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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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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Spacelab J: Microgravity and life sciences

Spacelab J is a joint venture between NASA and the National Space Development Agency of Japan (NASDA). Using a Spacelab pressurized long module, 43 experiments will be performed in the areas of microgravity and life sciences. These experiments benefit from the microgravity environment available on an orbiting Shuttle. Removed from the effects of gravity, scientists will seek to observe processes and phenomena impossible to study on Earth, to develop new and more uniform mixtures, to study the effects of microgravity and the space environment on living organisms, and to explore the suitability of microgravity for certain types of research. Mission planning and an overview of the experiments to be performed are presented. Orbital research appears to hold many advantages for microgravity science investigations, which on this mission include electronic materials, metals and alloys, glasses and ceramics, fluid dynamics and transport phenomena, and biotechnology. Gravity-induced effects are eliminated in microgravity. This allows the investigations on Spacelab J to help scientists develop a better understanding of how these gravity-induced phenomena affect both processing and products on Earth and to observe subtle phenomena that are masked in gravity. The data and samples from these investigations will not only allow scientists to better understand the materials but also will lead to improvements in the methods used in future experiments. Life sciences research will collect data on human adaptation to the microgravity environment, investigate ways of assisting astronauts to readapt to normal gravity, explore the effects of microgravity and radiation on living organisms, and gather data on the fertilization and development of organisms in the absence of gravity. This research will improve crew comfort and safety on future missions while helping scientists to further understand the human body.

Source record↗
NASA NTRS1989

Baseline estimation with semidynamic and geometric satellite methods

The potential of a semidynamic method for an estimation of satellite position was investigated in a real data environment, using observations of Lageos collected during the MERIT campaign. In this method, quasi-simultaneous laser range observations of Lageos by pairs of stations were transformed to simultaneous range differences (SRDs), in order to reduce the effects of orbital and observational residual biases. Baselines obtained via the SRD method were compared to those obtained by the geometric method. It was found that, for baselines of regional extent, the SRD method is very efficient and at least as accurate as the more complex dynamic methods.

Dedes, George C.↗
NASA NTRSDate not supplied

Documentarian’s Support Role: Recording Active Research for Outreach

The Space Crop Production Team at NASA’s Kennedy Space Center recently conducted a campaign of parabolic flights to study the best techniques for harvesting and collecting microgreens in microgravity. During this process, the research team invited a student with scientific training to the field sites, contributing as the documentarian and aiding in recording audio visual data. The documentarian was tasked with photographing and filming the preparation of experiments for parabolic flight, hardware installation in the parabolic flight plane, the crew performing their experiments, and the harvesting hardware and samples post-flight. Through having a documentarian on the team during experiments, the processes of problem-solving, teamwork, and data collection were able to be captured. This allows for these methods to be reflected upon post-experiment as a means of improving the procedures for the future. Additionally, the picture and video evidence became data that were analyzed following the flight test. The Space Crop Production team benefited by having a documentarian present, as they were able to develop a 45 second social media video with the intent of being published through official NASA outlets. Longer videos for classroom use are also in work. In addition to outreach usage, the collection of footage and visual data the documentarian provides has the potential to be scientifically published both in video-based scientific journals, and as supplemental data in online journal publications. Capturing research in this manner helps share an unseen side of the research practices with a larger audience and increases the potential for scientific engagement and activation.

Christopher Bermudez↗
NASA NTRS1973

A study to explore the use of orbital remote sensing to determine native arid plant distribution

The author has identified the following significant results. A theory has been developed of a method for determining the reflectivities of natural areas from ERTS-1 data. This method requires the following measurements: (1) ground truth reflectivity data from two different calibration areas; (2) radiance data from ERTS-1 MSS imagery for the same two calibration areas; and (3) radiance data from ERTS-1 MSS imagery for the area(s) in which reflectivity is to be determined. The method takes into account sun angle effects and atmospheric effects on the radiance seen by the space sensor. If certain assumptions are made, the ground truth data collection need not be simultaneous with the ERTS-1 overflight. The method allows the calculation of a conversion factor for converting ERTS-1 MSS radiance measurements of a given overflight to reflectivity values. This conversion factor can be used to determine the reflectivity of any area in the general vicinity of the calibration areas which has a relatively similar overlying atmosphere. This method, or some modification of it, may be useful in ERTS investigations which require the determination of spectral signatures of areas from spacecraft data.

