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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

An Empirical Non-TNT Approach to Launch Vehicle Explosion Modeling

In an effort to increase crew survivability from catastrophic explosions of Launch Vehicles (LV), a study was conducted to determine the best method for predicting LV explosion environments in the near field. After reviewing such methods as TNT equivalence, Vapor Cloud Explosion (VCE) theory, and Computational Fluid Dynamics (CFD), it was determined that the best approach for this study was to assemble all available empirical data from full scale launch vehicle explosion tests and accidents. Approximately 25 accidents or full-scale tests were found that had some amount of measured blast wave, thermal, or fragment explosion environment characteristics. Blast wave overpressure was found to be much lower in the near field than predicted by most TNT equivalence methods. Additionally, fragments tended to be larger, fewer, and slower than expected if the driving force was from a high explosive type event. In light of these discoveries, a simple model for cryogenic rocket explosions is presented. Predictions from this model encompass all known applicable full scale launch vehicle explosion data. Finally, a brief description of on-going analysis and testing to further refine the launch vehicle explosion environment is discussed.

Blackwood, James M.↗
NASA NTRS1991

Target Detection Using Fractal Geometry

The concepts and theory of fractal geometry were applied to the problem of segmenting a 256 x 256 pixel image so that manmade objects could be extracted from natural backgrounds. The two most important measurements necessary to extract these manmade objects were fractal dimension and lacunarity. Provision was made to pass the manmade portion to a lookup table for subsequent identification. A computer program was written to construct cloud backgrounds of fractal dimensions which were allowed to vary between 2.2 and 2.8. Images of three model space targets were combined with these backgrounds to provide a data set for testing the validity of the approach. Once the data set was constructed, computer programs were written to extract estimates of the fractal dimension and lacunarity on 4 x 4 pixel subsets of the image. It was shown that for clouds of fractal dimension 2.7 or less, appropriate thresholding on fractal dimension and lacunarity yielded a 64 x 64 edge-detected image with all or most of the cloud background removed. These images were enhanced by an erosion and dilation to provide the final image passed to the lookup table. While the ultimate goal was to pass the final image to a neural network for identification, this work shows the applicability of fractal geometry to the problems of image segmentation, edge detection and separating a target of interest from a natural background.

Fuller, J. Joseph↗
DOE OSTI2023

Advances in solar forecasting: Computer vision with deep learning

Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.

14 SOLAR ENERGY↗
DOE OSTI2013

UNDERSTANDING TRENDS ASSOCIATED WITH CLOUDS IN IRRADIATED EXOPLANETS

Unlike previously explored relationships between the properties of hot Jovian atmospheres, the geometric albedo and the incident stellar flux do not exhibit a clear correlation, as revealed by our re-analysis of Q0-Q14 Kepler data. If the albedo is primarily associated with the presence of clouds in these irradiated atmospheres, a holistic modeling approach needs to relate the following properties: the strength of stellar irradiation (and hence the strength and depth of atmospheric circulation), the geometric albedo (which controls both the fraction of starlight absorbed and the pressure level at which it is predominantly absorbed), and the properties of the embedded cloud particles (which determine the albedo). The anticipated diversity in cloud properties renders any correlation between the geometric albedo and the stellar flux weak and characterized by considerable scatter. In the limit of vertically uniform populations of scatterers and absorbers, we use an analytical model and scaling relations to relate the temperature-pressure profile of an irradiated atmosphere and the photon deposition layer and to estimate whether a cloud particle will be lofted by atmospheric circulation. We derive an analytical formula for computing the albedo spectrum in terms of the cloud properties, which we compare to the measured albedo spectrum of HD 189733b by Evans et al. Furthermore, we show that whether an optical phase curve is flat or sinusoidal depends on whether the particles are small or large as defined by the Knudsen number. This may be an explanation for why Kepler-7b exhibits evidence for the longitudinal variation in abundance of condensates, while Kepler-12b shows no evidence for the presence of condensates despite the incident stellar flux being similar for both exoplanets. We include an 'observer's cookbook' for deciphering various scenarios associated with the optical phase curve, the peak offset of the infrared phase curve, and the geometric albedo.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2007

