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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Human Mars Mission: Launch Window from Earth Orbit

The determination of orbital window characteristics is of major importance in the analysis of human interplanetary missions and systems. The orbital launch window characteristics are directly involved in the selection of mission trajectories, the development of orbit operational concepts, and the design of orbital launch systems. The orbital launch window problem arises because of the dynamic nature of the relative geometry between outgoing (departure) asymptote of the hyperbolic escape trajectory and the earth parking orbit. The orientation of the escape hyperbola asymptotic relative to the earth is a function of time. The required hyperbola energy level also varies with time. In addition, the inertial orientation of the parking orbit is a function of time because of the perturbations caused by the Earth's oblateness. Thus, a coplanar injection onto the escape hyperbola can be made only at a point in time when the outgoing escape asymptote is contained by the plane of parking orbit. Even though this condition may be planned as a nominal situation, it will not generally represent the more probable injection geometry. The general case of an escape injection maneuver performed at a time other than the coplanar time will involve both a path angle and plane change and, therefore, a delta V penalty. Usually, because of the delta V penalty the actual departure injection window is smaller in duration than that determined by energy requirement alone. This report contains the formulation, characteristics, and test cases for five different launch window modes for Earth orbit. These modes are: 1) One impulsive maneuver from a Highly Elliptical Orbit (HEO); 2) Two impulsive maneuvers from a Highly Elliptical Orbit (HEO); 3) One impulsive maneuver from a Low Earth Orbit (LEO); 4) Two impulsive maneuvers form LEO; and 5) Three impulsive maneuvers form LEO. The formulation of these five different launch window modes provides a rapid means of generating realistic parametric data for space exploration studies. Also the formulation provides vector and geometrical data sufficient for use as a good starting point in detail trajectory analysis based on calculus of variations, steepest descent, or parameter optimization program techniques.

Young, Archie↗
NASA NTRS2018

Enriching the Twitter Stream Increasing Data Mining Yield and Quality Using Machine Learning

Social media data streams are important sources of real-time and historical global information for science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we are exploring the Twitter data stream for its potential in augmenting the validation program of NASA Earth science missions, specifically the Global Precipitation Measurement (GPM) mission. We have implemented a tweet processing infrastructure that outputs classified precipitation tweets. Inputs are "passive" tweets, along with a smaller number of tweets from "active" participants, i.e., those knowingly contributing to our effort. The "active" tweets, presumably of higher quality, enrich the Twitter stream. "Active" sources include data scraped from other social media (e.g., public Facebook posts) and data from existing crowdsourcing programs (e.g., mPING reports). In addition, there is likely relevant precipitation information in images and documents that are the end points of links often included in tweets. Information derived from these "active" sources could then be tweeted into the Twitter stream, thus enriching its quality. The objective of our current work is to mine these tweet­ linked images and documents, using neural networks, to increase the information content and quality related to precipitation. For images, we classified them as either precipitation-related or not. For training and validation, we used images obtained via the Google custom search API. We created two models: (1) by training a simple Convolutional Neural Network and (2) by using transfer learning principles to adapt a pre-trained object recognition model. For documents, both those linked to tweets and the tweet contents, we trained Hierarchical Attention Networks to determine precipitation occurrence, type, and intensity. For training and validation, we used a keyword-filtered tweet data set labelled with ground truth data from Dark Sky (an API to retrieve weather-related labels) and the National Severe Storms Laboratory's Multi­ Radar/Multi-Sensor (MRMS) system. Our results demonstrated the efficacy of our machine learning approaches for enriching the Twitter stream, to derive information potentially useful for validation of earth science satellite data.

