma, such as pneumothorax and hemothorax.

Hamilton, Douglas R.↗
NASA NTRS2017

The Potential Impact of Satellite-Retrieved Cloud Parameters on Ground-Level PM2.5 Mass and Composition

Satellite-retrieved aerosol optical properties have been extensively used to estimate ground-level fine particulate matter (PM2.5) concentrations in support of air pollution health effects research and air quality assessment at the urban to global scales. However, a large proportion, approximately 70%, of satellite observations of aerosols are missing as a result of cloud-cover, surface brightness, and snow-cover. The resulting PM2.5 estimates could therefore be biased due to this non-random data missingness. Cloud-cover in particular has the potential to impact ground-level PM2.5 concentrations through complex chemical and physical processes. We developed a series of statistical models using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product at 1 km resolution with information from the MODIS cloud product and meteorological information to investigate the extent to which cloud parameters and associated meteorological conditions impact ground-level aerosols at two urban sites in the US: Atlanta and San Francisco. We find that changes in temperature, wind speed, relative humidity, planetary boundary layer height, convective available potential energy, precipitation, cloud effective radius, cloud optical depth, and cloud emissivity are associated with changes in PM2.5 concentration and composition, and the changes differ by overpass time and cloud phase as well as between the San Francisco and Atlanta sites. A case-study at the San Francisco site confirmed that accounting for cloud-cover and associated meteorological conditions could substantially alter the spatial distribution of monthly ground-level PM2.5 concentrations.

MAIAC AO↗
DOE OSTI2025

Seismicity-constrained fault detection and characterization with a multitask machine learning model

Geological fault detection and characterization are crucial for understanding subsurface dynamics across scales. While methods for fault delineation based on either seismicity location analysis or seismic image reflector discontinuity are well-established, a systematic approach that integrates both data types remains absent. We develop a novel machine learning model that unifies seismic reflector images and seismicity location information to automatically identify geological faults and characterize their geometrical properties. The model encodes a seismic image and a seismicity location image separately, and fuses the encoded features with a spatial-channel attention fusion module to improve the learning of important features in both inputs. We design an automated strategy to generate high-quality synthetic training data and labels. To improve the realism of the seismicity location image, we include random seismicity noise and missing seismicity location associated with some of the faults. We validate the model’s efficacy and accuracy using synthetic data examples and two field data examples. Moreover, we show that fine-tuning the trained model with a small, domain-specific dataset enhances its fidelity for field data applications. The results demonstrate that integrating seismicity location and seismic images into a unified framework allows the end-to-end neural network to achieve higher fidelity and accuracy in delineating subsurface faults and their geometrical properties compared with image-only fault detection methods. Our approach offers an adaptive data-driven tool for geological fault characterization and seismic hazard mitigation, bridging the gap between seismicity location and image-based fault detection methods.

58 GEOSCIENCES↗
DOE OSTI2020

Predicting lake surface water phosphorus dynamics using process-guided machine learning

Phosphorus (P) loading to lakes is degrading the quality and usability of water globally. Accurate predictions of lake P dynamics are needed to understand whole-ecosystem P budgets, as well as the consequences of changing lake P concentrations for water quality. However, complex biophysical processes within lakes, along with limited observational data, challenge our capacity to reproduce short-term lake dynamics needed for water quality predictions, as well as long-term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling, process-guided machine learning (PGML), to produce a phosphorus budget for Lake Mendota (Wisconsin, USA) and to accurately predict epilimnetic phosphorus over a time range of days to decades. In our implementation of PGML, which we term a Process-Guided Recurrent Neural Network (PGRNN), we combine a process-based model for lake P with a recurrent neural network, and then constrain the predictions with ecological principles. We test independently the process-based model, the recurrent neural network, and the PGRNN to evaluate the overall approach. The process-based model accounted for most of the observed pattern in lake P; however it missed the long-term trend in lake P and had the worst performance in predicting winter and summer P in surface waters. The root mean square error (RMSE) for the process-based model, the recurrent neural network, and the PGRNN was 33.0 μg P L -1 , 22.7 μg P L -1 , and 20.7 μg P L -1 , respectively. All models performed better during summer, with RMSE values for the three models (same order) equal to 14.3 μg P L -1 , 10.9 μg P L -1 , and 10.7 μg P L -1 . Although the PGRNN had only marginally better RMSE during summer, it had lower bias and reproduced long-term decreases in lake P missed by the other two models. For all seasons and all years, the recurrent neural network had better predictions than process alone, with root mean square error (RMSE) of 23.8 μg P L -1 and 28.0 μg P L -1 , respectively. The output of PGRNN indicated that new processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve the process model. By using ecological knowledge, as well as the information content of complex data, PGML shows promise as a technique for accurate prediction in messy, real-world ecological dynamics, while providing valuable information that can improve our understanding of process.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Cloud Features Detected by MODIS But Not by CloudSat and CALIOP

