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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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NASA NTRS1999

Development of an Advanced Computational Model for OMCVD of Indium Nitride

An advanced computational model is being developed to predict the formation of indium nitride (InN) film from the reaction of trimethylindium (In(CH3)3) with ammonia (NH3). The components are introduced into the reactor in the gas phase within a background of molecular nitrogen (N2). Organometallic chemical vapor deposition occurs on a heated sapphire surface. The model simulates heat and mass transport with gas and surface chemistry under steady state and pulsed conditions. The development and validation of an accurate model for the interactions between the diffusion of gas phase species and surface kinetics is essential to enable the regulation of the process in order to produce a low defect material. The validation of the model will be performed in concert with a NASA-North Carolina State University project.

Cardelino, Carlos A.↗
NASA NTRS2020

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

This paper investigates using machine learning to rapidly develop empirical models suitable for system-level aircraft noise studies. In particular, machine learning is used to train a neural network to predict the noise spectra produced by a round jet near a surface over a range of surface lengths, surface standoff distances, jet Mach numbers, and observer angles. These spectra include two sources, jet-mixing noise and jet-surface interaction (JSI) noise, with different scale factors as well as surface shielding and reflection effects to create a multi- dimensional problem. A second model is then trained using data from three rectangular nozzles to include nozzle aspect ratio in the spectral prediction. The training and validation data are from an extensive jet-surface interaction noise database acquired at the NASA Glenn Research Center's Aero-Acoustic Propulsion Laboratory. Although the number of training and validation points is small compared a typical machine learning application, the results of this investigation show that this approach is viable if the underlying data are well behaved.

Brown, Cliff↗
NASA NTRSDate not supplied

Effect of Rain-Adjusted Satellite Sea Surface Salinity on ENSO Predictions from the GMAO S2S Forecast System

ENSO has a significant impact on climate variability throughout the world and so has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of altimetry and in situ data lead to improved short-term forecasts of the coupled system. However, few studies have focused on improving the near-surface density/mixing through assimilation of satellite sea surface salinity (SSS). For expediency, most projects that do assimilate SSS do so as if these data were observed at the top model layer instead of at the surface. In rainy regions where buoyant water sits as a fresh lens at the surface, this assumption is likely invalid. Therefore, we adjust SSS so that it more accurately represents the salinity at the first model layer. The Rain Impact Model (RIM) uses a simple diffusion model to determine the near surface salinity gradient (i.e., 1 cm to 5 m). Satellite SSS data are modified using this near-surface salinity gradient, so the salinity values are now valid at the first model layer (we call this SSS@5m). We assess the impact of satellite SSS observations for near-surface dynamics within ocean reanalyses and how these impact dynamical ENSO forecasts using the NASA GMAO S2S coupled system. For all reanalysis experiments, all available along-track sea level and in situ observations are assimilated for 2011 to 2020 using the LETKF scheme (Penny et al., 2013). One reanalysis assimilates Aquarius /SMAP SSS as before. An additional reanalysis is performed assimilating the SSS@5m data. Validation statistics are compared for experiments that assimilate SSS (sub-optimally as before) versus the SSS@5m. We also compare results of coupled forecasts that are initialized from these reanalyses in spring. For all but the big La Niña in 2017, all NINO3.4 forecasts were improved by using the RIM. We will show that improved SSS estimates upgrades density and near-surface mixing leading to more accurate coupled air/sea interaction and better forecasts.

ENSO↗
NASA NTRS1984

Influence of precracked specimen configuration and starting stress intensity on the stress corrosion cracking of 4340 steel

Since the pioneer work of Brown (1966), precracked specimens and related fracture mechanics analyses have been extensively used to study stress corrosion cracking. Certain questions arose in connection with initial attempts to prepare standardized recommended practices by ASTM Committee G-1 on Corrosion of Metals. These questions were related to adequacy of test control as it pertains to acceptable limits of variability, and to validity of expressions for stress intensity and crack-surface displacements for both specimen configurations. An interlaboratory test program, was, therefore, planned with the objective to examine the validity of KIscc testing for selected specimen configurations, materials,and environmental systems. The results reported in the present paper include details of a single laboratory test program. The program was conducted to determine if the threshold value of stress intensity for onset and arrest of stress corrosion cracking was independent for the two specimen configurations examined.