Albayrak, Arif↗
DOE OSTI2016

Dimensionality Reduction Algorithms for Big Nuclear Data Streams with Application to SCALE Cross Sections

A central problem in performing accurate nuclear reactor simulations is the ability to capture the changes in the few group microscopic cross sections as functions of system state, e.g. moderator density, fuel temperature, or neutron spectrum, and time and/or burnup of the fuel. For most reactor simulation scenarios, this results in huge data stream, which must be interpolated as needed by downstream calculations (e.g., core-wide simulation). This is typically accomplished by using sophisticated interpolating functions to capture the few-group variations over the wide range of conditions expected during operation. To address handling this big data, this paper explores the use of dimensionality reduction (DR) techniques to identify the true dimensionality of the data prior to its use in the calculations. This could significantly reduce the computational cost required for data processing/manipulation, thus reducing a significant portion of the total calculations time. By way of an example, in the current version of the ORIGEN-ARP module, the assembly-averaged microscopic cross sections at different conditions, e.g., fuel enrichments, irradiation history and water density, which are calculated using the TRITON sequence and interpolated on-demand by the ARP module to model assembly depletion for a wide range of assembly types (e.g. Westinghouse 17x17 and GE 9x9) over a wide range of conditions. This approach typically results in ∼1 GB per assembly type, which although not insurmountable, can be impractical for distribution. and it is the premise of this paper that most of this data is heavily correlated and hence can take great benefit of condensation via DR. To demonstrate DR, the proposed algorithm is applied to the nuclear data streams generated by SCALE 6.1. Using the depletion sequence (TRITON), microscopic cross sections are calculated for a mixed oxide (MOX) fuel assembly design at a representative range of fuel enrichments and fuel burnup. In conventional core simulators, multi dimensional interpolation of these cross sections is used to provides neutronics feedback due to local conditions without returning to expensive TRITON calculations for every change. This paper shows that dimensionality reduction can help compress the size of the cross section data without sacrificing the accuracy of their reconstruction at all conditions of interest. This work explores the application of dimensionality reduction algorithms to nuclear data streams typically employed in support of conventional full-core reactor calculations which rely on interpolation of pre-calculated microscopic cross sections. In particular, analysis of the few group MOX cross sections revealed that a large degree of dependencies exist between the few-group cross sections, which can be revealed via range-finding algorithms. Specifically, correlations between different reaction types for the same isotope have been identified, and correlations between different enrichments and correlations across different isotopes have been identified. This paper demonstrates that the reduction errors can be quantified, and they are uncorrelated with the range of variations for the various cross sections. In future work, we will focus on identifying the smallest rank that takes into account all types of correlations across the range of conditions used to model cross section variations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2005

Sensor Data Distribution With Robustness and Reliability: Toward Distributed Components Model

In planetary surface exploration mission, sensor data distribution is required in many aspects, for example, in navigation, scheduling, planning, monitoring, diagnostics, and automation of the field tasks. The challenge is to distribute such data in the robust and reliable way so that we can minimize the errors caused by miscalculations, and misjudgments that based on the error data input in the mission. The ad-hoc wireless network on planetary surface is not constantly connected because of the nature of the rough terrain and lack of permanent establishments on the surface. There are some disconnected moments that the computation nodes will re-associate with different repeaters or access points until connections are reestablished. Such a nature requires our sensor data distribution software robust and reliable with ability to tolerant disconnected moments. This paper presents a distributed components model as a framework to accomplish such tasks. The software is written in Java and utilized the available Java Message Services schema and the Boss implementation. The results of field experimentations show that the model is very effective in completing the tasks.

Alena, Richard L.↗
NASA NTRS1961

Measurements of Sheath Currents and Equilibrium Potential on the Explorer VIII Satellite (1960 xi)

Experimental data were obtained from the Explorer VIII satellite on five parameters pertinent to the problem of the interaction of space vehicles with an ionized atmosphere. The five parameters are: photoemission current due to electrons emitted from the satellite surfaces as a result of solar radiation; electron and positive ion currents due to the diffusion of charged particles from the medium to the spacecraft; the vehicle potential relative to the medium, and the ambient electron temperature. Included in the experimental data is the aspect dependence of the photoemission and diffusion currents. On the basis of the observations, certain characteristics of the satellite's plasma sheath are postulated.

Bourdeau, R. E.↗
NASA NTRS1972

Use of data from space for earth resources exploration and management in Alabama

The University of Alabama, the Geological Survey of Alabama, and the George C. Marshall Space Flight Center are involved in an interagency, interdisciplinary effort to use remotely sensed, multispectral observations to yield improved and timely assessment of earth resources and environmental quality in Alabama. It is the goal of this effort to interpret these data and provide them in a format which is meaningful to and readily usable by agencies, industries, and individuals who are potential users throughout the State.

