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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Large-scale turbulence in the Jovian atmosphere

Voyager 1 and 2 image pairs taken one rotation period (10 hr) apart are analyzed in order to compute average cloud velocities in the Jovian atmosphere, as well as to study the global energetics of the processes, in particular the sign and the magnitude of the energy transfer from the fluctuating to the mean flow. Particular attention is paid to the Great Red Spot phenomenon; it is suggested to have originated as a small vortex extracting energy from the surrounding flow. Since the eddy, presently in its near-equilibrium state, has reached a considerable size, while the mean shear lies close to that of a neutrally stable profile, it can be concluded that the presence of one large eddy precludes the growth of any others in the depleted mean shear.

Mitchell, J. L.↗
NASA NTRS1986

Turbulent dispersion of the icing cloud from spray nozzles used in icing tunnels

To correctly simulate flight in natural icing conditions, the turbulence in an icing simulator must be as low as possible. But some turbulence is required to mix the droplets from the spray nozzles and achieve an icing cloud of uniform liquid water content. The goal for any spray system is to obtain the widest possible spray cloud with the lowest possible turbulence in the test section of a icing tunnel. This investigation reports the measurement of turbulence and the three-dimensional spread of the cloud from a single spray nozzle. The task was to determine how the air turbulence and cloud width are affected by spray bars of quite different drag coefficients, by changes in the turbulence upstream of the spray, the droplet size, and the atomizing air. An ice accretion grid, located 6.3 m downstream of the single spray nozzle, was used to measure cloud spread. Both the spray bar and the grid were located in the constant velocity test section. Three spray bar shapes were tested: the short blunt spray bar used in the NASA Lewis Icing Research Tunnel, a thin 14.6 cm chord airfoil, and a 53 cm chord NACA 0012 airfoil. At the low airspeed (56 km/hr) the ice accretion pattern was axisymmetric and was not affected by the shape of the spray bar. At the high airspeed (169 km/hr) the spread was 30 percent smaller than at the low airspeed. For the widest cloud the spray bars should be located as far upstream in the low velocity plenum of the icing tunnel. Good comparison is obtained between the cloud spread data and predicitons from a two-dimensional cloud mixing computer code using the two equation turbulence (k epsilon g) model.

Marek, C. J.↗
NASA NTRS1990

Description of SAFIRE for ISES

The SAFIRE (Spectroscopy of the Atmosphere using Far Infrared Emission) is a limb emission experiment using a far-IR Fourier transform spectrometer (FTS) and a mid-IR broadband multispectral radiometer covering the range 80 to 1600/cm. The purpose of this experiment is to obtain vertical distributions of temperature and key constituents of O(y), HO(y), NO(y), ClO(y), and BrO(y) families in the stratosphere, mesosphere, and thermosphere. The spectral channels and gases within each channel are summarized. The instrument includes a 48 element (6 x 8) Ge:GA detector array operating at 4 K in the far-IR and a 105 element (7 x 15) HgCdTe array operating at 80 K in the mid-IR. The SAFIRE uses four different scan modes for vertical coverage and resolution to address various scientific requirements. The SAFIRE data reduction will start with the retrieval of temperature profile as a function of pressure using two CO2 channel data. Constituent distributions then are obtained from other channel data using the retrieved temperature profile. The SAFIRE measurements are limited to the region above the tropopause because of radiance saturation by H2O and clouds. The computational capability necessary to process at the instrument data rate is estimated to be 19 MFLOPS for FTS data and 0.02 MFLOPS for radiometer data. It seems, therefore, that the real-time applications of SAFIRE data using an onboard processing device is not feasible. Although a temperature anomaly may be detected from the two CO2 radiometer channels using an onboard processor for the stratosphere, it is not possible to distinguish between CO2 outflux and temperature anomaly. Temperature anomaly does not, therefore, offer tropospheric information useful for real-time application.