Mcginnies, W. G.↗
DOE OSTI2024

Advancing spatiotemporal forecasts of CO 2 plume migration using deep learning networks with transfer learning and interpretation analysis

Accurate and timely forecasts of CO 2 plume distribution throughout the injection and post-injection phases are crucial for detecting plume migration, assessing leakage risks, and supporting operational decisions in geologic carbon storage (GCS). Current convolutional neural network-based approaches primarily focus on spatial information and overlook temporal dependencies in plume distributions, thus limiting their ability to capture dynamic movement effects and provide accurate predictions of plume migration. In this work, we propose two deep learning models, Auto-Encoder (AE)-LSTM and Encoder-Decoder (ED)-ConvLSTM, each uniquely designed to capture both spatial and temporal features. We apply the proposed methods to forecast the dynamic distribution of CO 2 plumes based on 108 reservoir simulations over a 30-year injection and a 30-year post-injection period. The results indicate that the ED-ConvLSTM model outperforms the AE-LSTM model in accurately predicting the spatiotemporal dynamics of CO 2 plume migration, achieving R 2 values above 0.99. To provide a deeper understanding of these model predictions, we employ a gradient-based explanation method on the trained models. This approach provides insights into the influence of input variables on plume migration forecasts and uncovers the underlying prediction mechanisms of the proposed models. Furthermore, we introduce a transfer learning technique, enabling fast and accurate plume migration forecasting in the post-injection phase by leveraging the trained model during the injection phase. This reduces the necessity for extensive data collection or re-training. In conclusion, the methods proposed in our work enhances the performance and interpretability of CO 2 plume migration forecasts, thereby facilitating informed decision-making throughout the entire lifecycle of GCS applications.

58 GEOSCIENCES↗
DOE OSTI2026

2014 Madison County, Indiana, Heartland in Motion Transportation Study

The 2014 Heartland in Motion Transportation Study collected household demographics and travel information from Madison County, Indiana, residents and evaluated preferred travel options between the county and Indianapolis, Indiana. Resource Systems Group administered the study for the Madison County Council of Governments. Prior to conducting the main survey in February to March 2014, a pilot study was conducted in December 2013 to evaluate the methods, approach, and response rates. The main study data collection occurred while public schools were in session, prior to spring break, and included a household travel survey and stated preference experiments. Households were asked to provide details of their trips for one weekday.

1Hz data↗
NASA NTRS2011

Integration of Evidence Base into a Probabilistic Risk Assessment

INTRODUCTION: A probabilistic decision support model such as the Integrated Medical Model (IMM) utilizes an immense amount of input data that necessitates a systematic, integrated approach for data collection, and management. As a result of this approach, IMM is able to forecasts medical events, resource utilization and crew health during space flight. METHODS: Inflight data is the most desirable input for the Integrated Medical Model. Non-attributable inflight data is collected from the Lifetime Surveillance for Astronaut Health study as well as the engineers, flight surgeons, and astronauts themselves. When inflight data is unavailable cohort studies, other models and Bayesian analyses are used, in addition to subject matters experts input on occasion. To determine the quality of evidence of a medical condition, the data source is categorized and assigned a level of evidence from 1-5; the highest level is one. The collected data reside and are managed in a relational SQL database with a web-based interface for data entry and review. The database is also capable of interfacing with outside applications which expands capabilities within the database itself. Via the public interface, customers can access a formatted Clinical Findings Form (CLiFF) that outlines the model input and evidence base for each medical condition. Changes to the database are tracked using a documented Configuration Management process. DISSCUSSION: This strategic approach provides a comprehensive data management plan for IMM. The IMM Database s structure and architecture has proven to support additional usages. As seen by the resources utilization across medical conditions analysis. In addition, the IMM Database s web-based interface provides a user-friendly format for customers to browse and download the clinical information for medical conditions. It is this type of functionality that will provide Exploratory Medicine Capabilities the evidence base for their medical condition list. CONCLUSION: The IMM Database in junction with the IMM is helping NASA aerospace program improve the health care and reduce risk for the astronauts crew. Both the database and model will continue to expand to meet customer needs through its multi-disciplinary evidence based approach to managing data. Future expansion could serve as a platform for a Space Medicine Wiki of medical conditions.