Lisagor, W. B.↗
NASA NTRS1972

Investigation of Atmospheric Effects in Image Transfer

The effect of the atmosphere on remote image interpretation and transfer was studied under laboratory conditions. Indoor laboratory measurements were made of the reflection patterns of typical natural surfaces using a dual channel polarizing radiometer. Outdoor ground measurements of these same surfaces were compared to indoor ones to validate the findings. From these data attempts were made to construct an atmospheric remote sensing model that could predict spectral intensity characteristics of the remote image as seen by a particular sensor given the spatial orientation of the sensor, the atmospheric conditions, the time of day, and the type of surface being viewed.

Coulson, K. L.↗
NASA NTRS1976

Application of the adaptive-wall concept to three-dimensional low-speed wind tunnels

Three methods for evaluating the functional relationships required to obtain interference-free flows about a model in a wind tunnel have been developed. The first, the original multipole expansion (MPE) procedure, is based on a series of point singularities which satisfy the governing Prandtl-Glauert equation. The second, the modified MPE, provides an improved representation of finite-span wings and thereby extends the range of validity of the original MPE to larger ratios of span-to-control-surface-width. The third method is more general and is based on source distributions over the control surface. Several numerical examples are presented to help establish the range of validity of these methods. An accuracy-assessment procedure, which combines the original MPE procedure with classical wall-correction theory, has been developed to estimate the degree of interference at the model if the functional relationships are not satisfied exactly. Several numerical examples are presented for representative wings and bodies.

Erickson, J. C., Jr.↗
NASA NTRS1995

Surface measurements of solar irradiance: A study of the spatial correlation between simultaneous measurements at separated sites

Pyranometers have been used for many years to measure broadband surface incoming solar irradiance, data that is necessary for surface energy budget, cloud forcing, and satellite validation research. Because such measurements are made at a specific location, it is unclear how representative they may be of a larger area. This study attempts to determine a reasonable spacing between measurement sites for such research by computing the correlation, and standard deviation from perfect correlation, between simultaneous measurements of incoming solar irradiance for a network of surface measurement sites covering a 75 km x 75 km area. Using 1-min data collected from this network of 11 sites during the NASA First ISSCP Radiation Experiment/Surface Radiation Budget (FIRE/SRB) Project temporal averages were calculated. The correlation between any two of these sites was determined by comparing simultaneous measurement averages for the 55 possible combinations of site pairs, along with the distances between them. In an attempt to remove the effect of the diurnal cycle, thus leaving clouds as the primary influence on correlation of the radiation field, model results for a clear day were used to normalize measured irradiances and correlations were again calculated.

Long, Charles N.↗
NASA NTRSDate not supplied

FloodPlanet: High-Resolution Commercial Imagery for Training and Validation of Deep Learning-Based Models of Inundation Extent