Park, Jae H.↗
NASA NTRS1998

Atmospheric Aerosol Chemical Composition Measurements for the Subsonic Aircraft: Contrail and Cloud Effects Special Study (SUCCESS)

We received funding to provide measurements of the chemical composition of aerosols aboard the NASA Ames DC-8 research aircraft during the SUCCESS mission. These measurements were successfully completed and the final data resides in the Cloud I computer archive at NASA Ames Research Center. The interpretation of the data obtained on this mission over the central United States has been published in special issues of Geophysical Research Letters. The papers with the University of New Hampshire as first author constitute this report and summarize the salient features of our data. The paper by Talbot et al. discusses the impact of vertical transport on free tropospheric chemistry over the the central USA in springtime. This transport was a dominant feature of the aerosol chemistry during SUCCESS. The paper by Dibb et al. discusses aerosol chemistry specifically as it related to free tropospheric sulfate related to jet exhaust and surface sources. Somewhat surprisingly, we found that surface sources appeared to dominant the distribution of aerosol sulfate in the free troposphere. In addition to these first authored papers, researchers from the University of New Hampshire were co-authors on numerous other companion papers in the special issues.

Talbot, Robert W.↗
NASA NTRS2017

Trade Study: Storing NASA HDF5/netCDF-4 Data in the Amazon Cloud and Retrieving Data Via Hyrax Server Data Server

This study explored three candidate architectures with different types of objects and access paths for serving NASA Earth Science HDF5 data via Hyrax running on Amazon Web Services (AWS). We studied the cost and performance for each architecture using several representative Use-Cases. The objectives of the study were: Conduct a trade study to identify one or more high performance integrated solutions for storing and retrieving NASA HDF5 and netCDF4 data in a cloud (web object store) environment. The target environment is Amazon Web Services (AWS) Simple Storage Service (S3). Conduct needed level of software development to properly evaluate solutions in the trade study and to obtain required benchmarking metrics for input into government decision of potential follow-on prototyping. Develop a cloud cost model for the preferred data storage solution (or solutions) that accounts for different granulation and aggregation schemes as well as cost and performance trades.We will describe the three architectures and the use cases along with performance results and recommendations for further work.

AWS cost↗
NASA NTRS2017

NASA's EOSDIS Cumulus: Ingesting, Archiving, Managing, and Distributing Earth Science Data from the Commercial Cloud

NASA's Earth Observing System Data and Information System (EOSDIS) has been working towards a vision of a cloud-based, highly-flexible, ingest, archive, management, and distribution system for its ever-growing and evolving data holdings. This system, Cumulus, is emerging from its prototyping stages and is poised to make a huge impact on how NASA manages and disseminates its Earth science data. This talk will outline the motivation for this work, present the achievements and hurdles of the past 18 months and will chart a course for the future expansion of the Cumulus expansion. We will explore on not just the technical, but also the socio-technical challenges that we face in evolving a system of this magnitude into the cloud and how we are rising to meet those challenges through open collaboration and intentional stakeholder engagement.

Earth Scienc↗
NASA NTRS2018

Advanced Analytics and Big Earth Data

NASA's Earth Science Data Systems process, archive and distribute petabytes of Earth Observation data to a variety of end users. These end users will face dramatically increased data size in the near future, bringing about new challenges and opportunities in analyzing those data. One area of particular ferment currently is Machine Learning. Many Machine Learning methods are black boxes, limiting direct insight into the data's properties. However, they can be used for a variety of data enhancement purposes, such as parameter retrieval, data fusion and image classification and segmentation. The Earth Observing System Data and Information System is also evolving to host large data volumes in the cloud, enabling data proximal analysis. As part of this effort, an Analytics framework is being developed to support and enhance user analysis of the data. By using standards based services in the framework, diverse user communities can be served, while also allowing inter-system collaboration in the analysis process.