The ability to characterize the global cloud cover from space has been greatly enhanced by the availability of MODIS, CloudSat, and CALIOP data. The three sensors provide good complementary information about clouds. In this study, we investigated unexpected observations of certain types of clouds apparent in the MODIS data but not detected by CloudSat and CALIOP. Several examples are presented and generally these undetected clouds are geometrically thin, low-level clouds. In particular, they are located in the Arctic region and have optical thicknesses of less than 14, top height altitudes of below 2.5 km, and layer thickness of less than 1 km. CloudSat may miss such low-level clouds because of its coarse vertical resolution of about 500 m and it has limited sensitivity near the surface. Unexpectedly, CALIOP with a much higher vertical resolution of 30 m also misses these clouds and this is due to the cloud s geometrically thin nature and surface proximity.

Chan, Mark Aaron↗
NASA NTRSDate not supplied

Learning from Past Missions for Today’s Case Studies

As the small spacecraft industry continues to grow, the extensive selection of subsystem parts and service providers can present unique challenges in mission implementation. These challenges include inadequate trade studies, missing lessons learned, and unknown solutions to common pitfalls. Trade studies frequently fall short because products and services ultimately do not meet customer expectations; the actual outcomes of a particular service or technology are not always shared with others seeking similar practices. It is difficult to: quantify the viability and robustness of the desired products and services, truly understand what solutions exist, and account for unique failures from previous smallsat missions. One reason for the lack of information as input for trade studies is that it is problematic to capture and disseminate the relevant lessons learned, experienced anomalies, and programmatic issues in both the laboratory and on-orbit setting as these types of information can be extremely sensitive and are specific to each unique mission. Therefore, they are not always made publicly available. The utilization and sharing of existing tools, databases, and literature can help distribute useful information for future smallsat missions to hopefully avoid these common pitfalls. This paper will provide the required framework for current and future smallsat mission implementation by providing best practices and identifying helpful resources to assist with subsystem parts and services trade study selection.

Small Spacecraft↗
NASA NTRSDate not supplied

Improving GES Disc Data Search and Discovery Through AI Metadata Augmentation

NASA’s Goddard Earth Science (GES) Data and Information Services Center (DISC) is one of twelve data centers in NASA's Science Mission Directorate (SMD), providing vital earth science data to a diverse user base. To enhance the discoverability of this data, GES DISC employs a keyword search system, which leverages scientific keywords embedded in dataset metadata. However, the evolving nature of scientific applications of our data necessitates regular review and augmentation of these keywords. To address this, we developed a service to automatically predict missing science keywords in the metadata. This service constructs a knowledge graph from the latest GES DISC metadata within NASA’s Common Metadata Repository (CMR). Using an open-source library, we trained a machine learning model to predict absent science keywords in the metadata. Our preliminary results indicate that the model has high levels of accuracy at predicting science keywords in the dataset metadata when exposed to data not included in its training. These predicted keywords were then evaluated by GES DISC data curation scientists and compared against other AI tools for metadata augmentation. We aim to enhance the overall usability and accessibility of NASA’s earth science data by implementing this tool in our data curation processes.

Kendall Gilbert↗
DOE OSTI2024

DETERMINATION OF STRUT QUALITY FACTORS IN ADDITIVELY MANUFACTURED LATTICES USING IN-SITU COMPRESSION TESTING µ-CT

In response to the need for an automated, commercial method to qualify additively manufactured (AM) lattice components, an experiment was conducted to evaluate the effects of defective lattice struts on the structural compression strength. Lattice samples with known defective or missing struts were compressed using a Deben CT5000RT and imaged using xray µ-CT. The compressive force and x-ray computed tomography results were compared to defect free standards to evaluate the impact of each defect type on the overall structure’s compressive strength. This analysis will allow for simplifications to Finite Element Analysis (FEA) on AM parts without sacrificing model fidelity. Understanding the contribution of each defect type and severity will also better inform non-destructive evaluation (NDE) personnel of the inspection parameters necessary to detect the smallest feature of importance.