Flooding events are becoming increasingly frequent worldwide and are known to cause extensive damage. Public optical and radar satellite imagery can be used to detect large areas of inundation in rural areas, however, long revisit times and coarse spatial resolution limit applications for short-lived events and urban areas. Commercial constellations such as those operated by Planet offer increased spatial and temporal resolution and can supplement mapping efforts to provide more information to disaster response, relief, and mitigation efforts. Deep learning requires high quality labeled data for training across coincident sensors. The FloodPlanet dataset presented here contains labeled surface water for 18 events across the world based on Planetscope imagery with coincident Harmonized Landsat Sentinel-2 ( HLS) or Sentinel-1 and builds upon the previously existing Sen1Floods11, xBD, and NASA Sentinel-1 datasets. Sen1Floods11 includes 4,831 512x512 pixel overlapping tiles of coincident Sentinel-1 and Sentinel-2 data observing 11 flood events across the world from 2017-2019. The dataset contains a combination of automated and hand-labeled surface water for use in training and validation of inundation modeling efforts. The xBD dataset identifies flood-damaged buildings and indicates the scale of damage to each (none, minor, moderate, and major) from four flood events which occurred in the United States, India, Nepal, and Bangladesh from the same time period. The NASA dataset contains hand-labeled water bodies observed in Sentinel-1 imagery during five flood events within the 2017-2019 period. The effort presented here utilizes observations from these previously investigated flood events to generate labels of surface water at the 3-5m spatial resolution provided by Planetscope and facilitate the comparison between public and commercial data. A data pipeline was built which uses clustering algorithms to pick the most suitable overlapping chips between the public data and PlanetScope data for manual labeling. Labels were created manually using NASA’s ImageLabeler tool and include areas of high- and low-confidence water. The high confidence designation is reserved for areas of open, unobstructed water while low confidence is used for areas of suspected water beneath vegetation, clouds, or cloud shadows. Expected to be released in late 2022, the FloodPlanet dataset will include tiled imagery with a unique ID for each 1024x1024 pixel tile, 7 bands of HLS data, and high- and low-confidence flood labels in both shapefile and tiff formats. The authors will follow Spatial Temporal Access Catalog (STAC) guidelines to release FloodPlanet on the Radiant Earth ML hub, which hosts public datasets for machine learning.

Alexander Melancon↗
NASA NTRS2001

The SMART Ground-based Remote Sensing for Terra/MODIS Validation

A ground-based remote sensing system - SMART (Surface Measurements for Atmospheric Radiative Transfer) - was deployed during both the SAFARI-2000 and the ARREX-1999 dry season campaigns. The measurement site is the Skukuza airport. The operation period for 1999 is from August 16 to September 10. The main instruments include shortwave (approximately 0.28-2.8 micrometers) and longwave (approximately 4-50 micrometers) broadband radiometers, a shadow-band radiometer, a micro-pulse lidar, and a microwave radiometer. We also did some measurements of solar spectral flux by using an ASD spectrometer. The operation period for 2000 is from August 15 to September 22. This time we added a few new features to the SMART system: a solar tracker for direct and diffuse components of solar fluxes; the scanning capability to the microwave radiometer; a whole sky camera for documenting the sky conditions every minute; and a mini-weather station for atmospheric pressure, temperature, humidity, wind speed/direction. A surface SSFR (Solar Spectral Flux Radiometer) from NASA Ames also joined us for the measurements. This is a unique data set with reasonably long observational period and high accuracy. The data show good correlation with the local weather patterns. We also see diurnal change and some special events, such as fierce fires nearby. To quantify the surface radiative forcing of biomass burning aerosols, many pyranometers, pyrgeometers, and pyrheliometers measure the global, direct, and diffuse irradiance at the surface. These fluxes combining with the collocated optical thickness retrievals from sun photometer (or shadow-band radiometer), the solar radiative forcing, proportional to delta F/delta tau, can be investigated. Integrated with measurements of other instruments at the site, these data sets will serve as "ground truth" for the satellite measurements and modeling.

Tsay, Si-Chee↗
NASA NTRS1997

Radiative Closure Experiments at a Cloud-Free Desert Site, Nevada, as Part of MISR Algorithm Validation

Radiative closure experiments involving a comparison between surface-measured spectral irradiance and the surface irradiance calculated according to a radiative transfer code at a desert site in Nevada under clear skies, yield the result that agreement between the two requires presence of an absorbing aerosol component with an imaginary refractive index equal to 0.03 and a 50:50 mix by optical depth of small and large particles with log-normal size distributions.