Cloud Computing↗
NASA NTRS2018

Accessing Data Stored in Amazon S3 Using the Hyrax OPeNDAP Server

For three years we have been investigating data storage and retrieval in the Amazon Cloud, striving to optimize both storage structures and (cache-enhanced retrieval processes. These optimizations are hidden from data users, who simply employ the Data Access Protocol (DAP), a popular, Web-based Application Programmer Interface (API) developed by OPeNDAP. We present our collected findings and their realization in the Hyrax data server. Specific techniques include caching data on spinning disk; accessing sharded data in place; optimizing the organizational structures for data within the flat key-value space of Simple Storage Service (S3); and employing an API extension that permits simultaneous operations on many datasets in a single request.

Data access↗
NASA NTRSDate not supplied

TPSAS-NF1676L-33992-DND

The CERES Science Team integrates and fuses observations from 6 CERES instruments aboard the Terra, Aqua, S-NPP, and NOAA-20 missions with data from more than 20 other unique data sources. Following the November 2017 launch of CERES Flight Model 6 (FM6) onboard NOAA20, CERES has now amassed over 80 instrument-years of valuable Earth radiation budget data. The rapidly growing volume of CERES data coupled with the introduction of new data products alongside improvements to existing science algorithms fosters the requirement for faster, more flexible, and scalable data production and orchestration. New virtualized, cloud-centric compute hardware hosted by the NASA Langley Research Center’s (LaRC) Atmospheric Sciences Data Center (ASDC) provides an ideal environment for these ever-increasing data production demands for CERES. This poster discusses updates to the implementation of the CERES Data Management Team’s (DMT) CERES AuTomAted job Loading sYSTem (CATALYST), a custom data processing workflow engine for CERES, to use on-demand computing resources to perform automated CERES data production processing in a Linux-based container environment. Linux containers provide CERES the flexibility to build multiple production environments in containers tailored for specific workloads and allow effortless provisioning of resources based on the CERES Science Team’s data production requirements.

Thomas N. Hillyer↗
NASA NTRSDate not supplied

NASA Cloud Data Access

Explore the source record for details and available documents.

Cloud Computing↗
NASA NTRSDate not supplied

Towards a Cloud-Based Flight Management System

This paper summarizes the results of an initial concept exploration into the Cloud Flight Management System, or Cloud FMS, idea. Cloud FMS is a concept in which part of the aircraft’s FMS is on the aircraft itself, while another part resides in a ground-based cloud environment. This paper is limited to understanding whether this idea can be practically implemented within the current constraints of air-ground network throughput, availability, reliability, and latency. Simulation and live flight tests were conducted to study this concept, and specific findings that further fine-tune the Cloud FMS concept are provided.

Flight Management System, Cloud Computing, Urban A↗
NASA NTRSDate not supplied

NASA Briefing to Unidata

The NASA report to Unidata for October, 2021 covers several topics of mutual interest to Unidata. These include plans for hosting NASA data in the cloud, data analysis-in-place, and NASA plans for Open Source Science. Major collaborations with other organizations are also discussed.

NetCDF↗
NASA NTRS2022

Satellite-derived Cyanobacteria Frequency and Magnitude in Headwaters & Near-dam Reservoir Surface Waters of the Southern U.S.

