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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 379 records · Page 21

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NASA NTRS1987

Use of topographic and climatological models in a geographical data base to improve Landsat MSS classification for Olympic National Park

An unsupervised computer classification of vegetation/landcover of Olympic National Park and surrounding environs was initially carried out using four bands of Landsat MSS data. The primary objective of the project was to derive a level of landcover classifications useful for park management applications while maintaining an acceptably high level of classification accuracy. Initially, nine generalized vegetation/landcover classes were derived. Overall classification accuracy was 91.7 percent. In an attempt to refine the level of classification, a geographic information system (GIS) approach was employed. Topographic data and watershed boundaries (inferred precipitation/temperature) data were registered with the Landsat MSS data. The resultant boolean operations yielded 21 vegetation/landcover classes while maintaining the same level of classification accuracy. The final classification provided much better identification and location of the major forest types within the park at the same high level of accuracy, and these met the project objective. This classification could now become inputs into a GIS system to help provide answers to park management coupled with other ancillary data programs such as fire management.

Cibula, William G.↗

NASA NTRS2018

A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification

A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high dimensional input space of the training samples into a low dimensional space with the topology relations preserved. This makes SOMs supportive of organizing and visualizing complex data sets and have been pervasively used among numerous disciplines with different applications. Notwithstanding its wide applications, the self-organizing map is perplexed by its inherent randomness, which produces dissimilar SOM patterns even when being trained on identical training samples with the same parameters every time, and thus causes usability concerns for other domain practitioners and precludes more potential users from exploring SOM based applications in a broader spectrum. Motivated by this practical concern, we propose a deterministic approach as a supplement to the standard self-organizing map. In accordance with the theoretical design, the experimental results with satellite cloud data demonstrate the effective and efficient organization as well as simplification capabilities of the proposed approach.

Initialization method↗

NASA NTRS1992

Learning class descriptions from a data base of spectral reflectance with multiple view angles

A learning program has been developed which combines 'learning by example' with the generate-and-test paradigm to furnish a robust learning environment capable of handling error-prone data. The problem is shown to be capable of learning class descriptions from positive and negative training examples of spectral and directional reflectance data taken from soil and vegetation. The program, which used AI techniques to automate very tedious processes, found the sequence of relationships that contained the most important information which could distinguish the classes.

Kimes, Daniel S.↗

NASA NTRS2001

Investigation of Thermospheric and Ionospheric Changes during Ionospheric Storms with Satellite and Ground-Based Data and Modeling

The purpose of this proposed research is to improve our basic understanding of the causes of ionospheric storm behavior in the midlatitude F region ionosphere. This objective will be achieved by detailed comparisons between ground based measurements of the peak electron density (N(sub m)F(sub 2)), Atmosphere Explorer satellite measurements of ion and neutral composition, and output from the Field Line Interhemispheric Plasma (FLIP) model. The primary result will be a better understanding of changes in the neutral densities and ion chemistry during magnetic storms that will improve our capability to model the weather of the ionosphere which will be needed as a basis for ionospheric prediction. Specifically, this study seeks to answer the following questions: (1) To what extent are negative ionospheric storm phases caused by changes in the atomic to molecular ratio? (2) Are the changes in neutral density ratio due to increased N2, or decreased O, or both? (3) Are there other chemical processes (e.g., excited N2) that increase O+ loss rates during negative storms? (4) Do neutral density altitude distributions differed from hydrostatic equilibrium? (5) Why do near normal nighttime densities often follow daytime depletions of electron density; and (6) Can changes in h(sub m)F2 fully account for positive storm phases? To answer these questions, we plan to combine ground-based and space-based measurements with the aid of our ionospheric model which is ideally suited to this purpose. These proposed studies will lead to a better capability to predict long term ionospheric variability, leading to better predictions of ionospheric weather.

Richards, Philip G.↗

NASA NTRS1973

NASIS data base management system: IBM 360 TSS implementation. Volume 6: NASIS message file

The message file for the NASA Aerospace Safety Information System (NASIS) is discussed. The message file contains all the message and term explanations for the system. The data contained in the file can be broken down into three separate sections: (1) global terms, (2) local terms, and (3) system messages. The various terms are defined and their use within the system is explained.