Lamoreaux, P. E.↗
DOE OSTI2023

Coso Geothermal Spectral Library for Rocks and Minerals

An integrated open mineral spectral library designed to enhance the utility and precision of mineral spectral data for geothermal exploration, developed from a reliable and comprehensive digital dataset for seamless sharing by integrating field data, the USGS spectral library, and pertinent information obtained from Coso geothermal field (Coso) in California. The ASD FieldSpec portable spectrometer was utilized for collecting spectral data, which was subsequently analyzed using the THOR Material Identification tool in ENVI, The Spectral Geologist (TSG) software by CSIRO, and the Fully Constrained Linear Spectral Unmixing algorithm (FCLSU) in MATLAB. Scanning Electron Microscopy (SEM) with a mineralogy-analyzing function was employed to assess the mineral composition of samples, ensuring precise mineralogical analysis. A portable X-ray fluorescence (pXRF) spectrometer was also utilized to gather information on elemental enrichment. A framework for developing spectra data and establishing spectral libraries for various geological cases was proposed within this study. The characteristic spectra of six alteration minerals - alunite, chalcedony, epidote, hematite, kaolinite, and opal - were acquired from Coso samples. The spectral library for the Coso alteration minerals was introduced for further application in academic study or industrial exploration. To browse the Coso Geothermal Spectral data and related figures from spreadsheets: #1 Unzip and store the following items in the same folder. 'Contact Probe Data.zip', 'Sample Photos.zip', and 'Coso spectra of higher-certainity minerals.xlsx'. #2 Open 'Coso spectra of higher-certainity minerals.xlsx'. The hyperlinks in the spreadsheet lead to the folders or figures of: spectra .asd file, spectra ASC II file, spectra plots, and sample photos. The spectra data is raw data without splice correction. Spectra .asd files require particular software to open. (These cannot be opened in GIS software such as ArcGIS.) Spectra ASC II files can be opened in a text editor or spread sheet program.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Inflationary features and shifts in cosmological parameters from Planck 2015 data

We explore the relationship between features in the Planck 2015 temperature and polarization data, shifts in the cosmological parameters, and features from inflation. Residuals in the temperature data at low multipole &ell;, which are responsible for the high H 0 ≈ 70 km s –1 Mpc –1 and low σ 8 $Ω^{1/2}_{m}$ values from &ell; < 1000 in power-law ΛCDM models, are better fit to inflationary features with a 1.9σ preference for running of the running of the tilt or a stronger 99% CL local significance preference for a sharp drop in power around k = 0.004 Mpc –1 in generalized slow roll and a lower H 0 ≈ 67km s –1 Mpc –1 . The same in-phase acoustic residuals at &ell; >1000 that drive the global H 0 constraints and appear as a lensing anomaly also favor running parameters which allow even lower H 0 , but not once lensing reconstruction is considered. Polarization spectra are intrinsically highly sensitive to these parameter shifts, and even more so in the Planck 2015 TE data due to an outlier at &ell; ≈ 165,which disfavors the best fit H 0 ΛCDM solution by more than 2σ, and high H 0 value at almost 3σ. Current polarization data also slightly enhance the significance of a sharp suppression of large-scale power but leave room for large improvements in the future with cosmic variance limited E-mode measurements.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1992

International Ultraviolet Explorer (IUE)

The observation, data reduction, and interpretation of ultraviolet spectra (obtained with the International Ultraviolet Explorer) of Herbig-Haro objects, stellar jets, and (in a few cases) reflection nebulae in star-forming regions is discussed. Intermediate results have been reported in the required semi-annual reports. The observations for this research were obtained in 23 (US1) IUE shifts. The spectra were taken in the low resolution mode with the large aperture. The following topics were investigated: (1) detection of UV spectra of high excitation Herbig-Haro (HH) objects, identification of emission lines, and a preliminary study of the energy distribution of the ultraviolet continuum; (2) details of the continuum energy distribution of these spectra and their possible interpretation; (3) the properties of the reddening (extinction) of HH objects; (4) the possible time variation of strong emission lines in high excitation HH objects; (5) the ultraviolet emission of low excitation HH objects, especially in the fluorescent lines of the H2 molecule; (6) the ultraviolet emission in the peculiar object HH24; (7) the spatial emission distribution of different lines and different parts of the continuum in different HH objects; and (8) some properties of reflection nebula, in the environment of Herbig-Haro objects. Each topic is discussed.