The Plane-parallel Albedo Bias of Liquid Clouds from MODIS Observations

In our most advanced modeling tools for climate change prediction, namely General Circulation Models (GCMs), the schemes used to calculate the budget of solar and thermal radiation commonly assume that clouds are horizontally homogeneous at scales as large as a few hundred kilometers. However, this assumption, used for convenience, computational speed, and lack of knowledge on cloud small scale variability, leads to erroneous estimates of the radiation budget. This paper provides a global picture of the solar radiation errors at scales of approximately 100 km due to warm (liquid phase) clouds only. To achieve this, we use cloud retrievals from the instrument MODIS on the Terra and Aqua satellites, along with atmospheric and surface information, as input into a GCM-style radiative transfer algorithm. Since the MODIS product contains information on cloud variability below 100 km we can run the radiation algorithm both for the variable and the (assumed) homogeneous clouds. The difference between these calculations for reflected or transmitted solar radiation constitutes the bias that GCMs would commit if they were able to perfectly predict the properties of warm clouds, but then assumed they were homogeneous for radiation calculations. We find that the global average of this bias is approx.2-3 times larger in terms of energy than the additional amount of thermal energy that would be trapped if we were to double carbon dioxide from current concentrations. We should therefore make a greater effort to predict horizontal cloud variability in GCMs and account for its effects in radiation calculations.

Oreopoulos, Lazaros↗
DOE OSTI2022

Ion energy distribution in an electron beam ion trap inferred from simulations of the trapped ion cloud

For this work, we have inferred the energy distribution of trapped ions in an electron beam ion trap (EBIT) from simulations of the spatial distribution of Fe 13+ ions and a comparison with measured visible light images of the ion cloud. We simulated the cloud of Fe 13+ ions by computing ion trajectories in the EBIT for different ion energy distributions used to initialize the trajectories. We then performed a least-squares fit to infer the ion energy distribution that best reproduced the measured ion cloud. These best-fit distributions were typically non-Maxwellian. For electron beam energies of 395–475 eV and electron beam currents of 1–9 mA, we find that the average ion energy is in the range of 10–300 eV. We also find that the average ion energy increases with increasing beam current approximately as $\langle$E$\rangle$ ≈ 25 I e eV, where I e is the electron beam current in mA. We have also compared our results to Maxwell-Boltzmann-distribution ion clouds. We find that our best-fit non-thermal distributions have an $\langle$E$\rangle$ that is less than half that of the T from the best-fit Maxwell-Boltzmann distributions ($\langle$E$\rangle$/q)/T=0.41 ± 0.05.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1978

Users' instructions for the NASA/MSFC cloud-rise preprocessor program, version 6, and the NASA/MSFC multilayer diffusion program, version 6: Research version for Univac 1108 system

The cloud-rise preprocessor and multilayer diffusion computer programs were used by NASA in predicting concentrations and dosages downwind from normal and abnormal launches of rocket vehicles. These programs incorporated: (1) the latest data for the heat content and chemistry of rocket exhaust clouds; (2) provision for the automated calculation of surface water pH due to deposition of HCl from precipitation scavenging; (3) provision for automated calculation of concentration and dosage parameters at any level within the vertical grounds for which meteorological inputs have been specified; and (4) provision for execution of multiple cases of meteorological data. Procedures used to automatically calculate wind direction shear in a layer were updated.

