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Designing Rolling-Element Bearings
Bearing Analysis Tool (BAT) is a computer program for designing rolling-element bearings for cryogenic turbomachines. BAT provides a graphical user interface (GUI) that guides the entry of data to develop mathematical models of bearings. The GUI breaks model data into logical subsets that are entered through logic-driven input screens. The software generates a threedimensional graphical model of a bearing as the data are entered. Most dataentry errors become immediately obvious in the graphical model. BAT provides for storage of all the data on a shaft/bearing system, enabling the creation of a library of proven designs. Data from the library can be transferred to subsequent projects by use of simple cut-and-paste routines. BAT includes a library of temperature- dependent cryogenic bearing-material properties for use in the mathematical models. BAT implements algorithms that (1) enable the user to select combinations of design and/or operating-condition parameters, and then (2) automatically optimize the design by performing trade studies over all of the parameter combinations. This feature enables optimization over a large trade space in a fraction of the time taken when using prior bearingmodel software.
A Dynamic Landslide Hazard Monitoring Framework for the Lower Mekong Region
The Lower Mekong region is one of the most landslide-prone areas of the world. Despite the need for dynamic characterization of landslide hazard zones within the region, it is largely understudied for several reasons. Dynamic and integrated understanding of landslide processes requires landslide inventories across the region, which have not been available previously. Computational limitations also hamper regional landslide hazard assessment, including accessing and processing remotely sensed information. Finally, open-source software and modelling packages are required to address regional landslide hazard analysis. Leveraging an open-source data-driven global Landslide Hazard Assessment for Situational Awareness model framework, this study develops a region-specific dynamic landslide hazard system leveraging satellite-based Earth observation data to assess landslide hazards across the lower Mekong region. A set of landslide inventories were prepared from high-resolution optical imagery using advanced image-processing techniques. Several static and dynamic explanatory variables (i.e., rainfall, soil moisture, slope, relief, distance to roads, distance to faults, distance to rivers) were considered during the model development phase. An extreme gradient boosting decision tree model was trained for the monsoon period of 2015–2019 and the model was evaluated with independent inventory information for the 2020 monsoon period. The model performance demonstrated considerable skill using receiver operating characteristic curve statistics, with Area Under the Curve values exceeding 0.95. The model architecture was designed to use near-real-time data, and it can be implemented in a cloud computing environment (i.e., Google Cloud Platform) for the routine assessment of landslide hazards in the Lower Mekong region. This work was developed in collaboration with scientists at the Asian Disaster Preparedness Center as part of the NASA SERVIR Program’s Mekong hub. The goal of this work is to develop a suite of tools and services on accessible open-source platforms that support and enable stakeholder communities to better assess landslide hazard and exposure at local to regional scales for decision making and planning.