Boehm, Karl-Heinz↗
NASA NTRS2007

Exploring and Visualizing A-Train Instrument Data

The succession of US and international satellites that follow each other in close succession, known as the A-Train, affords an opportunity to atmospheric researchers that no single platform could provide: Increasing the number of observations at any given geographic location.. . a more complete "virtual science platform". However, vertically and horizontally, co-registering and regridding datasets from independently developed missions, Aqua, Calipso, Cloudsat, Parasol, and Aura, so that they can be inter-compared can be daunting to some, and may be repeated by many. Scientists will individually spend much of their time and resources acquiring A-Train datasets of interest residing at various locations, developing algorithms to match up and graph datasets along the A-Train track, and search through large amounts of data for areas and/or phenomena of interest. The aggregate amount of effort that can be expended on repeating pre-science tasks could climb into the tens of millions of dollars. The goal of the A-Train Data Depot (ATDD) is to enable free movement of remotely located A-Train data so that they are combined to create a consolidated vertical view of the Earth's Atmosphere along the A-Train tracks. The innovative approach of analyzing and visualizing atmospheric profiles along the platforms track (i.e., time) is accomplished by through the ATDDs Giovanni data analysis and visualization tool. Giovanni brings together data from Aqua (MODIS, AIRS, AMSR-E), Cloudsat (cloud profiling radar) and Calipso (CALIOP, IIR), as well as the Aura (OMI, MLS, HIRDLS, TES) to create a consolidated vertical view of the Earth's Atmosphere along the A-Train tracks. This easy to learn and use exploration tool will allow users to create vertical profiles of any desired A-Train dataset, for any given time of choice. This presentation shows the power of Giovanni by describing and illustrating how this tool facilitates and aids A-Train science and research. A web based display system Giovanni provides users with the capability of creating co-located profile images of temperature and humidity data from the MODIS, MLS and AIRS instruments for a user specified time and spatial area. In addition, Cloud and Aerosol profiles may also be displayed for the Cloudsat and Caliop instruments. The ability to modify horizontal and vertical axis range, data range and dynamic color range is also provided. Two dimensional strip plots of MODIS, AIRS, OM1 and POLDER parameters, co-located along the Cloudsat reference track, can also be plotted along with the Cloudsat cloud profiling data. Center swath pixels for the same parameters can also be shown as line plots overlaying the Cloudsat or Calipso profile images. Images and subsetted data produced in each analysis run may be downloaded. Users truly can explore and discover data specific to their needs prior to ever transferring data to their analysis tools.

Kempler, S.↗
DOE OSTI2020

My vehicle is a data mine

In this talk we explore how analysis of vehicle data provides information of individual vehicle behaviors, information of other vehicles in the flow of traffic, and insights into the behavior of drivers. Over the last two decades traditional passenger vehicles have been transformed from integrated two-port electrical nodes to cyber-physical systems of communicating computational nodes whose individual state and control variables are shared on a standard controller area network (CAN) bus. As driver assistance systems have crept into vehicles as safety features, driver behaviors can be observed through analysis of the data streams on the CAN bus as these new nodes communicate with one another. The properties of these data streams, as well as architectures and approaches to gather the data, are important to consider when drawing conclusions on the relevance of the data in making decisions at varying levels of the information hierarchy. We will demonstrate several technical challenges associated with these data collection processes, as well as preliminary results that demonstrate application relevance of the data to behavior, traffic, and systems domains.

