Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Data exploration”

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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Recent developments in lithologic mapping using remote sensing data

Major development trends noted in remote sensing scanners are toward greater spatial and spectral resolution, as well as the acquisition of data over a broader portion of the electromagnetic spectrum. Attention is presently given to representative samples of the product of two new-generation satellite sensors, the Landsat-4 Thematic Mapper and SPOT, as well as the status of airborne scanner research aimed at the exploration of multispectral data in the thermal IR wavelength region (which encompasses the diagnostic spectral features of silicates and carbonates). Testing is underway for scanners having spectral bands as narrow as 0.01 micron in the visible and near-IR, which will be capable of identifying specific minerals.

Abrams, M.↗
NASA NTRS1975

To the outer planets - And onward

A series of missions is discussed to meet expected objectives in the exploration of the major planets and their satellite systems. These candidate missions can then be used to define certain future technology requirements. As an example of this process, a Jovian System Laboratory mission is examined. The concept includes an orbiter capable of delivering, supporting, and analyzing data from atmospheric explorers and satellite landers. System and subsystem technology requirements for such a mission are derived, and the uses of these elements in the total outer-planet exploration system are described.

Burke, J. D.↗
DOE OSTI2022

An idea to explore: How an interdisciplinary undergraduate course exploring a global health challenge in molecular detail enabled science communication and collaboration in diverse audiences

Abstract Communication and collaboration are key science competencies that support sharing of scientific knowledge with experts and non‐experts alike. On the one hand, they facilitate interdisciplinary conversations between students, educators, and researchers, while on the other they improve public awareness, enable informed choices, and impact policy decisions. Herein, we describe an interdisciplinary undergraduate course focused on using data from various bioinformatics data resources to explore the molecular underpinnings of diabetes mellitus (Types 1 and 2) and introducing students to science communication. Building on course materials and original student‐generated artifacts, a series of collaborative activities engaged students, educators, researchers, healthcare professionals and community members in exploring, learning about, and discussing the molecular bases of diabetes. These collaborations generated novel educational materials and approaches to learning and presenting complex ideas about major global health challenges in formats accessible to diverse audiences.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2015

The GEWEX Process Evaluation Study: GEWEX-PROES

Progress in realistically simulating the energy and water cycles in weather and climate models has been slower than desirable. Climate models submitted to the Coupled Model Intercomparison Project Phase-5 (CMIP-5), while more comprehensive than their predecessors, have shown little to no improvement in their biases in simulating key features of the climate system. As a result, uncertainties in global climate and hydrological sensitivities, and in the simulation of regional climate change, have not been reduced significantly. It is time to make use of these opportunities to significantly advance our understanding of key energy and water cycle processes at a wide range of space and time scales, and to provide a more insightful evaluation of the representation of these processes within models. This will require new ways of both analyzing the observations and diagnosing model behavior. The keys to success will be in skillfully combining different data sets and exploring relationships between them, as well as in the ability of models to reproduce those relationships correctly. In response to this challenge, a new GEWEX-wide activity, the GEWEX Process Evaluation Study, will take advantage of the opportunities that the combination of many of the existing data sets provide.

climate models↗
NASA NTRS1991

Automated technologies needed to prevent radioactive materials from reentering the atmosphere

Project SIREN (Search, Intercept, Retrieve, Expulsion Nuclear) was created to identify and evaluate the technologies and operational strategies needed to rendezvous with and capture aerospace radioactive materials (e.g., a distressed or spent space reactor core) before such materials can reenter the terrestrial atmosphere and then to safely move these captured materials to an acceptable space destination for proper disposal. A major component of the current project SIREN effort is the development of an interactive technology model (including a computerized data base) that explores, in building-block fashion, the interaction of the technologies and procedures needed to successfully accomplish a SIREN mission. The SIREN model will include appropriate national and international technology elements--both contemporary and projected into the next century. To obtain maximum flexibility and use, the SIREN technology data base is being programmed for use on 286-class PC's. The major technical elements for a successful SIREN mission include: ground and space-based tracking, launch vehicles of needed payload capacity, telerobotic systems, sensors, capture technologies, and space transport and disposal. However, Project SIREN also will impose specialized requirements including the use of dextrous aerospace systems capable of properly functioning in intense radiation and thermal environments.

