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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Analysis of Physical Properties of Dust Suspended in the Mars Atmosphere

Methods for iteratively determining the infrared optical constants for dust suspended in the Mars atmosphere are described. High quality spectra for wavenumbers from 200 to 2000 1/cm were obtained over a wide range of view angles by the Mariner 9 spacecraft, when it observed a global Martian dust storm in 1971-2. In this research, theoretical spectra of the emergent intensity from Martian dust clouds are generated using a 2-stream source-function radiative transfer code. The code computes the radiation field in a plane-parallel, vertically homogeneous, multiply scattering atmosphere. Calculated intensity spectra are compared with the actual spacecraft data to iteratively retrieve the optical properties and opacity of the dust, as well as the surface temperature of Mars at the time and location of each measurement. Many different particle size distributions a-re investigated to determine the best fit to the data. The particles are assumed spherical and the temperature profile was obtained from the CO2 band shape. Given a reasonable initial guess for the indices of refraction, the searches converge in a well-behaved fashion, producing a fit with error of less than 1.2 K (rms) to the observed brightness spectra. The particle size distribution corresponding to the best fit was a lognormal distribution with a mean particle radius, r(sub m) 0.66 pm, and variance, omega(sup 2) = 0.412 (r(sub eff) = 1.85 microns, v(sub eff) =.51), in close agreement with the size distribution found to be the best fit in the visible wavelengths in recent studies. The optical properties and the associated single scattering properties are shown to be a significant improvement over those used in existing models by demonstrating the effects of the new properties both on heating rates of the Mars atmosphere and in example spectral retrieval of surface characteristics from emission spectra.

Snook, Kelly↗
DOE OSTI2012

Final Report on HOLODEC 2 Technology Readiness Level

During the period of this project, the Holographic Detector for Clouds 2 (HOLODEC 2) instrument has advanced from a laboratory-proven instrument with some initial field testing to a fully flight-tested instrument capable of providing useful cloud microphysics measurements. This can be summarized as 'Technology Readiness Level 8: Technology is proven to work - Actual technology completed and qualified through test and demonstration.' As part of this project, improvements and upgrades have been made to the optical system, the instrument power control system, the data acquisition computer, the instrument control software, the data reconstruction and analysis software, and some of the basic algorithms for estimating basic microphysical variables like droplet diameter. Near the end of the project, the instrument flew on several research flights as part of the IDEAS 2011 project, and a small sample of data from the project is included as an example. There is one caveat in the technology readiness level stated above: the upgrades to the instrument power system were made after the flight testing, so they are not fully field proven. We anticipate that there will be an opportunity to fly the instrument as part of the IDEAS project in fall 2012.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1987

Characteristics of gravity waves generated by surface topography on Venus - Comparison with the VEGA Balloon results

Data from the 1985 VEGA Venus Balloon Mission indicate that the mountainous region known as Aphrodite influences atmospheric motions at balloon float altitudes near 54 km, an altitude located within the middle cloud region. It is shown that stationary gravity waves, generated by surface topography and Doppler-shifted by a wind blowing over the terrain, can propagate upward to the middle cloud layers. Under the right circumstances, waves are amplified considerably in excess of their amplification due to the decrease of density with altitude. The additional amplification is due to resonance that results from variations of static stability and mean zonal wind with altitude. Computed atmospheric propagation characteristics, combined with terrain slopes in Aphrodite estimated from Pioneer Venus radar altimeter data, can be sufficient to produce wind amplitudes at 54 km comparable to those observed by the VEGA-2 balloon as it overflew Aphrodite. The dominant waves have horizontal wavelengths of the order of several hundred kilometers.