Dinova, Vincent A.↗
DOE OSTI2022

Toward a machine-guided approach to energetic material discovery

In this article, we trained a machine learning (ML) model to connect microstructural details of an energetic material formulation to its performance for the purpose of guiding the discovery of new explosive formulations. Our hypothesis was that the algorithm would robustly learn the training data and produce an accurate surrogate model. Specifically, the algorithm learned the relationship between details of the void size distribution (VSD), initiating shock pressure, and the energetic material performance. We used realistic constraints on the VSD and a range of cases were ingested by a physically informed reactive flow model working within a hydrodynamic solver running on high-performance computing resources. The ML algorithm produced a surrogate model that accurately predicted known test points around the parameter space. In addition to the utility of the model and the process used for its development, we noted interesting comparisons between what we, the authors—subject matter experts, would heuristically conclude from the training data and the surrogate model predictions. We detected nuanced details that were missed by the surrogate model; however, these details are not important to an energetic material formulator. We concluded that the algorithm did indeed robustly learn the training data and produce an accurate surrogate model. We further concluded that the surrogate model is a powerful tool to guide the formulator in the absence of subject matter experts and limited-access computing resources.

42 ENGINEERING↗
NASA NTRS2013

Spacecraft Fire Safety 1956 to 1999: An Annotated Bibliography

Knowledge of fire safety in spacecraft has resulted from over 50 years of investigation and experience in space flight. Current practices and procedures for the operation of the Space Transportation System (STS) shuttle and the International Space Station (ISS) have been developed from this expertise, much of which has been documented in various reports. Extending manned space exploration from low Earth orbit to lunar or Martian habitats and beyond will require continued research in microgravity combustion and fire protection in low gravity. This descriptive bibliography has been produced to document and summarize significant work in the area of spacecraft fire safety that was published between 1956 and July 1999. Although some important work published in the late 1990s may be missing, these citations as well as work since 2000 can generally be found in Web-based resources that are easily accessed and searched. In addition to the citation, each reference includes a short description of the contents and conclusions of the article. The bibliography contains over 800 citations that are cross-referenced both by topic and the authors and editors. There is a DVD that accompanies this bibliography (available by request from the Center for Aerospace Information) containing the full-text articles of selected citations as well as an electronic version of this report that has these citations as active links to their corresponding full-text article.

Friedman, Robert↗
NASA NTRSDate not supplied

Exposure of Highly Reflective Far-Ultraviolet Coatings to LEO Environment for TRL Advancement for the Habitable Worlds Observatory

Over the past several years, our team at the NASA Goddard Space Flight Center (GSFC), in collaboration with the Jet Propulsion Laboratory (JPL) and the Naval Research Laboratory (NRL), have developed several new protected Al coating technologies with improved Far-Ultraviolet (FUV) reflectance performance and more robust environmental stability. These include Al protected with lithium-based fluoride (LiF) coatings such as hot deposited LiF (eLiF), XeF 2 -passivated LiF coatings (XeLiF), Li 3 AlF 6 overcoats, AlF 3 (plasma passivated), and XeF 2 passivated MgF 2 coatings (XeMgF 2 ). However, despite these developments, relatively little experimental information exists regarding the long-term stability of these coatings after simultaneous exposure to the Low Earth Orbit (LEO) environment. To evaluate their environmental durability and advance their Technology Readiness Level (TRL), representative samples of these coating technologies, Al+XeLiF, Al+eLiF, Al+Li 3 AlF 6 , Al+AlF 3 , Al+XeMgF 2 , and bare Al as reference samples, were flown onboard the International Space Station (ISS) as part of the Materials International Space Station Experiment 20 (MISSE-20). The MISSE platform is operated by Aegis Aerospace for relatively long-duration (e.g. 6 months) external exposure experiments in a space environment. The coatings were deposited on ULE and Zerodur substrates, and witness samples deposited on the same coatings runs, but on glass slides were left on Earth as control. A subset of the flight samples were fitted with MgF 2 windows to reduce direct exposure to atomic oxygen (AO) and charged particles, and still let through UV and other types of radiation on these samples. After approximately 6 months of exposure in the forward-facing (RAM) direction, the samples were returned to Earth and characterized by performing FUV/NUV/VIS/NIR reflectance measurements to quantify degradation, spectroscopic ellipsometry to evaluate coating thickness and optical constants, atomic force microscopy (AFM) to quantify surface roughness evolution during the flight in the ISS. The MgF 2 protective windows were also analyzed through FUV transmission measurements. Comparison with the witness samples left on the ground and with the flown bare Al samples (with just the naturally occurring Al 2 O 3 layer) provided a quantitative assessment of coating thickness variations, optical performance degradation, and morphological changes induced by prolonged exposure to the LEO environment.