Buden, David↗
NASA NTRS1991

Automated technologies needed to prevent radioactive materials from reentering the atmosphere

Project SIREN (Search, Intercept, Retrieve, Expulsion Nuclear) has been created to identify and evaluate the technologies and operational strategies needed to rendezvous with and capture aerospace radioactive materials (e.g., a distressed or spent space reactor core) before such materials can reenter the terrestrial atmosphere and then to safely move these captured materials to an acceptable space destination for proper disposal. A major component of the current Project SIREN effort is the development of an interactive technology model (including a computerized data base) that explores in building block fashion the interaction of the technologies and procedures needed to successfully accomplish a SIREN mission. This SIREN model will include appropriate national and international technology elements-both contemporary and projected into the next century. To permit maximum flexibility and use, the SIREN technology data base is being programmed for use on 386-class PC's.

Buden, David↗
NASA NTRS2019

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (TB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and TB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted TB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic TB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.

Kwon, Yonghwan↗
NASA NTRS2003

Plausible Explanations for Trends in Project Programmatic Findings

This paper investigates several published hypotheses to determine which might explain the observed cyclic pattern in programmatic findings by independent review teams over a ten-year period. The data does not neatly support any of the intra-project theories considered; however, something is obviously triggering these findings. A closer examination of the narratives associated with the programmatic findings did reveal consistency. The recurring themes that appeared with more numerous findings were unclear roles and staffing problems. Other explanations, external to the project teams, were also explored and the data supported a distributed political influence on project performance.

Parsons, Vickie S.↗
NASA NTRS1971

Radio astronomy Explorer-1 observations of the Gum nebula

Complicating factors in the spectrum analysis of the Gum nebula are discussed. These include accounting for the spectrum of supernova remnants in the direction of the nebula, the different absorption laws for radiation from beyond and within the nebula, and the Razin effect. This last results in a low frequency cutoff to the spectrum of synchrotron radiation by particles in a thermal plasma. These factors cause the observer to overestimate the amount of absorption occurring in the nebula. Data from the Explorer 38 satellite are presented for 3.93 and 6.55 MHz. Average optical depth for the nebula at 4 MHz was calculated.

Alexander, J. K.↗
NASA NTRS1972

Nightside electromagnetic response of the moon

The electromagnetic response of the Moon to excitation by the time dependent fluctuations of the interplanetary magnetic field is given for the dark or antisolar hemisphere of the Moon. Six hours of time series data from the Explorer 35 magnetometer and the lunar surface magnetometer on Apollo 12 are used to obtain the Fourier spectral amplitudes of the surface and interplanetary fields from which transfer functions are calculated for the east-west, north-south, and vertical directions at the Apollo site. A critical discussion of lunar conductivity profiles derived from night side radial magnetic field data and vacuum scattering theory is presented. Limitations are shown that there is no evidence for a lunar core as conducting as 0.01 mhos/m.

Schubert, G.↗
NASA NTRS2008

Facilitating Analysis of Multiple Partial Data Streams

Robotic Operations Automation: Mechanisms, Imaging, Navigation report Generation (ROAMING) is a set of computer programs that facilitates and accelerates both tactical and strategic analysis of time-sampled data especially the disparate and often incomplete streams of Mars Explorer Rover (MER) telemetry data described in the immediately preceding article. As used here, tactical refers to the activities over a relatively short time (one Martian day in the original MER application) and strategic refers to a longer time (the entire multi-year MER missions in the original application). Prior to installation, ROAMING must be configured with the types of data of interest, and parsers must be modified to understand the format of the input data (many example parsers are provided, including for general CSV files). Thereafter, new data from multiple disparate sources are automatically resampled into a single common annotated spreadsheet stored in a readable space-separated format, and these data can be processed or plotted at any time scale. Such processing or plotting makes it possible to study not only the details of a particular activity spanning only a few seconds, but also longer-term trends. ROAMING makes it possible to generate mission-wide plots of multiple engineering quantities [e.g., vehicle tilt as in Figure 1(a), motor current, numbers of images] that, heretofore could be found only in thousands of separate files. ROAMING also supports automatic annotation of both images and graphs. In the MER application, labels given to terrain features by rover scientists and engineers are automatically plotted in all received images based on their associated camera models (see Figure 2), times measured in seconds are mapped to Mars local time, and command names or arbitrary time-labeled events can be used to label engineering plots, as in Figure 1(b).