Young, Richard E.↗
NASA NTRS2022

A Novel Machine Learning-Based Gap-Filling of Fine-Resolution Remotely Sensed Snow Cover Fraction Data By Combining Downscaling and Regression

Satellite-based remotely sensed observations of snow cover fraction (SCF) can have data gaps in spatially distributed coverage from sensor and orbital limitations. We mitigate these limitations in the example fine-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data by gap-filling using auxiliary 1-km datasets that either aid in downscaling from coarser-resolution (5 km) MODIS SCF wherever not fully covered by clouds, or else by themselves via regression wherever fully cloud covered. This study’s prototype predicts a 1-km version of the 500-m MOD10A1 SCF target. Due to noncollocatedness of spatial gaps even across input and auxiliary datasets, we consider a recent gap-agnostic advancement of partial convolution in computer vision for both training and predictive gap-filling. Partial convolution accommodates spatially consistent gaps across the input images, effectively implementing a two-dimensional masking. To overcome reduced usable data from noncollocated spatial gaps across inputs, we innovate a fully generalized three-dimensional masking in this partial convolution. This enables a valid output value at a pixel even if only a single valid input variable and its value exist in the neighborhood covered by the convolutional filter zone centered around that pixel. Thus, our gap-agnostic technique can use significantly more examples for training (∼67%) and prediction (∼100%), instead of only less than 10% for the previous partial convolution. We train an example simple three-layer legacy super-resolution convolutional neural network (SRCNN) to obtain downscaling and regression component performances that are better than baseline values of either climatology or MOD10C1 SCF as relevant. Our generalized partial convolution can enable multiple Earth science applications like downscaling, regression, classification, and segmentation that were hindered by data gaps.

Soni Yatheendradas↗
NASA NTRS2016

The Matsu Wheel: A Cloud-Based Framework for Efficient Analysis and Reanalysis of Earth Satellite Imagery

Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for cloud-based processing of Earth satellite imagery with practical applications to aid in natural disaster detection and relief. Project Matsu has developed an open source cloud-based infrastructure to process, analyze, and reanalyze large collections of hyperspectral satellite image data using OpenStack, Hadoop, MapReduce and related technologies. We describe a framework for efficient analysis of large amounts of data called the Matsu "Wheel." The Matsu Wheel is currently used to process incoming hyperspectral satellite data produced daily by NASA's Earth Observing-1 (EO-1) satellite. The framework allows batches of analytics, scanning for new data, to be applied to data as it flows in. In the Matsu Wheel, the data only need to be accessed and preprocessed once, regardless of the number or types of analytics, which can easily be slotted into the existing framework. The Matsu Wheel system provides a significantly more efficient use of computational resources over alternative methods when the data are large, have high-volume throughput, may require heavy preprocessing, and are typically used for many types of analysis. We also describe our preliminary Wheel analytics, including an anomaly detector for rare spectral signatures or thermal anomalies in hyperspectral data and a land cover classifier that can be used for water and flood detection. Each of these analytics can generate visual reports accessible via the web for the public and interested decision makers. The result products of the analytics are also made accessible through an Open Geospatial Compliant (OGC)-compliant Web Map Service (WMS) for further distribution. The Matsu Wheel allows many shared data services to be performed together to efficiently use resources for processing hyperspectral satellite image data and other, e.g., large environmental datasets that may be analyzed for many purposes.

0↗
NASA NTRSDate not supplied

Using Federated Learning to Overcome Data Gravity in Space

Humans intend to take longer missions to outer space. Understanding the impact that space has on human health is paramount to the success of these missions. Controlled experiments with model organisms are run to infer the impact of space conditions on human health, but the data these experiments generate are too large to transfer to Earth for building models. The same is true for space-relevant data generated on Earth. Ideally, these datasets should be combined to improve statistical power and model accuracy without having to transfer data. Federated learning is such a method which trains an algorithm across decentralized computing systems, each of which has their own local copy of training and testing data. In this research, made possible by NASA@Work, the AI for Life in Space group at NASA demonstrates the use of federated learning to train an ensemble of causality inference models on a combination of data residing on the International Space Station (ISS) and in the cloud. Our work leverages CRISP, a causal inference platform developed during the 2020 Frontier Development Lab’s “Astronaut Health Challenge.” We also leverage the OpenFL federated learning library which was collaboratively developed at Intel and UPenn. We used publicly available data from the NASA Ames Life Sciences Data Archive to identify features in ionizing radiation experiments as causal of changes in cardiac blood velocity. This research demonstrates, for the first time, the possibility of running machine learning algorithms on datasets separated by astronomical distances. In this experiment, all the data were generated in terra, half of which were transferred to the ISS and analyzed on the Spaceborne Computer. In the future, our research will leverage federated learning on data generated in situ on the ISS with data generated terrestrially to predict the impact of spaceflight on mammalian female reproductive capacity.