42 ENGINEERING↗
DOE OSTI2024

Developing a Mapping Inquiry Tool and Geodatabase to Support Characterization of Candidate Geologic Carbon Storage Sites

Poster to showcase the Class VI Mapping Inquiry Tool (C6 MapIT) and support geodatabase available on EDX. C6 MapIT and the underpinning Class VI Data Support Tool Geodatabase serve as comprehensive resources to aid with understanding data availability and supporting the initial stages of Class VI siting and permit applications. In addition to enabling users to explore available spatial data, additional resources are provided to empower users to find more information, such as links to documentation to support project goals, a query tool for USGS publications, and links to relevant sections of the EPA Class VI documentation. Safe and appropriate selection of UIC Class VI well sites is key as the U.S. makes the energy transition. The Class VI Mapping Inquiry Tool and Class VI Data Tool Geodatabase are valuable, intuitive resources that empower commercial and industry stakeholders to explore, analyze, and acquire Class VI related data.

Pantaleone, Scott↗
DOE OSTI2024

RCSB Protein Data Bank: supporting research and education worldwide through explorations of experimentally determined and computationally predicted atomic level 3D biostructures

The Protein Data Bank (PDB) was established as the first open-access digital data resource in biology and medicine in 1971 with seven X-ray crystal structures of proteins. Today, the PDB houses >210 000 experimentally determined, atomic level, 3D structures of proteins and nucleic acids as well as their complexes with one another and small molecules ( e.g. approved drugs, enzyme cofactors). These data provide insights into fundamental biology, biomedicine, bioenergy and biotechnology. They proved particularly important for understanding the SARS-CoV-2 global pandemic. The US-funded Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and other members of the Worldwide Protein Data Bank (wwPDB) partnership jointly manage the PDB archive and support >60 000 `data depositors' (structural biologists) around the world. wwPDB ensures the quality and integrity of the data in the ever-expanding PDB archive and supports global open access without limitations on data usage. The RCSB PDB research-focused web portal at https://www.rcsb.org/ (RCSB.org) supports millions of users worldwide, representing a broad range of expertise and interests. In addition to retrieving 3D structure data, PDB `data consumers' access comparative data and external annotations, such as information about disease-causing point mutations and genetic variations. RCSB.org also provides access to >1 000 000 computed structure models (CSMs) generated using artificial intelligence/machine-learning methods. To avoid doubt, the provenance and reliability of experimentally determined PDB structures and CSMs are identified. Related training materials are available to support users in their RCSB.org explorations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Artificial neural network potentials for mechanics and fracture dynamics of two-dimensional crystals **

Understanding the mechanics and failure of materials at the nanoscale is critical for their engineering and applications. The accurate atomistic modeling of brittle failure with crack propagation in covalent crystals requires a quantum mechanics-based description of individual bond-breaking events. Artificial neural network potentials (NNPs) have emerged to overcome the traditional, physics-based modeling tradeoff between accuracy and accessible time and length scales. Previous studies have shown successful applications of NNPs for describing the structure and dynamics of molecular systems and amorphous or liquid phases of materials. However, their application to deformation and failure processes in materials is still uncommon. In this study, we discuss the apparent limitations of NNPs for the description of deformation and fracture under loadings and propose a way to generate and select training data for their employment in simulations of deformation and fracture simulations of crystals. We applied the proposed approach to 2D crystalline graphene, utilizing the density-functional tight-binding method for more efficient and extensive data generation in place of density functional theory. Then, we explored how the data selection affects the accuracy of the developed artificial NNPs. It revealed that NNP’s reliability should not only be measured based on the total energy and atomic force comparisons for reference structures but also utilize comparisons for physical properties, e.g. stress–strain curves and geometric deformation. In sharp contrast to popular reactive bond order potentials, our optimized NNP predicts straight crack propagation in graphene along both armchair and zigzag (ZZ) lattice directions, as well as higher fracture toughness of ZZ edge direction. Our study provides significant insight into crack propagation mechanisms on atomic scales and highlights strategies for NNP developments of broader materials.