Maimone, Mark W.↗
NASA NTRSDate not supplied

Bringing Research to New Heights: How CASEI Integrates Data Curation, Discovery, and Education in Earth and Atmospheric Science

A challenging aspect of any project is finding all the relevant data and information needed to address the research objective. Searching for data and its contextual metadata can become overwhelming for both undergraduate and graduate students, potentially hindering their work and affecting the scientific discoveries that could be made in the long run. To ease this, the NASA Airborne Data Management Group (ADMG), part of the Interagency Implementation and Advanced Concepts Team (IMPACT), has developed the new Catalog of Archived Suborbital Earth science Investigations (CASEI). CASEI includes a web portal that users, be they professionals or students, can use to search, browse, discover, and locate relevant observations associated with NASA’s airborne and field campaigns. Users are able to query data in a variety of ways (via keywords, locations, timeframe, etc) from one online portal, minimizing the amount of time needed to search. CASEI also allows access to key contextual metadata and data from a wide array of Earth and Atmospheric Science topics such as aerosols and boundary layer processes, as well as ice and glacial properties or processes. Users are able to access the data via DOI links to data set landing pages. This presentation will demonstrate how CASEI can be used for classwork and student research. Teachers can provide CASEI to their students as a tool for their studies, or use it to find data themselves while constructing their curriculums. Additionally, users can leverage CASEI to learn about NASA’s Earth and Atmospheric Science research efforts and to find data relevant for assignments or other research projects. The metadata in CASEI has been carefully curated, and highlights important information about the campaigns and their data. Students can explore and learn about the scientific objectives of the campaigns, as well as descriptions of the campaign’s best research days. Having access to contextual metadata in an easy to understand way can help plant the seeds of new ideas in students at any point in their academic journey. From class projects to theses/dissertations and other research, CASEI is a valuable emerging tool for data discovery, giving access to all users and guiding researchers to NASA’s unique airborne data to answer the burning Earth Science questions of our time.

education↗
NASA NTRS1995

Penetration rates over 30 years in the space age

Experimental data from spacecraft providing impact penetration rates and cratering for metallic targets is reviewed. Data includes NASA Explorers 16 and 23 and the Pegasus series, the second US-UK satellite Ariel 2, Space Shuttle STS-3 (MFE), recovered surfaces on Solar Max Satellite, The Long Duration Exposure Facility (LDEF) and EuReCa TiCCE. Factors concerning exposure to the environment are considered and, especially, material properties which affect the penetration resistance. Reference to a common material, Aluminum alloy 2024-T3, is effected and the data then compared to define firstly an average impact flux over the period. The data is examined, in the context of possible satellite and space debris growth rates, to determine the constancy of the flux. This also provides strong constraints on the current space debris component. It is found that the impact data are consistent with domination by natural meteoroid sources. Growth rates are not evident within the period 1980-1990 and Eureca TiCCE fluxes in 1993, for particles penetrating foils of around 10 microns thickness, supports the constancy of the flux. At larger dimensions the 1993 Eureca TiCCE fluxes show an 8-fold increase but this is considered not inconsistent with the selective exposure to meteoroid streams of a satellite stabilized in heliocentric co-ordinates for an 11 month period.

Mcdonnell, J. A. M.↗
NASA NTRS1996

Parallel Climate Data Assimilation PSAS Package

We have designed and implemented a set of highly efficient and highly scalable algorithms for an unstructured computational package, the PSAS data assimilation package, as demonstrated by detailed performance analysis of systematic runs on up to 512node Intel Paragon. The equation solver achieves a sustained 18 Gflops performance. As the results, we achieved an unprecedented 100-fold solution time reduction on the Intel Paragon parallel platform over the Cray C90. This not only meets and exceeds the DAO time requirements, but also significantly enlarges the window of exploration in climate data assimilations.