James Casaletto↗
NASA NTRS2018

Overview of the Digitization Workflow Post Image Acquisition of Apollo Lunar and Antarctic Meteorite Samples Using Agisoft Photoscan for the NASA 3D Astromaterials Virtual Samples Collection

The 3D Virtual Astromaterials Samples (3DVAS) collection is a multi-year funded project to create a digital database of sixty Apollo Lunar and Antarctic Meteorite samples following non-destructive documentation conservation protocols. After initial image processing, the photos are evaluated and processed using unique structure-from-motion photogrammetric techniques in a high performance modelling software designed to create a 3D model from 2D images: Agisoft Photoscan Pro. Agisoft Photoscan Pro uses image processing algorithms and techniques originating in computer vision to resolve 3D models for accurate and detailed visualization of a subject. The software provides a stepwise process that is tailored per model based on spatial and specular reflectance properties, for example. The process includes: photo alignment, creation of a dense point cloud, mesh, and finally texture. Photo alignment is dependent on model properties. The 3DVAS process requires a special rotation platform with calibrated photogrammetric targets, specific distance rotation protocols, and a contrasting background for alignment and scale accuracy. As a result of the photographic process, alignment will complete with two mirrored hemispheres that, in a sense, represent the 2D images overlapping to create a 3D model. Each dense point cloud is analyzed with provided statistical measures in a gradual selection process to eliminate outliers. The point cloud is reduced to include only data valuable to the final model. When a precise dense point cloud is achieved, a mesh and texture are applied. Each model is scaled with scale bar accuracies within 100 microns. Each sample has its own intimate process for modelling; there is no standard for the parameters required in the final creation of a high resolution model. By processing multiple samples, a skill is gained in practice to allow a close definition of the original sample and will result in the most detailed version of the sample shell. This process completes one-fifth of the 3DVAS protocol for providing accurate digital documentation. Each model shell is merged with X-ray Computed Tomography data to create a full volumetric sample. All 3DVAS data will be served on NASA's Astromaterials Acquisition and Curation website with an early subset of data available in 2019 and the 3D Virtual Astromaterials Samples Collection launch in 2020.

Thomas, Andi B.↗
DOE OSTI2011

Evaluation of Water Permittivity Models From Ground-Based Observations of Cold Clouds at Frequencies Between 23 and 170 GHz

Accurate retrievals of liquid water path (LWP) from passive microwave radiometers rely on the use of radiative transfer models to describe the absorption of radiation by various atmospheric components. When clouds are present, atmospheric absorption is affected by the dielectric properties of liquid water. In this paper, we use measurements from four microwave radiometers to assess four models of the complex permittivity of water. The observations are collected at five frequencies between 23.8 and 170 GHz. The purpose of the study is to compare measurements of microwave absorption with model computations in supercooled liquid clouds that have temperatures between 0 °C and -30 °C. Models of liquid water permittivity in this temperature range suffer from a lack of laboratory measurements and are generally derived from the extrapolation of available data. An additional rationale for this work is to examine to what degree the use of different dielectric models affects the retrieval of LWP in supercooled liquid clouds. Inaccuracies in modeling the water permittivity at low temperatures are likely one of the largest sources of retrieval uncertainty in supercooled clouds, uncertainty that could offset the advantages offered by the enhanced sensitivity of channels at frequencies at and above 90 GHz relative to lower frequencies.