Saile, Lyn↗
NASA NTRS2007

Bootstrap data methodology for sequential hybrid model building

A method for modeling engine operation comprising the steps of: 1. collecting a first plurality of sensory data, 2. partitioning a flight envelope into a plurality of sub-regions, 3. assigning the first plurality of sensory data into the plurality of sub-regions, 4. generating an empirical model of at least one of the plurality of sub-regions, 5. generating a statistical summary model for at least one of the plurality of sub-regions, 6. collecting an additional plurality of sensory data, 7. partitioning the second plurality of sensory data into the plurality of sub-regions, 8. generating a plurality of pseudo-data using the empirical model, and 9. concatenating the plurality of pseudo-data and the additional plurality of sensory data to generate an updated empirical model and an updated statistical summary model for at least one of the plurality of sub-regions.

Volponi, Allan J.↗
NASA NTRS1982

Conversion of raster coded images to polygonal data structures

A method is presented for converting polygons coded in raster data structures into conventional vector structures to allow the output of scanner-based data collection systems to be input directly to conventional geographic information systems. The method relies on topological principles to (1) uniquely label each polygon in the image and produce an output image in which each pixel is described by the label of the polygon to which it belongs; (2) create line segment components of polygon boundaries, with nodes labeled and the two adjacent polygons identified; and (3) traverse the polygon boundaries by connecting the appropriate adjacent line segments. The conversion capability makes it possible to design systems which automatically convert to the data structure most appropriate for a particular application.

Nichols, D. A.↗
NASA NTRS2022

Four Sub-Jovian-Mass Planets Detected by High-Cadence Microlensing Surveys

Aims. With the aim of finding short-term planetary signals, we investigated the data collected from current high-cadence microlensing surveys. Methods. From this investigation, we found four planetary systems with low planet-to-host mass ratios, including OGLE-2017-BLG- 1691L, KMT-2021-BLG-0320L, KMT-2021-BLG-1303L, and KMT-2021-BLG-1554L. Despite the short durations, ranging from a few hours to a couple of days, the planetary signals were clearly detected by the combined data of the lensing surveys. We found that three of the planetary systems have mass ratios on the order of 10−4 and the other has a mass ratio that is slightly greater than 10−3. Results. The estimated masses indicate that all discovered planets have sub-Jovian masses. The planet masses of KMT-2021-BLG- 0320Lb, KMT-2021-BLG-1303Lb, and KMT-2021-BLG-1554Lb correspond to ∼0.10, ∼0.38, and ∼0.12 times the mass of the Jupiter, and the mass of OGLE-2017-BLG-1691Lb corresponds to that of the Uranus. The estimated mass of the planet host KMT-2021-BLG- 1554L, M host ∼ 0.08 M ⊙ , corresponds to the boundary between a star and a brown dwarf. Besides this system, the host stars of the other planetary systems are low-mass stars with masses in the range of ∼[0.3–0.6] M ⊙ . The discoveries of the planets fully demonstrate the capability of the current high-cadence microlensing surveys in detecting low-mass planets.

planets and satellites: detection↗
DOE OSTI2021

An automated multiplexed turbidometric and data collection system for measuring growth kinetics of anaerobes dependent on gaseous substrates