Source record↗

NASA NTRS1973

NASIS data base management system - IBM 360/370 OS MVT implementation. 6: NASIS message file

Source record↗

NASA NTRS1992

The interannual variability of polar cap recessions as a measure of Martian climate and weather: Using Earth-based data to augment the time line for the Mars observer mapping mission

The recessions of the polar ice caps are the most visible and most studied indication of seasonal change on Mars. Circumstantial evidence links these recessions to the seasonal cycles of CO2, water, and dust. The possible advent of a planet encircling storm during the Mars Observer (MO) mission will provide a detailed correlation with a cap recession for that one Martian year. That cap recession will then be compared with other storm and nonstorm years. MO data will also provide a stronger link between cap recessions and the water and CO2 cycles. Cap recession variability might also be used to determine the variability of these cycles. After nearly a century of valiant attempts at measuring polar cap recessions, including Mariner 9 and Viking data, MO will provide the first comprehensive dataset. In contrast to MO, the older data are much less detailed and precise and could be forgotten, except that it will still be the only information on interannual variability. By obtaining simultaneous Earth-based observations (including those from Hubble) during the MO mission, direct comparisons can be made between the datasets.

Martin, L. J.↗

NASA NTRS1981

Data base manipulation for assessment of multiresource suitability and land change

Progress is reported in three tasks which support the overall objectives of renewable resources inventory task of the AgRISTARS program. In the first task, the geometric correction algorithms of the Master Data Processor were investigated to determine the utility of data corrected by this processor for U.S. Forest Service uses. The second task involved investigation of logic to form blobs as a precursor step to automatic change detection involving two dates of LANDSAT data. Some routine procedures for selecting BLOB (spatial averaging) parameters were developed. In the third task, a major effort was made to develop land suitability modeling approches for timber, grazing, and wildlife habitat in support of resource planning efforts on the San Juan National Forest.

Colwell, J.↗

NASA NTRS2003

Secondary Neutron Production from Space Radiation Interactions: Advances in Model and Experimental Data Base Development

For humans engaged in long-duration missions in deep space or near-Earth orbit, the risk from exposure to galactic and solar cosmic rays is an important factor in the design of spacecraft, spacesuits, and planetary bases. As cosmic rays are transported through shielding materials and human tissue components, a secondary radiation field is produced. Neutrons are an important component of that secondary field, especially in thickly-shielded environments. Calculations predict that 50% of the dose-equivalent in a lunar or Martian base comes from neutrons, and a recent workshop held at the Johnson Space Center concluded that as much as 30% of the dose in the International Space Station may come from secondary neutrons. Accelerator facilities provide a means for measuring the effectiveness of various materials in their ability to limit neutron production, using beams and energies that are present in cosmic radiation. The nearly limitless range of beams, energies, and target materials that are present in space, however, means that accelerator-based experiments will not provide a complete database of cross sections and thick-target yields that are necessary to plan and design long-duration missions. As such, accurate nuclear models of neutron production are needed, as well as data sets that can be used to compare with, and verify, the predictions from such models. Improvements in a model of secondary neutron production from heavy-ion interactions are presented here, along with the results from recent accelerator-based measurements of neutron-production cross sections. An analytical knockout-ablation model capable of predicting neutron production from high-energy hadron-hadron interactions (both nucleon-nucleus and nucleus-nucleus collisions) has been previously developed. In the knockout stage, the collision between two nuclei result in the emission of one or more nucleons from the projectile and/or target. The resulting projectile and target remnants, referred to as prefragments, then decay by the emission of nucleons, composites, and gamma rays. Recent improvements to the model have incorporated coalescence effects, which effectively tie up single nucleons in the formation of composites during final-state interactions. Comparison of the improved model s predictions with neutron production data near 0 deg in the CA-40+ H reaction at 357 and 565 MeV/nucleon show marked improvement.

Heilbronn, Lawrence H.↗

NASA NTRS1982

A data based random number generator for a multivariate distribution (using stochastic interpolation)

Let X be a K-dimensional random variable serving as input for a system with output Y (not necessarily of dimension k). given X, an outcome Y or a distribution of outcomes G(Y/X) may be obtained either explicitly or implicity. The situation is considered in which there is a real world data set X sub j sub = 1 (n) and a means of simulating an outcome Y. A method for empirical random number generation based on the sample of observations of the random variable X without estimating the underlying density is discussed.