Clound liquid absorption models↗
DOE OSTI2021

Building an AI-enhanced modeling framework to address multiscale predictability challenges

Focal Area(s): Build an AI-enhanced modeling framework that integrates the three focus areas in the solicitation. Science Challenge: The 4M-2N complexities (Multiscale, Multiphysics, Multibody, Multidimension, Non-linearity, and Non-Gaussianality) of atmospheric aerosol-cloud-precipitation-turbulence-radiation system poses physical and computational challenges to further advance predictive models; We plan to address the challenges by developing an AI-enhanced modeling framework that facilitates automated calibration and improvement of subgrid parameterizations, enhance data assimilation of measurements to improve initial and boundary conditions used to drive the physical model, and optimally blends data-driven and physics-based forecasting models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An Update on the Geothermal Data Repository's Data Standards and Pipelines: Geospatial Data and Distributed Acoustic Sensing Data

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has implemented data standards and automated data pipelines for the following data types: 1) drilling data, 2) geospatial datasets, and 3) DAS data. An additional data pipeline is proposed for stimulation data. These data standards and pipelines are intended to improve the real-world applicability of geothermal machine learning outputs through improving the quality of data. More specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, allowing more time to be spent on actual research. By automating this process, the burden of standardization is taken off of the user, overall increasing the availability of standardized data. This paper provides an update on the GDR's transition toward data standardization through automated data pipelines and calls for feedback from the community on how we can improve this process.

cloud-optimized↗
NASA NTRS1980

Application of an automatic cloud tracking technique to Meteosat water vapor and infrared observations

The automatic cloud tracking system was applied to METEOSAT 6.7 micrometers water vapor measurements to learn whether the system can track the motions of water vapor patterns. Data for the midlatitudes, subtropics, and tropics were selected from a sequence of METEOSAT pictures for 25 April 1978. Trackable features in the water vapor patterns were identified using a clustering technique and the features were tracked by two different methods. In flat (low contrast) water vapor fields, the automatic motion computations were not reliable, but in areas where the water vapor fields contained small scale structure (such as in the vicinity of active weather phenomena) the computations were successful. Cloud motions were computed using METEOSAT infrared observations (including tropical convective systems and midlatitude jet stream cirrus).

Endlich, R. M.↗
DOE OSTI2021

Land cover change-induced decline in terrestrial gross primary production over the conterminous United States from 2001 to 2016

As one of the most dynamic aspects of global environmental change, land cover change (LCC) has a profound impact on terrestrial carbon sequestration. However, LCC-induced carbon fluxes are still the most uncertain terms in global and regional carbon budgets. Ecosystem gross primary production (GPP) is the total carbon uptake by vegetation through photosynthesis, serving as a major control on ecosystem function and land carbon balance during and after the modification of the land surface. However, accurately capturing LCC-induced GPP changes requires both high-quality land cover data and controlling for variation driven by other environmental factors such as climate. In this study, we comprehensively examined the effects of LCC on annual GPP trends over the conterminous United States (CONUS) from 2001 to 2016 using the USGS National Land Cover Database, a remote sensing-driven ecosystem model, and the Google Earth Engine cloud computing platform. We designed a series of model experiments to identify LCC effects on GPP by controlling climate effects. During the study period, LCC exerted a strong negative effect on total GPP across the CONUS ([-2.2, -1.8] Tg C yr -2 ), while climate had smaller positive effects ([0.17, 0. 92] Tg C yr -2 ). The LCC-induced reduction of GPP was mainly caused by net forest loss ([-1.98, -1.39] Tg C yr -2 ) and urban expansion ([-2.03, -1.92] Tg C yr -2 ), but was partially offset by increases in crop area ([+0.66, +0.79] Tg C yr -2 ). Ensemble simulations from TRENDY did not capture the strong negative LCC influences on GPP, likely due to limitations of the adopted land use/cover data. Overall, our study provides a novel perspective on LCC-induced GPP changes, which could help to improve our understanding of ecosystem function changes and constrain the estimation of land carbon balance in the context of anthropogenic activity and climate change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Calculations of surface radiation in arid regions - A case study