Bjorklund, J. R.↗
DOE OSTI2018

National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) is a serially complete collection of meteorological and solar irradiance data sets for the United States and a growing list of international locations for 1998-2023. The NSRDB is updated annually and provides foundational information to support U.S. Department of Energy programs, research, industry and the general public. The NSRDB provides time-series data at 30-minute resolution of resource averaged over surface cells of 0.038 degrees in both latitude and longitude, or nominally 4 km in size. Additionally time series data at 5 minutes for the US and 10 minutes for North, Central and South America at 2 km resolution are produced from the next generation of GOES satellites and made available from 2019. The solar radiation values represent the resource available to solar energy systems. The data was created using cloud properties which are generated using the AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) algorithms developed by the University of Wisconsin. Fast all-sky radiation model for solar applications (FARMS) in conjunction with the cloud properties, and aerosol optical depth (AOD) and precipitable water vapor (PWV) from ancillary source are used to estimate solar irradiance (GHI, DNI, and DHI). The Global Horizontal Irradiance (GHI) is computed for clear skies using the REST2 model. For cloud scenes identified by the cloud mask, FARMS is used to compute GHI and FARMS DNI is used to compute the Direct Normal Irradiance (DNI). The PATMOS-X model uses radiance images in visible and infrared channels from the Geostationary Operational Environmental Satellite (GOES) series of geostationary weather satellites. Ancillary variables needed to run REST2 and FARMS (e.g., aerosol optical depth, precipitable water vapor, and albedo) are derived from NASA's Modern Era-Retrospective Analysis (MERRA-2) dataset. Temperature and wind speed data are also derived from MERRA-2 and provided for use in NREL's System Advisor Model (SAM) to compute PV generation.

aerosol↗
NASA NTRS1974

Geographical correlation of earth cloud cover pictures obtained from unoriented satellites

Mathematical models have been developed for the determination of the geographic coordinates of cloud photographs taken from earth satellites. The proposed methods were realized on a Minsk-22 computer, and were used to process the earth cloud pictures obtained from the communications satellite Molyniya 1 and the automatic stations Zond 5 and 7. Experience using these methods showed that the accuracy of orientation angular element determination is on the order of 1 deg.

Solovyev, V. I.↗
NASA NTRS1987

A fast radiation parameterization for atmospheric circulation models

Several currently available broadband parameterizations for longwave and shortwave radiation have been combined to produce a computationally fast radiation parameterization that is well suited for atmospheric circulation models. The main features of the parameterization are the ability to include overlapping partly cloudy layers in the longwave, the use of a delta-Eddington technique to treat clouds in the shortwave, and a computational structure that is amenable to vectorization on supercomputers. Selected results of off-line one-dimensional computations using the code have been compared with more rigorous methods as part of an international intercomparison program and found to be quite accurate.

HARSHVARDHAN↗
NASA NTRS1991

Photochemical ozone production in tropical squall line convection during NASA Global Tropospheric Experiment/Amazon Boundary Layer Experiment 2A

The role of convection was examined in trace gas transport and ozone production in a tropical dry season squall line sampled on August 3, 1985, during NASA Global Tropospheric Experiment/Amazon Boundary Layer Experiment 2A (NASA GTE/ABLE 2A) in Amazonia, Brazil. Two types of analyses were performed. Transient effects within the cloud are examined with a combination of two-dimensional cloud and one-dimensional photochemical modeling. Tracer analyses using the cloud model wind fields yield a series of cross sections of NO(x), CO, and O3 distribution during the lifetime of the cloud; these fields are used in the photochemical model to compute the net rate of O3 production. At noon, when the cloud was mature, the instantaneous ozone production potential in the cloud is between 50 and 60 percent less than in no-cloud conditions due to reduced photolysis and cloud scavenging of radicals. Analysis of cloud inflows and outflows is used to differentiate between air that is undisturbed and air that has been modified by the storm. These profiles are used in the photochemical model to examine the aftereffects of convective redistribution in the 24-hour period following the storm. Total tropospheric column O3 production changed little due to convection because so little NO(x) was available in the lower troposphere. However, the integrated O3 production potential in the 5- to 13-km layer changed from net destruction to net production as a result of the convection. The conditions of the August 3, 1985, event may be typical of the early part of the dry season in Amazonia, when only minimal amounts of pollution from biomass burning have been transported into the region.