Analysis of Darkened Fragments Resulting from Laboratory Hypervelocity Experiments
NASA’s Orbital Debris Program Office (ODPO) relies on measurements from optical, radar, and in situ measurements to facilitate the development of data-driven orbital debris environmental engineering models such as the NASA Orbital Debris Engineering Model (ORDEM). For optical measurements, the ODPO relies on ground-based optical telescopes to statistically assess objects in geosynchronous orbit and, in the future, low Earth orbit (LEO). The data collected include the detected object’s orbital parameters, time of observation, and optical magnitude. The latter parameter can be converted to a size using NASA’s optical Size Estimation Model (oSEM). It is well known that the observed magnitude of orbital debris can vary based on an object's material constituents, observational geometry, and the effects of space weathering. To assess these magnitude variations, the ODPO uses the Optical Measurement Center (OMC) at NASA Johnson Space Center to characterize a variety of materials and fragments from laboratory impact tests representative of fragments that constitute the orbital debris population. One experiment was DebriSat: a 56 kg spacecraft was built to incorporate structural elements of a modern LEO spacecraft and was subjected to a hypervelocity impact test at the U.S. Air Force’s Arnold Engineering Development Complex using test parameters that may be encountered in LEO. The DebriSat project has provided an abundance of information for assessing fragmentation debris in terms of material, color, shape, size, density, mass, and other derived parameters. Prior to the impact test, the ODPO collected spectral measurements on a subset of the materials used to construct DebriSat for a “ground-truth” of their optical properties. After the successful hypervelocity impact test, the DebriSat team observed a fine, dark dust coating all the fragments. Prior research has suggested that this came from ablated material deposited on the fragments during the impact test, causing a change in the reflective properties [1]. Given that this lower reflectivity on the DebriSat fragments will influence the laboratory-acquired magnitudes used to calculate size and inform potential updates to the oSEM, it is critical to assess if this darkening effect on the DebriSat fragments is a laboratory bias or something that could occur in on-orbit breakup events. This paper will provide a brief overview of the OMC and DebriSat experiment, focused on the optical characterization of a subset of materials using broadband photometric measurements and spectroscopic measurements. In addition, elemental analysis of various DebriSat fragments and the soft-catch foam used in the hypervelocity experiment compared with pristine foam will be examined to further evaluate the source of the dark material coating all fragments. Finally, the authors will present a twofold plan 1) for assessing potential biases in laboratory impact experiments that could affect laboratory optical characterization and 2) for mitigating biases when compared with ground-based optical telescopic measurements of the orbital debris environment.
Analysis of Darkened Fragments Resulting from Laboratory Hypervelocity Experiments
NASA’s Orbital Debris Program Office (ODPO) relies on measurements from optical, radar, and in situ measurements to facilitate the development of data-driven orbital debris environmental engineering models such as the NASA Orbital Debris Engineering Model (ORDEM). For optical measurements, the ODPO relies on ground-based optical telescopes to statistically assess objects in geosynchronous orbit and, in the future, low Earth orbit (LEO). The data collected include the detected object’s orbital parameters, time of observation, and optical magnitude. The latter parameter can be converted to a size using NASA’s optical Size Estimation Model (oSEM). It is well known that the observed magnitude of orbital debris can vary based on an object's material constituents, observational geometry, and the effects of space weathering. To assess these magnitude variations, the ODPO uses the Optical Measurement Center (OMC) at NASA Johnson Space Center to characterize a variety of materials and fragments from laboratory impact tests representative of fragments that constitute the orbital debris population. One experiment was DebriSat: a 56 kg spacecraft was built to incorporate structural elements of a modern LEO spacecraft and was subjected to a hypervelocity impact test at the U.S. Air Force’s Arnold Engineering Development Complex using test parameters that may be encountered in LEO. The DebriSat project has provided an abundance of information for assessing fragmentation debris in terms of material, color, shape, size, density, mass, and other derived parameters. Prior to the impact test, the ODPO collected spectral measurements on a subset of the materials used to construct DebriSat for a “ground-truth” of their optical properties. After the successful hypervelocity impact test, the DebriSat team observed a fine, dark dust coating all the fragments. Prior research has suggested that this came from ablated material deposited on the fragments during the impact test, causing a change in the reflective properties [1]. Given that this lower reflectivity on the DebriSat fragments will influence the laboratory-acquired magnitudes used to calculate size and inform potential updates to the oSEM, it is critical to assess if this darkening effect on the DebriSat fragments is a laboratory bias or something that could occur in on-orbit breakup events. This presentation will provide a brief overview of the OMC and DebriSat experiment, focused on the optical characterization of a subset of materials using broadband photometric measurements and spectroscopic measurements. In addition, elemental analysis of various DebriSat fragments and the soft-catch foam used in the hypervelocity experiment compared with pristine foam will be examined to further evaluate the source of the dark material coating all fragments. Finally, the authors will present a twofold plan 1) for assessing potential biases in laboratory impact experiments that could affect laboratory optical characterization and 2) for mitigating biases when compared with ground-based optical telescopic measurements of the orbital debris environment.