The surface and satellite data measured during the preliminary field experiment for the Land-Atmosphere Interactions Experiment conducted at the Heihe River basin in western China are used to investigate the difficulties encountered in the derivation of the surface radiation budget in arid regions. The surface radiation is derived by coupling theoretical radiative transfer calculations with satellite cloud retrievals. For cloud-free cases, the modeled downward solar fluxes are systematically larger than the measured fluxes. It is found that the error can be reduced and that good agreement between the computed and measured surface solar fluxes can be obtained by using an aerosol single-scattering albedo of 0.5 and an optical thickness of about 0.2 in the afternoon hours. For all the cases studied when both surface and satellite data are available, the mean errors are 4.3 and -4.7 W/sq m for the net downward surface solar flux and the downward surface IR flux, respectively. The rms errors are 17.4 and 22.1 W/sq m for the respective surface fluxes. The importance of aerosols in surface radiation calculations is underscored.

Chou, Ming-Dah↗
DOE OSTI2022

Exploring Tradeoffs in Federated Learning on Serverless Computing Architectures

Federated learning is driving the development of new techniques to efficiently and securely use data across multiple sites while using diverse resources. One of these techniques is the use of the serverless computing paradigm to abstract away resource specific configurations, allowing federated learning across heterogeneous environments. However, deploying federated learning across edge resources, the cloud, and traditional HPC sites will require specialized approaches in order to best account for the weaknesses and strengths of each resource. In this work, we explore the new tradeoffs presented by managing a federated learning task across heterogeneous resources and demonstrate these tradeoffs with experiments using a serverless federated learning framework.

Baughman, Matt↗
DOE OSTI2015

Web-Based Computational Model Builder for Nuclear Energy Advanced Modeling & Simulation (Phase I Final Report)

Today, the importance of nuclear power in the US is a geopolitical matter as much as it is an economic one. This is due to the notion that increasing the use of nuclear power reduces the US's reliance on imported oil and gas. In order to design future systems and continue operational improvements at existing US plants, advanced modeling and simulation of nuclear power reactors is crucial. A variety of challenges exist to using advanced modeling and simulation in practice, especially for the nuclear energy community and other organizations with fewer computational resources and limited expertise. There is an increasing need of access to highly capable compute, data, and software resources from network systems, which are becoming more administratively restricted from access through installation of traditional client-server applications. The nuclear energy community is not capable of maintaining exponential growth of computing capability and data storage coupled with the necessary end-to-end network bandwidth improvements. Applications that provide higher fidelity with increased physics and decreased assumptions, by their very nature, require more and more computational resources. We believe Web-based, Cloud-enabled, end-to-end simulation software for nuclear energy advanced modeling and simulation to be the solution to these challenges.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2016

Evaluation of the Sensitivity of the Amazonian Diurnal Cycle to Convective Intensity in Reanalyses

Model parameterizations of tropical deep convection are unable to reproduce the observed diurnal and spatial variability of convection in the Amazon, which contributes to climatological biases in the water cycle and energy budget. Convective intensity regimes are defined using percentiles of daily minimum 3-hourly averaged outgoing longwave radiation (OLR) from Clouds and the Earth's Radiant Energy System (CERES). This study compares the observed spatial variability of convective diurnal cycle statistics for each regime to MERRA-2 and ERA-Interim (ERA) reanalysis data sets. Composite diurnal cycle statistics are computed for daytime hours (06:00-21:00 local time) in the wet season (December-January-February). MERRA-2 matches observations more closely than ERA for domain averaged composite diurnal statistics-specifically precipitation. However, ERA reproduces mesoscale features of OLR and precipitation phase associated with topography and the propagation of the coastal squall line. Both reanalysis models are shown to underestimate extreme convection.