Reservoirs are dominant features of the modern hydrologic landscape and provide vital services. However, the unique morphology of reservoirs can create suitable conditions for excessive algae growth and associated cyanobacteria blooms in shallow in-flow reservoir locations by providing warm water environments with relatively high nutrient inputs, deposition, and nutrient storage. Cyanobacteria harmful algal blooms (cyanoHAB) are costly water management issues and bloom recurrence is associated with economic costs and negative impacts to human, animal, and environmental health. As cyanoHAB occurrence varies substantially within different regions of a water body, understanding in-lake cyanoHAB spatial dynamics is essential to guide reservoir monitoring and mitigate potential public exposure to cyanotoxins. Cloud-based computational processing power and high temporal frequency of satellites enables advanced pixel-based spatial analysis of cyanoHAB frequency and quantitative assessment of reservoir headwater in-flows compared to near-dam surface waters of individual reservoirs. Additionally, extensive spatial coverage of satellite imagery allows for evaluation of spatial trends across many dozens of reservoir sites. Surface water cyanobacteria concentrations for sixty reservoirs in the southern U.S. were estimated using 300 m resolution European Space Agency (ESA) Ocean and Land Colour Instrument (OLCI) satellite sensor for a five year period (May 2016–April 2021). Of the reservoirs studied, spatial analysis of OLCI data revealed 98% had more frequent cyanoHAB occurrence above the concentration of >100,000 cells/mL in headwaters compared to near-dam surface waters (P < 0.001). Headwaters exhibited greater seasonal variability with more frequent and higher magnitude cyanoHABs occurring mid-summer to fall. Examination of reservoirs identified extremely high concentration cyanobacteria events (>1,000,000 cells/mL) occurring in 70% of headwater locations while only 30% of near-dam locations exceeded this threshold. Wilcoxon signed-rank tests of cyanoHAB magnitudes using paired-observations (dates with observations in both a reservoir's headwater and near-dam locations) confirmed significantly higher concentrations in headwater versus near-dam locations (p < 0.001).

Amber R Ignatius↗
NASA NTRS2022

Satellite-derived cyanobacteria frequency and magnitude in headwaters & near-dam reservoir surface waters of the Southern U.S.

Reservoirs are dominant features of the modern hydrologic landscape and provide vital services. However, the unique morphology of reservoirs can create suitable conditions for excessive algae growth and associated cyanobacteria blooms in shallow in-flow reservoir locations by providing warm water environments with relatively high nutrient inputs, deposition, and nutrient storage. Cyanobacteria harmful algal blooms (cyanoHAB) are costly water management issues and bloom recurrence is associated with economic costs and negative impacts to human, animal, and environmental health. As cyanoHAB occurrence varies substantially within different regions of a water body, understanding in-lake cyanoHAB spatial dynamics is essential to guide reservoir monitoring and mitigate potential public exposure to cyanotoxins. Cloud-based computational processing power and high temporal frequency of satellites enables advanced pixel-based spatial analysis of cyanoHAB frequency and quantitative assessment of reservoir headwater in-flows compared to near-dam surface waters of individual reservoirs. Additionally, extensive spatial coverage of satellite imagery allows for evaluation of spatial trends across many dozens of reservoir sites. Surface water cyanobacteria concentrations for sixty reservoirs in the southern U.S. were estimated using 300m resolution European Space Agency (ESA) Ocean and Land Colour Instrument (OLCI) satellite sensor for a five year period (May 2016–April 2021). Of the reservoirs studied, spatial analysis of OLCI data revealed 98% had more frequent cyanoHAB occurrence above the concentration of >100,000 cells/mL in headwaters compared to near-dam surface waters (P < 0.001). Headwaters exhibited greater seasonal variability with more frequent and higher magnitude cyanoHABs occurring mid-summer to fall. Examination of reservoirs identified extremely high concentration cyanobacteria events (>1,000,000 cells/mL) occurring in 70% of headwater locations while only 30% of near-dam locations exceeded this threshold. Wilcoxon signed-rank tests of cyanoHAB magnitudes using paired-observations (dates with observations in both a reservoir's headwater and near-dam locations) confirmed significantly higher concentrations in headwater versus near-dam locations (p < 0.001).

cyanobacteria↗
NASA NTRSDate not supplied

The Machine Learning Showroom: Presentation to OCIO Data Science Summit

Artificial Intelligence/Machine Learning (AI/ML) has become an indispensable tool for descriptive, predictive and prescriptive analytics. Demand for AI/ML models at NASA is outpacing Data Scientist staff. The AI/ML Showroom is an effort to empower NASA professionals to evaluate AI/ML solutions for their problems in a scalable self-help manner, relying on coding examples, reference use cases, digital assistant guides, jam sessions, video training, and pre-configured cloud resources.

machine learning↗