Analysis of Darkened Fragments Resulting from Laboratory Hypervelocity Experiments
NASA’s Orbital Debris Program Office (ODPO) relies on measurements from optical, radar, and in situ measurements to facilitate the development of data-driven orbital debris environmental engineering models such as the NASA Orbital Debris Engineering Model (ORDEM). For optical measurements, the ODPO relies on ground-based optical telescopes to statistically assess objects in geosynchronous orbit (GEO) and, in the future, low Earth orbit (LEO). The data collected include the detected object’s orbital parameters, time of observation, and optical magnitude. The latter parameter can be converted to a size using NASA’s optical Size Estimation Model (oSEM). It is well known that the observed magnitude of orbital debris can vary based on an object's material constituents, observational geometry, and the effects of space weathering.
Canopy reflectance modeling in a tropical wooded grassland
The Li-Strahler canopy reflectance model, driven by LANDSAT Thematic Mapper (TM) data, provided regional estimates of tree size and density in two bioclimatic zones in Africa. This model exploits tree geometry in an inversion technique to predict average tree size and density from reflectance data using a few simple patameters measured in the field and in the imagery. Reflectance properties of the trees were measured in the study sites using a pole-mounted radiometer. The measurements showed that the assumptions of the simple Li-Strahler model are reasonable for these woodlands. The field radiometer measurements were used to calculate the normalized difference vegetation index (NDVI), and the integrated NDVI over the canopy was related to crown volume. Predictions of tree size and density from the canopy model were used with allometric equations from the literature to estimate woody biomass and potential foliar biomass for the sites and for the regions. Estimates were compared with independent measurements made in the Sahelian sites, and to typical values from the literature for these regions and for similar woodlands. In order to apply the inversion procedure regionally, an area must first be stratified into woodland cover classes, and dry-season TM data were used to generate a stratum map of the study areas with reasonable accuracy. The method used was unsupervised classification of multi-data principal components images.
Development of Carbon Flux Model Using ABI Data Over the Conterminous US
The satellite-driven carbon flux estimation has been playing important role to estimate continental-scale carbon budget. One of the biggest recent advances in the satellite-driven carbon flux modeling is utilization of high-frequent geostationary satellites to estimate diurnal cycle in carbon fluxes. The satellite based carbon flux estimation used the polar orbiting satellite sensors (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)), which allow us to observe target regions only once during the day. The new generation of geostationary satellite sensors provide frequent observations, often less than every 10 minutes. Here, we use GOES Advanced Baseline Imager (ABI) data to estimate hourly NEE over the conterminous US. We used the Terrestrial Observation Prediction System (TOPS) model for estimating hourly NEE. TOPS is a diagnostic ecosystem process model that simulates the fluxes of carbon and water through vegetation in response to climate variability. For the climate input, we developed hourly climate data using the same algorithm with NASA Earth Exchange Gridded Daily Meteorology (NEX-GDM) datasets based on machine learning techniques. The hourly climate data includes precipitation, maximum temperature, minimum temperature, dew point temperature, and solar radiation were derived from the Geostationary observations. The spatial patterns of ecosystem parameters used in TOPS are optimized using satellite Solar Induced Fluorescence (SIF) data. The high frequency GPP estimations from geostationary satellite sensors make it comparable to the instantaneous SIF data than daily GPP. We also used Ameriflux data for optimization of model parameters and the validation of the output. The derived data addresses the diurnal dynamics of carbon cycling at large scales and should help in reducing the uncertainties in carbon budget studies.