Pickering, Kenneth E.↗
NASA NTRSDate not supplied

Entwine Point Tiles for 3D Visualization and Querying of ICESat-2

Point Cloud data from non-optical sensors present challenges in scientific computing in both volume of data and files, even for cloud services environments. As part of the Multi-Mission Algorithm and Analysis Platform (MAAP), a joint open science platform for global biomass modelling, we’ve developed a cloud optimized workflow for using ATL08 (ICESat-2) data as a point cloud. For MAAP, the ATL08 data product is published as Entwine Point Tiles (EPT), allowing users to visualize and query the full extent of this collection interactively without pre-downloading, or preprocessing. The EPT format is a cloud-optimized point cloud data format which re-organizes points into a cloud friendly spatially indexed data structure. MAAP uses AWS S3 to store these point clouds and serves them over OGC specified APIs, 3DTiles for visualization, and WFS for querying. This workflow allows for interactive 3D visualizations in a web browser, including notebook environments and facilitates on the fly subsetting for interactive data exploration, all of which can be applied to other similar sensors.

Alex Mandel↗
DOE OSTI2019

Data automation at light sources

Rapidly growing data volumes at light sources demand increasingly automated data collection, distribution, and analysis processes, in order to enable new scientific discoveries while not overwhelming finite human capabilities. We present here the case for automating and outsourcing light source science using cloud-hosted data automation and enrichment services, institutional computing resources, and high- performance computing facilities to provide cost-effective, scalable, and reliable implementations of such processes. We discuss three specific services that accomplish these goals for data distribution, automation, and transformation. In the first, Globus cloud-hosted data automation services are used to implement data capture, distribution, and analysis workflows for Advanced Photon Source and Advanced Light Source beamlines, leveraging institutional storage and computing. In the second, such services are combined with cloud-hosted data indexing and institutional storage to create a collaborative data publication, indexing, and discovery service, the Materials Data Facility (MDF), built to support a host of informatics applications in materials science. The third integrates components of the previous two projects with machine learning capabilities provided by the Data and Learning Hub for science (DLHub) to enable on-demand access to machine learning models from light source data capture and analysis workflows, and provides simplified interfaces to train new models on data from sources such as MDF on leadership scale computing resources. We draw conclusions about best practices for building next-generation data automation systems for future light sources.

Blaiszik, Ben↗
NASA NTRS1977

Absorption of X-rays in the interstellar medium

The X-ray photoabsorption cross sections (per hydrogen atom) of interstellar clouds and the intercloud medium are calculated separately, taking into account abundance evolution, self-blanketing by grains, molecule formation, and ionization structure. A reference cross section describing a neutral monatomic gas of elements in solar abundances is computed for purposes of comparison, the cross section of the intercloud medium is calculated by assuming that 15% of the hydrogen is ionized, and the cross section of the clouds is computed for the cases where all hydrogen is in either molecular or atomic form. A comparison of the reference cross section with the others shows that a cross section is increased relative to the reference if abundances are enhanced or if molecular hydrogen exists and is decreased if atoms exist in grains or if hydrogen is ionized. It is noted that the total cross section will be a linear combination of the cross sections of the clouds and the intercloud medium and can reflect the characteristics of a particular line of sight.

Ride, S. K.↗
NASA NTRS2011

Photogrammetric Characterization of a Brownout Cloud

Brownout is a dangerous problem for rotorcraft operating in arid and dusty environments such as the current operating theaters in Iraq and Afghanistan. Although the interest in brownout has increased in the past decade, the fundamental physics that govern the shape and size of the cloud are not yet well understood. Many computational and scaled experimental studies have been performed in an attempt to further this understanding and to simulate and predict the brownout cloud formation. However, the phenomenon significantly lacks experimental data, particularly at full-scale, which is needed to help validate the brownout simulations being performed. In an effort to increase the data set needed for this validation, tests were performed at the US Army Yuma Proving Ground using photogrammetry to obtain brownout cloud data of an EH-60L Black Hawk. Particle testing was performed on a sample of sand from the landing zone to gain more understanding on the nature of the soil. The photogrammetry technique applied to obtaining data on the formation and evolution of a brownout cloud was verified in an earlier study. The data for a landing approach was examined in greater detail and enabled velocity components of points on the cloud to be determined, as well as the dimensions of structures within the cloud.