PSAS data scalable algorithms Intel Paragon 512nod↗
DOE OSTI2024

Cell‐type‐specific transcriptomics uncovers spatial regulatory networks in bioenergy sorghum stems

SUMMARY Bioenergy sorghum is a low‐input, drought‐resilient, deep‐rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum's 4–5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about cell‐type gene expression and regulation in stems was available to enable engineering. To obtain this information, laser capture microdissection was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell‐type‐specific and cell‐preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell‐type‐specific gene regulatory networks (GRNs) revealed that unique transcription factor families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell‐specific transcriptome data was combined with known secondary cell wall (SCW) networks to identify the GRNs that differentially activate SCW formation in vascular sclerenchyma and epidermal cells. The spatial transcriptomic dataset provides a valuable source of information about the function of different sorghum cell types and GRNs that will enable the engineering of bioenergy sorghum stems, and an interactive web application developed during this project will allow easy access and exploration of the data ( https://mc‐lab.shinyapps.io/lcm‐dataset/ ).

09 BIOMASS FUELS↗
DOE OSTI2023

Scenario Discovery Analysis of Drivers of Solar and Wind Energy Transitions Through 2050

Deep human-Earth system uncertainties and strong multi-sector dynamics make it difficult to anticipate which conditions are most likely to lead to higher or lower adoption of renewable energy, and models project a broad range of future solar and wind energy shares across future scenarios. To elucidate these dynamics, we explore a large data set of scenarios simulated from the Global Change Analysis Model (GCAM) and use scenario discovery to identify the most significant factors affecting solar and wind adoption by mid-century. We generated a data set of over 4,000 scenarios from GCAM by varying 12 different socioeconomic factors at high and low levels, including assumptions about future energy demand, resource costs, and fossil fuel emissions paths, as well as specific technology assumptions including wind and solar backup requirements and storage costs. Using scenario discovery, we assess the most important factors globally and regionally in creating high fractions of solar and wind energy and explore interconnected effects on other systems including water and non-CO 2 emissions. Globally and regionally, we found that solar and wind-related technology costs were the primary drivers of high wind and solar energy adoption, though a few regions depend heavily on other parameters like carbon capture and storage costs, population and gross domestic product trajectories, and fossil fuel costs. We also identify four key paths to high solar and wind energy by mid-century and discuss their tradeoffs in terms of other outcomes.

14 SOLAR ENERGY↗
DOE OSTI2024

Linking Spatiotemporal Biological Data to Predict Harmful Algal Blooms

Cyanobacterial Harmful Algal Blooms (cHABs) have significant impacts on an affected region’s economy, ecology, and human health. The blooms can release toxins that kill fish and poison water for people and animals. The global adverse effects of cHABs are exacerbated by the consequences of climate change and increased pollution. Though the phenomena are well documented, scientists’ efforts to mitigate the damage are hampered by insufficient predictive models and incomplete granular knowledge of cHAB community structure. With a goal of leveraging bioinformatics and machine learning tools to better understand and predict cHABs, we are first exploring water sample data sets. Using nearly four thousand samples from the National Center for Biotechnology Information Sequence Read Archive (NCBI-SRA) across 16 years with latitude and longitude embedded in the metadata, we mapped the location of the samples onto a Lake Erie shape file. We combined information about location, date, and community taxa in the NCBI samples to discover factors that determine cHAB features. The data are separated into three distinct zones, with the majority pooled at the southwest end of the lake and occurring in 2017. The samples are rich in biological data; our next steps are to carry out whole genome sequence analysis and use the community profiles as part of our predictive machine learning model.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Methodology for physics-informed generation of synthetic neutron time-of-flight measurement data

Accurate neutron cross section data are a vital input to the simulation of nuclear systems for a wide range of applications from energy production to national security. The evaluation of experimental data is a key step in producing accurate cross sections. There is a widely recognized lack of reproducibility in the evaluation process due to its artisanal nature and therefore there is a call for improvement within the nuclear data community. This can be realized by automating/standardizing viable parts of the process, namely, parameter estimation by fitting theoretical models to experimental data. This automation effort could greatly benefit from a synthetic data resource. This work leverages problem-specific physics, Monte Carlo sampling, and a general methodology for data synthesis to generate unlimited, labelled experimental cross-section data that is statistically indistinguishable to the observed data. Heuristic and, where applicable, rigorous statistical comparisons to observed data support this claim. The demonstration is based on/limited to transmission measurements at Rensselaer Polytechnic Institute (RPI) and energy-differential cross sections in the resolved resonance region (RRR). An open-source software is published alongside this article that executes the complete methodology to produce high-utility synthetic datasets. The goal of this work is to provide an approach and corresponding tool that will allow the evaluation community to begin exploring more data-driven, ML-based solutions to long-standing challenges in the field.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