The New NASA Orbital Debris Engineering Model ORDEM 3.0
The NASA Orbital Debris Program Office (ODPO) has released its latest Orbital Debris Engineering Model, ORDEM 3.0. It supersedes ORDEM 2.0. This newer model encompasses the Earth satellite and debris flux environment from altitudes of low Earth orbit (LEO) through geosynchronous orbit (GEO). Debris sizes of 10 microns through 1 m in non-GEO and 10 cm through 1 m in GEO are modeled. The inclusive years are 2010 through 2035. The ORDEM model series has always been data driven. ORDEM 3.0 has the benefit of many more hours from existing data sources and from new sources that weren't available to past versions. Returned surfaces, ground tests, and remote sensors all contribute data. The returned surface and ground test data reveal material characteristics of small particles. Densities of fragmentation debris particles smaller than 10 cm are grouped in ORDEM 3.0 in terms of high-, medium-, and lowdensities, along with RORSAT sodium-potassium droplets. Supporting models have advanced significantly. The LEO-to-GEO ENvironment Debris model (LEGEND) includes an historical and a future projection component with yearly populations that include launched and maneuvered intacts, mission related debris (MRD), and explosion and collision fragments. LEGEND propagates objects with ephemerides and physical characteristics down to 1 mm in size. The full LEGEND yearly population acts as an a priori condition for a Bayesian statistical model. Specific, well defined populations are added like the Radar Ocean Reconnaissance Satellite (RORSAT) sodium-potassium (NaK) droplets, recent major accidental and deliberate collision fragments, and known anomalous debris event fragments. For microdebris of sizes 10 microns to 1 mm the ODPO uses an in-house Degradation/Ejecta model in which a MLE technique is used with returned surface data to estimate populations. This paper elaborates on the upgrades of this model over previous versions highlighting the material density splits and consequences of that to the penetration risk to spacecraft.
Invertible canopy reflectance modeling of vegetation structure in semiarid woodland
The Li-Strahler canopy reflectance model, driven by Landsat Thematic Mapper (TM) data, provided regional estimates of tree size and density in two bioclimatic zones in West Africa. This model exploits tree geometry in an inversion technique to predict average tree size and density from reflectance data using a few simple parameters measured in the field (spatial pattern, shape, and size distribution of trees) and in the imagery (spectral signatures of scene components). The model was tested in sparse woodland and wooded grassland in the Sahelian and Sudanian bioclimatic zones in West Africa.
The New NASA Orbital Debris Engineering Model ORDEM 3.0
The NASA Orbital Debris Program Office (ODPO) has released its latest Orbital Debris Engineering Model, ORDEM 3.0. It supersedes ORDEM 2000, now referred to as ORDEM 2.0. This newer model encompasses the Earth satellite and debris flux environment from altitudes of low Earth orbit (LEO) through geosynchronous orbit (GEO). Debris sizes of 10 micron through larger than 1 m in non-GEO and 10 cm through larger than 1 m in GEO are available. The inclusive years are 2010 through 2035. The ORDEM model series has always been data driven. ORDEM 3.0 has the benefit of many more hours of data from existing sources and from new sources than past ORDEM versions. The object data range in size from 10 μm to larger than 1 m, and include in situ and remote measurements. The in situ data reveals material characteristics of small particles. Mass densities are grouped in ORDEM 3.0 in terms of 'high-density', represented by 7.9 g/cc, 'medium-density' represented by 2.8 g/cc and 'low-density' represented by 1.4 g/cc. Supporting models have also advanced significantly. The LEO-to-GEO ENvironment Debris model (LEGEND) includes an historical and a future projection component with yearly populations that include launched and maneuvered intact spacecraft and rocket bodies, mission related debris, and explosion and collision event fragments. LEGEND propagates objects with ephemerides and physical characteristics down to 1 mm in size. The full LEGEND yearly population acts as an a priori condition for a Bayesian statistical model. Specific populations are added from sodium potassium droplet releases, recent major accidental and deliberate collisions, and known anomalous debris events. This paper elaborates on the upgrades of this model over previous versions. Sample validation results with remote and in situ measurements are shown, and the consequences of including material density are discussed as it relates to heightened risks to crewed and robotic spacecraft
Strategies for concurrent processing of complex algorithms in data driven architectures
The results of ongoing research directed at developing a graph theoretical model for describing data and control flow associated with the execution of large grained algorithms in a spatial distributed computer environment is presented. This model is identified by the acronym ATAMM (Algorithm/Architecture Mapping Model). The purpose of such a model is to provide a basis for establishing rules for relating an algorithm to its execution in a multiprocessor environment. Specifications derived from the model lead directly to the description of a data flow architecture which is a consequence of the inherent behavior of the data and control flow described by the model. The purpose of the ATAMM based architecture is to optimize computational concurrency in the multiprocessor environment and to provide an analytical basis for performance evaluation. The ATAMM model and architecture specifications are demonstrated on a prototype system for concept validation.