Standard methods of monitoring the growth kinetics of anaerobic microorganisms are generally impractical when there is a protracted or indeterminate period of active growth, and when high numbers of samples or replications are required. As part of our studies of the adaptive evolution of a simple anaerobic syntrophic mutualism, requiring the characterization of many isolates and alternative syntrophic pairings, here we developed a multiplexed growth monitoring system using a combination of commercially available electronics and custom designed circuitry and materials. This system automatically monitors up to 64 sealed, and as needed pressurized, culture tubes and reports the growth data in real-time through integration with a customized relational database. The utility of this system was demonstrated by resolving minor differences in growth kinetics associated with the adaptive evolution of a simple microbial community comprised of a sulfate reducing bacterium, Desulfovibrio vulgaris, grown in syntrophic association with Methanococcus maripaludis, a hydrogenotrophic methanogen.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Energy resolution of the LZ detector for high-energy electronic recoils

The LUX-ZEPLIN (LZ) detector is a dual-phase liquid xenon time projection chamber (TPC) installed at the Sanford Underground Research Facility (Lead, South Dakota) at a depth of 1478 meters. Although the main objective of LZ is the direct detection of dark matter, its low background environment allows for the search of other rare processes, such as the neutrinoless double beta decay of xenon isotopes 134 Xe and 136 Xe with the respective Q-values of 826 keV and 2458 keV. The sensitivity of the detector to these decays is directly determined by the energy resolution, which, in turn, is degraded by non-uniformities in detector response. In this work, we present a novel method to correct, in the data, the non-uniformity of the light collected by an array of photosensors in a scintillation detector. This method is based on the knowledge of the light response functions of individual photosensors. With these techniques, we report, at a very early phase of the detector operations, a state-of-the-art energy resolution (σ/μ) of (0.67 ± 0.01)% at 2614 keV for the fiducial volume of 5.6 tonnes of liquid xenon.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2016

Augmenting Conceptual Design Trajectory Tradespace Exploration with Graph Theory

Within conceptual design changes occur rapidly due to a combination of uncertainty and shifting requirements. To stay relevant in this fluid time, trade studies must also be performed rapidly. In order to drive down analysis time while improving the information gained by these studies, surrogate models can be created to represent the complex output of a tool or tools within a specified tradespace. In order to create this model however, a large amount of data must be collected in a short amount of time. By this method, the historical approach of relying on subject matter experts to generate the data required is schedule infeasible. However, by implementing automation and distributed analysis the required data can be generated in a fraction of the time. Previous work focused on setting up a tool called multiPOST capable of orchestrating many simultaneous runs of an analysis tool assessing these automated analyses utilizing heuristics gleaned from the best practices of current subject matter experts. In this update to the previous work, elements of graph theory are included to further drive down analysis time by leveraging data previously gathered. It is shown to outperform the previous method in both time required, and the quantity and quality of data produced.

Dees, Patrick D.↗
NASA NTRS2016

Augmenting Conceptual Design Trajectory Tradespace Exploration with Graph Theory

Within conceptual design changes occur rapidly due to a combination of uncertainty and shifting requirements. To stay relevant in this fluid time, trade studies must also be performed rapidly. In order to drive down analysis time while improving the information gained by these studies, surrogate models can be created to represent the complex output of a tool or tools within a specified tradespace. In order to create this model however, a large amount of data must be collected in a short amount of time. By this method, the historical approach of relying on subject matter experts to generate the data required is schedule infeasible. However, by implementing automation and distributed analysis the required data can be generated in a fraction of the time. Previous work focused on setting up a tool called multiPOST capable of orchestrating many simultaneous runs of an analysis tool assessing these automated analyses utilizing heuristics gleaned from the best practices of current subject matter experts. In this update to the previous work, elements of graph theory are included to further drive down analysis time by leveraging data previously gathered. It is shown to outperform the previous method in both time required, and the quantity and quality of data produced.