MISR experiment field observations calibration dat↗
NASA NTRS2011

Global Precipitation Measurement, Validation, and Applications Integrated Hydrologic Validation to Improve Physical Precipitation Retrievals for GPM

Land surface modeling and data assimilation can provide dynamic land surface state variables necessary to support physical precipitation retrieval algorithms over land. It is well-known that surface emission, particularly over the range of frequencies to be included in the Global Precipitation Measurement Mission (GPM), is sensitive to land surface states, including soil properties, vegetation type and greenness, soil moisture, surface temperature, and snow cover, density, and grain size. In order to investigate the robustness of both the land surface model states and the microwave emissivity and forward radiative transfer models, we have undertaken a multi-site investigation as part of the NASA Precipitation Measurement Missions (PMM) Land Surface Characterization Working Group. Specifically, we will demonstrate the performance of the Land Information System (LIS; http://lis.gsfc.nasa.gov; Peters-Lidard et aI., 2007; Kumar et al., 2006) coupled to the Joint Center for Satellite Data Assimilation (JCSDA's) Community Radiative Transfer Model (CRTM; Weng, 2007; van Deist, 2009). The land surface is characterized by complex physical/chemical constituents and creates temporally and spatially heterogeneous surface properties in response to microwave radiation scattering. The uncertainties in surface microwave emission (both surface radiative temperature and emissivity) and very low polarization ratio are linked to difficulties in rainfall detection using low-frequency passive microwave sensors (e.g.,Kummerow et al. 2001). Therefore, addressing these issues is of utmost importance for the GPM mission. There are many approaches to parameterizing land surface emission and radiative transfer, some of which have been customized for snow (e.g., the Helsinki University of Technology or HUT radiative transfer model;) and soil moisture (e.g., the Land Surface Microwave Emission Model or LSMEM).

Peters-Lidar, Christa D.↗
NASA NTRS2003

NASA GPM GV Science Requirements

An important scientific objective of the NASA portion of the GPM Mission is to generate quantitatively-based error characterization information along with the rainrate retrievals emanating from the GPM constellation of satellites. These data must serve four main purposes: (1) they must be of sufficient quality, uniformity, and timeliness to govern the observation weighting schemes used in the data assimilation modules of numerical weather prediction models; (2) they must extend over that portion of the globe accessible by the GPM core satellite to which the NASA GV program is focused - (approx.65 degree inclination); (3) they must have sufficient specificity to enable detection of physically-formulated microphysical and meteorological weaknesses in the standard physical level 2 rainrate algorithms to be used in the GPM Precipitation Processing System (PPS), i.e., algorithms which will have evolved from the TRMM standard physical level 2 algorithms; and (4) they must support the use of physical error modeling as a primary validation tool and as the eventual replacement of the conventional GV approach of statistically intercomparing surface rainrates fiom ground and satellite measurements. This approach to ground validation research represents a paradigm shift vis-&-vis the program developed for the TRMM mission, which conducted ground validation largely as a statistical intercomparison process between raingauge-derived or radar-derived rainrates and the TRMM satellite rainrate retrievals -- long after the original satellite retrievals were archived. This approach has been able to quantify averaged rainrate differences between the satellite algorithms and the ground instruments, but has not been able to explain causes of algorithm failures or produce error information directly compatible with the cost functions of data assimilation schemes. These schemes require periodic and near-realtime bias uncertainty (i.e., global space-time distributed conditional accuracy of the retrieved rainrates) and local error covariance structure (i.e., global space-time distributed error correlation information for the local 4-dimensional space-time domain -- or in simpler terms, the matrix form of precision error). This can only be accomplished by establishing a network of high quality-heavily instrumented supersites selectively distributed at a few oceanic, continental, and coastal sites. Economics and pragmatics dictate that the network must be made up of a relatively small number of sites (6-8) created through international cooperation. This presentation will address some of the details of the methodology behind the error characterization approach, some proposed solutions for expanding site-developed error properties to regional scales, a data processing and communications concept that would enable rapid implementation of algorithm improvement by the algorithm developers, and the likely available options for developing the supersite network.