2D materials↗
DOE OSTI2014

The Coal-Seq III Consortium. Advancing the Science of CO 2 Sequestration in Coal Seam and Gas Shale Reservoirs

The Coal-Seq consortium is a government-industry collaborative that was initially launched in 2000 as a U.S. Department of Energy sponsored investigation into CO2 sequestration in deep, unmineable coal seams. The consortium’s objective aimed to advancing industry’s understanding of complex coalbed methane and gas shale reservoir behavior in the presence of multi-component gases via laboratory experiments, theoretical model development and field validation studies. Research from this collaborative effort was utilized to produce modules to enhance reservoir simulation and modeling capabilities to assess the technical and economic potential for CO2 storage and enhanced coalbed methane recovery in coal basins. Coal-Seq Phase 3 expands upon the learnings garnered from Phase 1 & 2, which has led to further investigation into refined model development related to multicomponent equations-of-state, sorption and diffusion behavior, geomechanical and permeability studies, technical and economic feasibility studies for major international coal basins the extension of the work to gas shale reservoirs, and continued global technology exchange. The first research objective assesses changes in coal and shale properties with exposure to CO2 under field replicated conditions. Results indicate that no significant weakening occurs when coal and shale were exposed to CO2, therefore, there was no need to account for mechanical weakening of coal due to the injection of CO2 for modeling. The second major research objective evaluates cleat, Cp, and matrix, Cm, swelling/shrinkage compressibility under field replicated conditions. The experimental studies found that both Cp and Cm vary due to changes in reservoir pressure during injection and depletion under field replicated conditions. Using laboratory data from this study, a compressibility model was developed to predict the pore-volume compressibility, Cp, and the matrix compressibility, Cm, of coal and shale, which was applied to modeling software to enhance model robustness. Research was also conducted to improve algorithms and generalized adsorption models to facilitate realistic simulation of CO2 sequestration in coal seams and shale gas reservoirs. The interaction among water and the adsorbed gases, carbon dioxide (CO2), methane (CH4), and nitrogen (N2) in coalbeds is examined using experimental in situ laboratory techniques to comprehensively model CBM production and CO2 sequestration in coals. An equation of state (EOS) module was developed which is capable of predicting the density of pure components and mixtures involving the wet CBM gases CH4, CO2, and N2 at typical reservoir condition, and is used to inform CO2 injection models. The final research objective examined the effects adsorbed CO2 has on coal strength and permeability. This research studied the weakening or failure of coal by the adsorption of CO2 from empirically derived gas production data to develop models for advanced modeling of permeability changes during CO2 sequestration. The results of this research effort have been used to construct a new and improved model for assessing changes in permeability of coal reservoirs due CO2 injection. The modules developed from these studies and knowledge learned are applied to field validation and basin assessment studies. These data were used to assess the flow and storage of CO2 in a shale reservoir, test newly developed code against large-scale projects, and conduct a basin-oriented review of coal storage potential in the San Juan Basin. The storage potential and flow of CO2 was modeled for shale sequestration of a proprietary Marcellus Shale horizontal gas production well using COMET3 simulation software. Simulation results from five model runs indicate that stored CO2 quantities are linked to the duration of primary production preceding injection. Matrix CO2 saturation is observed to increase in each shale zone after injection with an increase in primary production, and the size of the CO2 plume is also observed to increase in size the longer initial production is sustained. The simulation modules developed around the Coal-Seq experimental work are also incorporated into a pre-existing large-scale numerical simulation model of the Pump Canyon CO2-ECBM pilot in the San Juan Basin. The new model was applied to re-history match the data set to explore the improvements made in permeability prediction against previously published data sets and to validate this module. The assessment of the new data, however, indicates that the impact of the variable Cp is negligible on the overall behavior of the coal for CO2 storage purposes. Applying these new modules, the San Juan Basin and the Marcellus Shale are assessed for their technical ECBM/AGR and CO2 storage potential and the economic potential of these operations. The San Juan Basin was divided into 4 unique geographic zones based on production history, and the Marcellus was divided into nine. Each was assessed based upon each zone’s properties, and simulations were run to assess the potential of full Basin development. Models of a fully developed San Juan Basin suggest the potential for up to 104 Tcf of CO2 storage, and 12.3 Tcf of methane recovery. The Marcellus models suggest 1,248 Tcf of CO2 storage and 924 Tcf of AGR. The economics are deemed favorable where credits cover the cost of CO2 in the San Juan Basin, and in many cases in the Marcellus, but to maximize storage potential, credits need to extend to pay the operator to store CO2.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS2019

Developing a Machine-Learning-Based Processing Framework for Twitter and Other Crowdsourced Data