Far Ultraviolet (FUV)↗
NASA NTRS1990

CSMA/RN: A universal protocol for gigabit networks

Networks must provide intelligent access for nodes to share the communications resources. In the range of 100 Mbps to 1 Gbps, the demand access class of protocols were studied extensively. Many use some form of slot or reservation system and many the concept of attempt and defer to determine the presence or absence of incoming information. The random access class of protocols like shared channel systems (Ethernet), also use the concept of attempt and defer in the form of carrier sensing to alleviate the damaging effects of collisions. In CSMA/CD, the sensing of interference is on a global basis. All systems discussed above have one aspect in common, they examine activity on the network either locally or globally and react in an attempt and whatever mechanism. Of the attempt + mechanisms discussed, one is obviously missing; that is attempt and truncate. Attempt and truncate was studied in a ring configuration called the Carrier Sensed Multiple Access Ring Network (CSMA/RN). The system features of CSMA/RN are described including a discussion of the node operations for inserting and removing messages and for handling integrated traffic. The performance and operational features based on analytical and simulation studies which indicate that CSMA/RN is a useful and adaptable protocol over a wide range of network conditions are discussed. Finally, the research and development activities necessary to demonstrate and realize the potential of CSMA/RN as a universal, gigabit network protocol is outlined.

Foudriat, E. C.↗
NASA NTRS2005

The Small Aircraft Transportation System (SATS), Higher Volume Operations (HVO) Concept and Research

The ability to conduct concurrent, multiple aircraft operations in poor weather at virtually any airport offers an important opportunity for a significant increase in the rate of flight operations, a major improvement in passenger convenience, and the potential to foster growth of operations at small airports. The Small Aircraft Transportation System, (SATS) Higher Volume Operations (HVO) concept is designed to increase capacity at the 3400 non-radar, non-towered airports in the United States where operations are currently restricted to one-in/one-out procedural separation during low visibility or ceilings. The concept s key feature is that pilots maintain their own separation from other aircraft using air-to-air datalink and on-board software within the Self-Controlled Area (SCA), an area of flight operations established during poor visibility and low ceilings around an airport without Air Traffic Control (ATC) services. While pilots self-separate within the SCA, an Airport Management Module (AMM) located at the airport assigns arriving pilots their sequence based on aircraft performance, position, winds, missed approach requirements, and ATC intent. The HVO design uses distributed decision-making, safe procedures, attempts to minimize pilot and controller workload, and integrates with today's ATC environment. The HVO procedures have pilots make their own flight path decisions when flying in Instrument Metrological Conditions (IMC) while meeting these requirements. This paper summarizes the HVO concept and procedures, presents a summary of the research conducted and results, and outlines areas where future HVO research is required. More information about SATS HVO can be found at http://ntrs.nasa.gov.

Baxley, B.↗
DOE OSTI2022

Current Status and Future Prospects for the Light Dark Matter eXperiment

The constituents of dark matter are still unknown, and the viable possibilities span a vast range of masses. The physics community has established searching for sub-GeV dark matter as a high priority and identified accelerator-based experiments as an essential facet of this search strategy. A key goal of the accelerator-based dark matter program is testing the broad idea of thermally produced sub-GeV dark matter through experiments designed to directly produce dark matter particles. The most sensitive way to search for the production of light dark matter is to use a primary electron beam to produce it in fixed-target collisions. The Light Dark Matter eXperiment (LDMX) is an electron-beam fixed-target missing-momentum experiment that realizes this approach and provides unique sensitivity to light dark matter in the sub-GeV range. This contribution provides an overview of the theoretical motivation, the main experimental challenges, how LDMX addresses these challenges, and projected sensitivities. We further describe the capabilities of LDMX to explore other interesting new and standard physics, such as visibly-decaying axion and vector mediators or rare meson decays, and to provide timely electronuclear scattering measurements that will inform the modeling of neutrino-nucleus scattering for DUNE.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