Tanner, Philip E.↗
NASA NTRS2018

Earthdata Cloud Analytics Project

This presentation describes a nascent project in NASA to develop a framework to support end-user analytics of NASA's Earth science data in the cloud. The chief benefit of migrating EOSDIS (Earth Observation System Data and Information Systems) data to the cloud is to position the data next to enormous computing capacity to allow end users to process data at scale. The Earthdata Cloud Analytics project will user a service-based approach to facilitate the infusion of evolving analytics technology and the integration with non-NASA analytics or other complementary functionality at other agencies and in other nations.

data services↗
DOE OSTI2018

Lagrangian condensation microphysics with Twomey CCN activation

We report the development of a novel Lagrangian microphysics methodology for simulations of warm ice-free clouds. The approach applies the traditional Eulerian method for the momentum and continuous thermodynamic fields such as the temperature and water vapor mixing ratio, and uses Lagrangian super-droplets to represent condensed phase such as cloud droplets and drizzle or rain drops. In other applications of the Lagrangian warm-rain microphysics, the super-droplets outside clouds represent unactivated cloud condensation nuclei (CCN) that become activated upon entering a cloud and can further grow through diffusional and collisional processes. The original methodology allows for the detailed study of not only effects of CCN on cloud microphysics and dynamics, but also CCN processing by a cloud. However, when cloud processing is not of interest, a simpler and computationally more efficient approach can be used with super-droplets forming only when CCN is activated and no super-droplet existing outside a cloud. This is possible by applying the Twomey activation scheme where the local supersaturation dictates the concentration of cloud droplets that need to be present inside a cloudy volume, as typically used in Eulerian bin microphysics schemes. Since a cloud volume is a small fraction of the computational domain volume, the Twomey super-droplets provide significant computational advantage when compared to the original super-droplet methodology. Additional advantage comes from significantly longer time steps that can be used when modeling of CCN deliquescence is avoided. Moreover, other formulation of the droplet activation can be applied in case of low vertical resolution of the host model, for instance, linking the concentration of activated cloud droplets to the local updraft speed. This paper discusses the development and testing of the Twomey super-droplet methodology, focusing on the activation and diffusional growth. Details of the activation implementation, transport of super-droplets in the physical space, and the coupling between super-droplets and the Eulerian temperature and water vapor field are discussed in detail. Some of these are relevant to the original super-droplet methodology as well and to the ice phase modeling using the Lagrangian approach. As a computational example, the scheme is applied to an idealized moist thermal rising in a stratified environment, with the original super-droplet methodology providing a benchmark to which the new scheme is compared.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Radiative transfer for a three-dimensional raining cloud

Satellite-sensor-based microwave brightness temperatures for a three-dimensional raining cloud over a reflecting surface are computed by using a radiative transfer model based on the discrete-ordinates solution procedure. The three-dimensional model applied to a plane layer is validated by comparison with results from a one-dimensional model that is available in the literature. Results examining the effects of cloud height, rainfall rate, surface reflectance, rainfall footprint area, and satellite viewing position on one- and three-dimensional brightness temperature calculations are reported. The numerical experiments indicate that, under certain conditions, three-dimensional effects are significant in the analysis of satellite-sensor-based rainfall retrieval algorithms. The results point to the need to consider carefully three-dimensional effects as well as surface reflectance effects when interpreting satellite-measured radiation data.

Haferman, J. L.↗