Dees, Patrick D.↗
NASA NTRS2004

Creating Simulated Microgravity Patient Models

The Medical Operational Support Team (MOST) has been tasked by the Space and Life Sciences Directorate (SLSD) at the NASA Johnson Space Center (JSC) to integrate medical simulation into 1) medical training for ground and flight crews and into 2) evaluations of medical procedures and equipment for the International Space Station (ISS). To do this, the MOST requires patient models that represent the physiological changes observed during spaceflight. Despite the presence of physiological data collected during spaceflight, there is no defined set of parameters that illustrate or mimic a 'space normal' patient. Methods: The MOST culled space-relevant medical literature and data from clinical studies performed in microgravity environments. The areas of focus for data collection were in the fields of cardiovascular, respiratory and renal physiology. Results: The MOST developed evidence-based patient models that mimic the physiology believed to be induced by human exposure to a microgravity environment. These models have been integrated into space-relevant scenarios using a human patient simulator and ISS medical resources. Discussion: Despite the lack of a set of physiological parameters representing 'space normal,' the MOST developed space-relevant patient models that mimic microgravity-induced changes in terrestrial physiology. These models are used in clinical scenarios that will medically train flight surgeons, biomedical flight controllers (biomedical engineers; BME) and, eventually, astronaut-crew medical officers (CMO).

Hurst, Victor↗
DOE OSTI2024

Catalyzing deep decarbonization with federated battery diagnosis and prognosis for better data management in energy storage systems

Industrial data analytics methods play a central role in improving energy storage performance and efficiency, impacting the future of electrified transportation and renewable electricity generation. However, significant challenges hinder the large-scale deployment of batteries. Conventional methods rely on centralized collection and processing of fleet-level data, leading to database size issues and privacy concerns due to potential data breaches. To enable scalable deployment of battery management systems, this article proposes a federated battery diagnosis and prognosis model, which distributes the processing of battery standard current-voltage-time-usage data in a privacy-preserving manner. Instead of transferring the raw data, this approach communicates only the locally processed parameters, thus reducing communication load and preserving data confidentiality. The federated model offers a paradigm shift in battery health management through privacy-preserving distributed methods for battery data processing and lifetime prediction, ensuring the reliable and sustainable deployment of lithium-ion batteries in a rapidly evolving world.

asset health management↗
NASA NTRS1993

A survey for rapid variability among early main-sequence A stars

A survey of nonpeculiar early (A0-A5) main-sequence A stars for rapid, low-amplitude variability is presented. The survey is also an independent test of the main-sequence mass-loss theory proposed by Willson et al. (1986). All observations were gathered with the University of Wisconsin Two-Star Photometer. Several period-searching methods are used to analyze time series of differential-photometric data. This instrument coupled with a computerized high-speed data collection system was used with small telescopes at Pine Bluff Observatory and Table Mountain Observatory. Several period-search methods are used to analyze time series of differential-photometric data. This instrument and data-reduction technique allow millimagnitude light variations to be detected. The survey also discovered several low-amplitude Delta Sct stars, all of which are in or blueward of the recognized instability strip.

Schutt, Randy L.↗
DOE OSTI2023

Confidentiality-preserving machine learning algorithms for soft-failure detection in optical communication networks

Automated fault management is at the forefront of next-generation optical communication networks. The increase in complexity of modern networks has triggered the need for programmable and software-driven architectures to support the operation of agile and self-managed systems. In these scenarios, the European Telecommunications Standards Institute zero-touch network and service management approach is imperative. The need for machine learning algorithms to process the large volume of telemetry data brings safety concerns as distributed cloud-computing solutions become the preferred approach for deploying reliable communication network automation. This paper’s contribution is twofold. First, we propose a simple yet effective method to guarantee the confidentiality of the telemetry data based on feature scrambling. The method allows the operation of third-party computational services without direct access to the full content of the collected data. Additionally, the effectiveness of four unsupervised machine learning algorithms for soft-failure detection is evaluated when applied to the scrambled telemetry data. The methods are based on factor analysis, principal component analysis, nonlinear principal component analysis, and singular value decomposition. Most dimensionality reduction algorithms have the common property that they can maintain similar levels of fault classification performance while hiding the data structure from unauthorized access. Evaluations of the proposed algorithms demonstrate this capability.

97 MATHEMATICS AND COMPUTING↗