Thompson, J. R.↗

DOE OSTI2024

ML-based Data Assimilation and History Matching: Application to the IBDP CCS Project

It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficiency. However, the computational cost of existing data assimilation methods can be prohibitively expensive due to the complex physics with multi-component non-isothermal simulation and high dimensionality of large-scale reservoir models. We address this challenge by proposing an accelerated deep learning-based workflow for model calibration and prediction of CO2 plume evolution in the reservoir.The power and efficacy of our workflow is demonstrated by application to the Illinois Basin-Decatur Project (IBDP), a large-scale CO2 storage test in saline aquifer. The data assimilation process is implemented rapidly by the proposed workflow with given field measurements including distributed pressure and temperature sensing (DTS) data at an injection and a monitoring well. CO2 plume evolution is predicted by running the simulations of the calibrated reservoir models.

Nagao, Masahiro↗

NASA NTRS1972

A communication link between the GIM data base and a general application program

Utilizing the extract verb of GIM causes the requested information to be extracted from the GIM base and written on to tape. When the GIM extract has completed, a FORTRAN program is then compiled and executed. This program reads the tape generated by GIM, formats and prints the extracted data on the line printer. When an end of file on the extracted tape is encountered the job is terminated.

Argo, W. V.↗

NASA NTRS1973

NASIS data base management system - IBM 360/370 OS MVT implementation. 5: Retrieval command system reference manual

The retrieval command subsystem reference manual for the NASA Aerospace Safety Information System (NASIS) is presented. The output oriented classification of retrieval commands provides the user with the ability to review a set of data items for verification or inspection as a typewriter or CRT terminal and to print a set of data on a remote printer. Predefined and user-definable data formatting are available for both output media.

Source record↗

NASA NTRS2003

A High Temperature Cyclic Oxidation Data Base for Selected Materials Tested at NASA Glenn Research Center

The cyclic oxidation test results for some 1000 high temperature commercial and experimental alloys have been collected in an EXCEL database. This database represents over thirty years of research at NASA Glenn Research Center in Cleveland, Ohio. The data is in the form of a series of runs of specific weight change versus time values for a set of samples tested at a given temperature, cycle time, and exposure time. Included on each run is a set of embedded plots of the critical data. The nature of the data is discussed along with analysis of the cyclic oxidation process. In addition examples are given as to how a set of results can be analyzed. The data is assembled on a read-only compact disk which is available on request from Materials Durability Branch, NASA Glenn Research Center, Cleveland, Ohio.

Barrett, Charles A.↗

NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗

NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗

DOE OSTI2024

A Framework to Demonstrate a DNP3 Interface With a CIM-Based Data Integration Platform: Preprint

The contemporary electrical grid is characterized by its complexity and abundance of data. A control-rich environment supported by information and communication technologies within an Advanced Distribution Management System (ADMS) presents a viable and cost-effective option for utility companies aiming to implement advanced real-time analytical schemes for monitoring and remotely controlling distribution feeders. Modular platform-based approaches to distribution operations require a structured framework for acquiring field device measurements, performing analytics, converting the setpoint to the correct protocol, and sending it on the appropriate communications network to the field devices. We present the development and deployment of an application service to integrate an open-source standardsbased platform with an ADMS test bed with field devices using the Distributed Network Protocol (DNP3) for data exchange. The step-by-step procedure for establishing the DNP3-Master service on an open-source distribution platform is outlined, comprehensively explaining the Master setup process. Moreover, sample use case results highlight the capabilities of the DNP3- Master service setup. Results demonstrate the scalability and configurability of the DNP3-Master service, making it adaptable for integration with other relevant applications, thus providing potential opportunities for real-world field trials and real-time assessments.

ADMS↗

NASA NTRS1992

Brine shrimp development in space: ground-based data to shuttle flight results

The brine shrimp, Artemia salina, has been used as a model system to assess microgravity effects on developing organisms. Following fertilization and early development, the egg can arrest in early gastrula as a dehydrated cyst stage that is stable to harsh environments over long time periods. When salt water is added, the cysts can reactivate, with embryonic development and egg hatching occurring in about 24 h. A series of larval molts or instars, over about a 2 week period, results in the adult crustacean. We have assessed these developmental events in a closed syringe system, a bioprocessing module, in ground-based studies, and have conducted preliminary in-orbit experiments aboard the Space Shuttle Atlantis during the flights of STS-37 and STS-43. Although the in-flight data are limited, spectacular degrees of development have been achieved.

NASA Discipline Number 93-10↗