Smith, E.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-17940-DND

The NASA/GEWEX SRB (Global Energy and Water Exchanges, Surface Radiation Budget) project produces and archives shortwave and longwave radiation budget flux estimates at the top of the atmosphere and at the Earth's surface. The latest version in the archive, Release 3.0, is available as 3-hourly, 3-hourly-monthly, daily and monthly means continuously over the period from July 1983 to December 2007 on a quasi-equal-area grid system of 44016 grid boxes. SRB Release 4 with further improvements in data quality and higher spatial resolution is being developed. The SRB shortwave/longwave fluxes at the Earth's surface from the algorithm GSW(V3.0)/GLW(V3.1) have been extensively validated against high-quality ground-based observations, in particular observed data from the Baseline Surface Radiation Network (BSRN). Comparisons with nearly 6000 site-months of both shortwave and longwave data from 52 BSRN sites show generally good agreement. In addition, the GEWEX SRB data have also been found to compare favorably with the World Radiation Data Centre (WRDC) data and the Global Energy Balance Archive (GEBA) data. In spite of the fact that the BSRN sites are scattered on all seven continents, the validation of the SRB data over the vast oceans had not been done until recently. In this paper, we present comparisons of the GEWEX-SRB data shortwave/longwave data with observations made on arrays moored in tropical oceans. Specifically, we have data from 21 buoys, or moorings, from Predictions and Research Moored Array in the Atlantic (PIRATA), 14 buoys from the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) in the Indian Ocean, 20 buoys from the Tropical Atmosphere Ocean (TAO) array in the Pacific, and 3 buoys from the Woods Hole Oceanographic Institute (WHOI) (2 in the Pacific and 1 in the Atlantic). The data from these buoys span 12 years from 2000 to 2011, though not necessarily continuously. It is found that except for occasional outliers, the majority of the comparable pairs of data points agree well. Further examination of the outliers is necessary to determine whether they can be attributed to wind-driven tilt and/or aerosol accumulation on the instruments. In order to see how the ocean-based validation compares with land-based validation, we made SRB-BSRN monthly mean shortwave/longwave radiation comparisons in the tropical region only, where all the buoys are moored. The results show that in the case of shortwave radiation, the bias error from the ocean-based validation is somewhat larger than its land-based counterpart, but their RMS errors are not significantly different. In the case of longwave radiation, the ocean-based and land-based validations show nearly identical comparison statistics.

Taiping Zhang↗
NASA NTRS2013

Testing of the Trim Tab Parametric Model in NASA Langley's Unitary Plan Wind Tunnel

In support of NASA's Entry, Descent, and Landing technology development efforts, testing of Langley's Trim Tab Parametric Models was conducted in Test Section 2 of NASA Langley's Unitary Plan Wind Tunnel. The objectives of these tests were to generate quantitative aerodynamic data and qualitative surface pressure data for experimental and computational validation and aerodynamic database development. Six component force-and-moment data were measured on 38 unique, blunt body trim tab configurations at Mach numbers of 2.5, 3.5, and 4.5, angles of attack from -4deg to +20deg, and angles of sideslip from 0deg to +8deg. Configuration parameters investigated in this study were forebody shape, tab area, tab cant angle, and tab aspect ratio. Pressure Sensitive Paint was used to provide qualitative surface pressure mapping for a subset of these flow and configuration variables. Over the range of parameters tested, the effects of varying tab area and tab cant angle were found to be much more significant than varying tab aspect ratio relative to key aerodynamic performance requirements. Qualitative surface pressure data supported the integrated aerodynamic data and provided information to aid in future analyses of localized phenomena for trim tab configurations.

Murphy, Kelly J.↗
NASA NTRS1985

Surface Structures of Hawaiian Lavas

Surface and internal lava structures can be valid indicators of lava viscosity and rheology, provided that care is taken to identify and eliminate structures which are strain-rate-dependent. Here, a spectrum of types among Hawaiian basaltic flows is found ranging from pahoehoe to a'a, that are interpreted as marking a progression in lava viscosity and a change in rheology. The most fluid type in this spectrum is normal pahoehoe that has a smooth but commonly wrinkled or folded (ropy) surface. The next type, distinctly more viscous and probably non-Newtonian in rheology, is spiny pahoehoe which is characterized by a spinose surface and an absence of ropy structures. Preliminary studies on the long lavas of Mauna Loa indicated, perhaps surprisingly, that there is no clear-cut correlation of lava length with type in this spectrum of lavas, indicating that viscosity/yield strength of the basaltic lavas per se are not the primary controls determining flow length. Flowage of the lava through lava tubes, while it may help to account for the long flow distance of some lavas, is not a generally applicable explanation for long flow length.