Shorebird Migration Patterns in Response to Climate Change: A Modeling Approach
The availability of satellite remote sensing observations at multiple spatial and temporal scales, coupled with advances in climate modeling and information technologies offer new opportunities for the application of mechanistic models to predict how continental scale bird migration patterns may change in response to environmental change. In earlier studies, we explored the phenotypic plasticity of a migratory population of Pectoral sandpipers by simulating the movement patterns of an ensemble of 10,000 individual birds in response to changes in stopover locations as an indicator of the impacts of wetland loss and inter-annual variability on the fitness of migratory shorebirds. We used an individual based, biophysical migration model, driven by remotely sensed land surface data, climate data, and biological field data. Mean stop-over durations and stop-over frequency with latitude predicted from our model for nominal cases were consistent with results reported in the literature and available field data. In this study, we take advantage of new computing capabilities enabled by recent GP-GPU computing paradigms and commodity hardware (general purchase computing on graphics processing units). Several aspects of our individual based (agent modeling) approach lend themselves well to GP-GPU computing. We have been able to allocate compute-intensive tasks to the graphics processing units, and now simulate ensembles of 400,000 birds at varying spatial resolutions along the central North American flyway. We are incorporating additional, species specific, mechanistic processes to better reflect the processes underlying bird phenotypic plasticity responses to different climate change scenarios in the central U.S.
NASA's Carbon Monitoring System Flux-Pilot Project: A Multi-Component Analysis System for Carbon-Cycle Research and Monitoring
The importance of greenhouse gas increases for climate motivates NASA s observing strategy for CO2 from space, including the forthcoming Orbiting Carbon Observatory (OCO-2) mission. Carbon cycle monitoring, including attribution of atmospheric concentrations to regional emissions and uptake, requires a robust modeling and analysis infrastructure to optimally extract information from the observations. NASA's Carbon-Monitoring System Flux-Pilot Project (FPP) is a prototype for such analysis, combining a set of unique tools to facilitate analysis of atmospheric CO2 along with fluxes between the atmosphere and the terrestrial biosphere or ocean. NASA's analysis system is unique, in that it combines information and expertise from the land, oceanic, and atmospheric branches of the carbon cycle and includes some estimates of uncertainty. Numerous existing space-based missions provide information of relevance to the carbon cycle. This study describes the components of the FPP framework, assessing the realism of computed fluxes, thus providing the basis for research and monitoring applications. Fluxes are computed using data-constrained terrestrial biosphere models and physical ocean models, driven by atmospheric observations and assimilating ocean-color information. Use of two estimates provides a measure of uncertainty in the fluxes. Along with inventories of other emissions, these data-derived fluxes are used in transport models to assess their consistency with atmospheric CO2 observations. Closure is achieved by using a four-dimensional data assimilation (inverse) approach that adjusts the terrestrial biosphere fluxes to make them consistent with the atmospheric CO2 observations. Results will be shown, illustrating the year-to-year variations in land biospheric and oceanic fluxes computed in the FPP. The signals of these surface-flux variations on atmospheric CO2 will be isolated using forward modeling tools, which also incorporate estimates of transport error. The results will be discussed in the context of interannual variability of observed atmospheric CO2 distributions.