Itterly, Kyle F.↗
NASA NTRSDate not supplied

Large-Drop Ice Accretion Test Results for a Large Scale Swept Wing Model

In-flight icing is an important consideration that affects aircraft design, performance and safety. Newer regulations combined with increasing demand to reduce fuel burn, emissions and noise are driving a need for improvements in icing simulation capability. To that end, this paper presents the results of ice accretion testing conducted in the NASA Icing Research Tunnel on a large swept wing section typical of a modern commercial transport. The model was based upon a section of the Common Research Model wing at the 64% semispan station with a streamwise chord length of 136 in. The test conditions were developed with an icing scaling analysis to generate similar conditions for a small MVD = 25 μm and a large MVD = 110 μm. A series of tests were conducted over a range of total temperature from -23.8 ˚C to -1.4 ˚C with all other conditions held constant. The results for the small MVD cloud conditions were consistent with previous work and showed the changing ice morphology from rime ice at colder temperatures to highly 3D scallop ice in the range of -8.7 ˚C to -3.8 ˚C. The results for the large MVD cloud conditions exhibited some differences in the ice accretion morphology from the small MVD conditions. Scallop-like ice features were observed at total temperature of -3.8˚C. The large MVD ice accretion at lower temperatures tended to be smoother than the corresponding ice accretion for small MVD. The thickness of the main ice shape tended to be larger for the small MVD conditions compared to the corresponding large MVD ice shape while the latter had more ice farther downstream. The measured ice mass was approximately equal between the corresponding small and large MVD ice shapes up to a freezing fraction of 0.6. For higher freezing fraction, the large MVD ice shapes weighed more. Cloud MVD variation from 50 μm to 230 μm while holding the scaling parameters constant resulted in very minor differences in the ice shapes, mostly in the upper and lower surface chordwise extents. Ice volume was computed from 3D scan data and used to calculate a ratio of ice mass to volume. The resulting values were in the range of 240 to 455 Kg/m3. While this is consistent with analogous values previously reported in the literature, more data are needed to determine a specific trend in this ratio as a function of freezing fraction and the corresponding governing physical phenomena. This work has resulted in a significant experimental database of ice accretion for small and large MVD conditions applicable to large-scale swept wings.

Icing↗
NASA NTRSDate not supplied

Large-Drop Ice Accretion Test Results for a Large Scale Swept Wing Section

In-flight icing is an important consideration that affects aircraft design, performance and safety. Newer regulations combined with increasing demand to reduce fuel burn, emissions and noise are driving a need for improvements in icing simulation capability. To that end, this paper presents the results of ice accretion testing conducted in the NASA Icing Research Tunnel on a large swept wing typical of a modern commercial transport. The model was based upon a section of the Common Research Model wing at the 64% semispan station with a streamwise chord length of 136 in. The test conditions were developed with an icing scaling analysis to generate similar conditions for a small MVD = 25 μm and a large MVD = 110 μm. A series of tests were conducted over a range of total temperature from -23.8 ˚C to -1.4 ˚C with all other conditions held constant. The results for the small MVD cloud conditions were consistent with previous work and showed the changing ice morphology from rime ice at colder temperatures to highly 3D scallop ice in the range of -8.7 ˚C to -3.8 ˚C. The results for the large MVD cloud conditions exhibited some differences in the ice accretion morphology. Scallop-like ice features were observed at total temperature of -3.8˚C. The large MVD ice accretion at lower temperatures tended to be smoother than the corresponding ice accretion for small MVD. The thickness of the main ice shape tended to be larger for the small MVD conditions compared to the corresponding large MVD ice shape while the latter had more ice farther downstream. The measured ice mass was approximately equal between the corresponding small and large MVD ice shapes up to a freezing fraction of 0.6. For higher freezing fraction, the large MVD ice shapes weighed more. Cloud MVD variation from 50 μm to 230 μm while holding the scaling parameters constant resulted in very minor differences in the ice shapes, mostly in the upper and lower surface chordwise extents. Ice volume was computed from 3D scan data and used to calculate a ratio of ice mass to volume. The resulting values were in the range of 240 to 455 Kg/m3. While this is consistent with analogous values previously reported in the literature, more data are needed to determine a specific trend in this ratio as a function of freezing fraction and the corresponding governing physical phenomena. This work has resulted in a significant experimental database of ice accretion for small and large MVD conditions applicable to large-scale swept wings.

Icing↗