Rowland, S. K.↗
NASA NTRS2005

NASA's Global Precipitation Mission Ground Validation Segment

NASA is designing a Ground Validation Segment (GVS) as one of its contributions to the Global Precipitation Measurement (GPM) mission. The GPM GVS provides an independent means for evaluation, diagnosis, and ultimately improvement of the GPM spaceborne measurements and precipitation products. NASA's GPM GVS concept calls for a combination of direct observations executed within a Multidimensional Observing Volume (MOV) and model-based analyses executed by a Satellite Simulator Model (SSM). The MOV consists of ground-based instruments that measure local surface and atmospheric properties required for GPM validation. The SSM utilizes MOV measurements in a forward numerical model. The goal of the SSM forward modeling is calculation of the following properties: top-of-atmosphere microwave radiative quantities to within sensor noise of those measured by the GPM Core Satellite, precipitation quantities identical to those generated by the standard GPM precipitation retrieval algorithms, and quantitative/objective error estimates of both sets of quantities. At present, the GVS is in the early design stage and various scenarios have been generated to assess how it will be used in the GPM era. The GPM GVS will be operational in the year prior to the launch of the GPM core satellite, which has a launch date scheduled for December 2010.

GVS (GROUND VALIDATION SEGMENT)↗
NASA NTRS2011

Bridging Empirical and Physical Approaches for Landslide Monitoring and Early Warning

Rainfall-triggered landslides typically occur and are evaluated at local scales, using slope-stability models to calculate coincident changes in driving and resisting forces at the hillslope level in order to anticipate slope failures. Over larger areas, detailed high resolution landslide modeling is often infeasible due to difficulties in quantifying the complex interaction between rainfall infiltration and surface materials as well as the dearth of available in situ soil and rainfall estimates and accurate landslide validation data. This presentation will discuss how satellite precipitation and surface information can be applied within a landslide hazard assessment framework to improve landslide monitoring and early warning by considering two disparate approaches to landslide hazard assessment: an empirical landslide forecasting algorithm and a physical slope-stability model. The goal of this research is to advance near real-time landslide hazard assessment and early warning at larger spatial scales. This is done by employing high resolution surface and precipitation information within a probabilistic framework to provide more physically-based grounding to empirical landslide triggering thresholds. The empirical landslide forecasting tool, running in near real-time at http://trmm.nasa.gov, considers potential landslide activity at the global scale and relies on Tropical Rainfall Measuring Mission (TRMM) precipitation data and surface products to provide a near real-time picture of where landslides may be triggered. The physical approach considers how rainfall infiltration on a hillslope affects the in situ hydro-mechanical processes that may lead to slope failure. Evaluation of these empirical and physical approaches are performed within the Land Information System (LIS), a high performance land surface model processing and data assimilation system developed within the Hydrological Sciences Branch at NASA's Goddard Space Flight Center. LIS provides the capabilities to quantify uncertainty from model inputs and calculate probabilistic estimates for slope failures. Results indicate that remote sensing data can provide many of the spatiotemporal requirements for accurate landslide monitoring and early warning; however, higher resolution precipitation inputs will help to better identify small-scale precipitation forcings that contribute to significant landslide triggering. Future missions, such as the Global Precipitation Measurement (GPM) mission will provide more frequent and extensive estimates of precipitation at the global scale, which will serve as key inputs to significantly advance the accuracy of landslide hazard assessment, particularly over larger spatial scales.

Kirschbaum, Dalia↗