Improved canopy reflectance modeling and scene inference through improved understanding of scene pattern
The Li-Strahler reflectance model, driven by LANDSAT Thematic Mapper (TM) data, provided regional estimates of tree size and density within 20 percent of sampled values in two bioclimatic zones in West Africa. This model exploits tree geometry in an inversion technique to predict average tree size and density from reflectance data using a few simple parameters measured in the field (spatial pattern, shape, and size distribution of trees) and in the imagery (spectral signatures of scene components). Trees are treated as simply shaped objects, and multispectral reflectance of a pixel is assumed to be related only to the proportions of tree crown, shadow, and understory in the pixel. These, in turn, are a direct function of the number and size of trees, the solar illumination angle, and the spectral signatures of crown, shadow and understory. Given the variance in reflectance from pixel to pixel within a homogeneous area of woodland, caused by the variation in the number and size of trees, the model can be inverted to give estimates of average tree size and density. Because the inversion is sensitive to correct determination of component signatures, predictions are not accurate for small areas.
The Heliophysics Data Environment Today
Driven by the nature of the research questions now most critical to further progress in heliophysics science, data-driven research has evolved from a model once centered on individual instrument Principal investigator groups and a circle of immediate collaborators into a more inclusive and open environment where data gathered ay great public cost must then be findable and useable throughout the broad national and international research community. In this paper and as an introduction to this special session, we will draw a picture of existing and evolving resources throughout the heliophyscs community, the capabilities and data now available to end users, and the relationships and complementarity of different elements in the environment today. We will cite the relative roles of mission and instrument data centers and resident archives, multi-mission data centers, and the growing importance of virtual discipline observatories and cross-cutting services including the evolution of a common data dictionary. We will briefly summarize our view of the most important challenges still faced by users and providers, and our vision in ow the efforts today can evolve into a more and more enabling data framework for the global research community to tap the widest range of existing missions and their data to address a full range of critical science questions from the scale of microphysics to the heliospheric system as a whole.
The Correlation Between Atmospheric Dust Deposition to the Surface Ocean and SeaWiFS Ocean Color: A Global Satellite-Based Analysis
Since the atmospheric deposition of iron has been linked to primary productivity in various oceanic regions, we have conducted an objective study of the correlation of dust deposition and satellite remotely sensed surface ocean chlorophyll concentrations. We present a global analysis of the correlation between atmospheric dust deposition derived from a satellite-based 3-D atmospheric transport model and SeaWiFs estimates of ocean color. We use the monthly mean dust deposition fields of Ginoux et al. which are based on a global model of dust generation and transport. This model is driven by atmospheric circulation from the Data Assimilation Office (DAO) for the period 1995-1998. This global dust model is constrained by several satellite estimates of standard circulation characteristics. We then perform an analysis of the correlation between the dust deposition and the 1998 SeaWIFS ocean color data for each 2.0 deg x 2.5 deg lat/long grid point, for each month of the year. The results are surprisingly robust. The region between 40 S and 60 S has correlation coefficients from 0.6 to 0.95, statistically significant at the 0.05 level. There are swaths of high correlation at the edges of some major ocean current systems. We interpret these correlations as reflecting areas that have shear related turbulence bringing nitrogen and phosphorus from depth into the surface ocean, and the atmospheric supply of iron provides the limiting nutrient and the correlation between iron deposition and surface ocean chlorophyll is high. There is a region in the western North Pacific with high correlation, reflecting the input of Asian dust to that region. The southern hemisphere has an average correlation coefficient of 0.72 compared that in the northern hemisphere of 0.42 consistent with present conceptual models of where atmospheric iron deposition may play a role in surface ocean biogeochemical cycles. The spatial structure of the correlation fields will be discussed within the context of guiding the design of field programs.