Crowdsourced data streams such as Twitter and other social media are important sources of real-time and historical global information for Earth science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we have been exploring the Twitter data stream for its potential in augmenting the validation program of NASA's Global Precipitation Measurement (GPM) mission. To realize this potential, we need to increase the information density and enhance the quality of filtered precipitation tweets. We have implemented various components of a machine learning (ML)-based processing infrastructure for crowdsourced data that outputs, in this instance, useful and usable information derived from precipitation tweets. We have test enriched the Twitter stream with higher quality active tweets from those knowingly contributing to our effort and from existing crowdsourced programs (e.g., mPING, CoCoRaHS). We have experimented with various algorithms for processing tweets, including Naà ve Bayes, Convolutional Neural Network (CNN), Hierarchical Attention Network (HAN), and semi-supervised learning (with tri-training). Our current work focuses on (1) automated review of Earth science-related publications to determine relationships between discipline research needs and ML algorithms; (2) investigating Sequential Generative Adversarial Network (SeqGAN) for processing precipitation tweets for anomaly detection; and (3) managing crowdsourced data in a way that is compatible with existing NASA satellite data archives and using the data for ML applications. Key results include (1) network visualization of NLP-processed publications in various Earth science disciplines; (2) difference between GPM-linked, generated tweets and collected actual tweets that is small for GPM-determined light to moderate rain cases and high for GPM-determined heavy rain cases; and (3) identification of MongoDB for storing raw tweets and Zarr format for gridded tweets (compatible with GPM data). Our results have taken us a step closer to an operational ML-based tweet processing infrastructure and have already demonstrated that tweet-derived precipitation information is potentially useful for validation of Earth science satellite data.

Teng, William↗
NASA NTRS2024

Teachers’ Use and Adaptation of A Model-Based Climate Curriculum: A Three-Year Longitudinal Study

Foregrounding climate education in formal science learning environments provides students with opportunities to develop critical climate-related knowledge and skills. However, research has shown many challenges to teaching and learning about Earth’s climate and global climate change (GCC). This longitudinal study aims to establish how secondary science teachers, over time, implement model-based climate curricula in support of students’ climate and GCC education by utilizing EzGCM. The model (EzGCM) is a data-driven, computer-based climate modeling tool use to explore global climate data. Multiple sources of data collection, including teacher interviews, classroom observations, and daily reflections, were employed to address the research question: “How did two teachers’ implementation strategies evolve over the three-year study while utilizing a model-based, climate-focused curriculum?” This study provides insight into how and why these resources [model-based climate education curricula] are utilized in science learning environments, thereby informing ongoing efforts to enhance climate education and, in doing so, preparing the next generation of climate-literate adults prepared to confront this most critical global challenge of our age. The findings showed while both teachers engaged in increasingly model-centric instructional practices, these changes were modest. Furthermore, both teacher’s observed classroom practices were less model-centric than the designed curriculum. Ultimately emphasizing the transition from existing practices to improved ones, rather than seeking the perfect approach, the study offers practical insights that can honestly assist secondary educators in real-world settings by highlighting state of climate education in secondary science classrooms.

Secondary science teaching↗
NASA NTRS1997

Estimating Attitude, Trajectory, and Gyro Biases in an Extended Kalman Filter using Earth Magnetic Field Data from the Rossi X-Ray Timing Explorer

Traditionally satellite attitude and trajectory have been estimated with completely separate systems, using different measurement data. The estimation of both trajectory and attitude for low earth orbit satellites has been successfully demonstrated in ground software using magnetometer and gyroscope data. Since the earth's magnetic field is a function of time and position, and since time is known quite precisely, the differences between the computed and measured magnetic field components, as measured by the magnetometers throughout the entire spacecraft orbit, are a function of both the spacecraft trajectory and attitude errors. Therefore, these errors can be used to estimate both trajectory and attitude. This work further tests the single augmented Extended Kalman Filter (EKF) which simultaneously and autonomously estimates spacecraft trajectory and attitude with data from the Rossi X-Ray Timing Explorer (RXTE) magnetometer and gyro-measured body rates. In addition, gyro biases are added to the state and the filter's ability to estimate them is presented.

Deutschmann, Julie↗