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At least 1,441 records · Page 80

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NASA NTRS2003

Towards Automating Spacecraft Attitude Sensor Calibration

With a view towards reducing cost and complexity for spacecraft early mission support at the NASA Goddard Space Flight Center (GSFC), efforts are being made to automate the attitude sensor calibration process. This paper addresses one of the major components needed by such a system. The beneficiaries of an improved calibration process are missions that demand moderate to high precision attitude knowledge or that need to perform accurate attitude slews. Improved slew accuracy reduces the time needed for re-acquisition of fine-pointing after each attitude maneuver, Rapid target acquisition can be very important for astronomical targeting or for off-nadir surface feature targeting by Earth-oriented spacecraft. The normal sequence of on-orbit calibration starts with alignment calibration of the star trackers and possibly the Sun sensor. Their relative alignment needs to be determined using a sufficiently large data set so their fields of view are adequately sampled. Next, the inertial reference unit (IRU) is calibrated for corrections to its alignment and scale factors. The IRU biases are estimated continuously by the onboard attitude control system, but the IRU alignment and scale factors are usually determined on the ground using a batch-processing method on a data set that includes several slews sufficient to give full observability of all the IRU calibration parameters. Finally, magnetometer biases, alignment, and its coupling to the magnetic torquers are determined in order io improve momentum management and occasionally for use in the attitude determination system. The detailed approach used for automating calibrations will depend on whether the automated system resides on the ground or on the spacecraft with an ultimate goal of autonomous calibration. Current efforts focus on a ground-based system driving subsystems that could run either on the ground or onboard. The distinction is that onboard calibration should process the data sequentially rather than in a single large batch since onboard computer data storage is limited. Very good batch- processing calibration utilities have been developed and used extensively at NASA/GSFC for mission support but no sequential calibration utilities are available. To meet this need, this paper presents the mathematical description of a sequential IRU calibration system. The system has been tested using flight data from the Rossi X-ray Timing Explorer (RXTE) during a series of attitude slews. The paper also discusses the current state of the overall automated system and describes plans for adding sequential alignment calibration and other additions that will reduce the amount of analyst time and input.

Sedlak, Joseph↗

NASA NTRS2007

Titan's Surface from Cassini RADAR SAR and High Resolution Radiometry Data of the First Five Flybys

The first five Titan flybys with Cassini's Synthetic Aperture RADAR (SAR) and radiometer are examined with emphasis on the calibration and interpretation of the high-resolution radiometry data acquired during the SAR mode (SAR-radiometry). Maps of the 2-cm wavelength brightness temperature are obtained coincident with the SAR swath imaging, with spatial resolution approaching 6 km. A preliminary calibration shows that brightness temperature in these maps varies from 64 to 89 K. Surface features and physical properties derived from the SAR-radiometry maps and SAR imaging are strongly correlated; in general, we find that surface features with high radar reflectivity are associated with radiometrically cold regions, while surface features with low radar reflectivity correlate with radiometrically warm regions. We examined scatterplots of the normalized radar cross-section sigma(exp o) versus brightness temperature, finding differing signatures that characterize various terrains and surface features. Implications for the physical and compositional properties of these features are discussed. The results indicate that volume scattering is important in many areas of Titan's surface, particularly Xanadu, while other areas exhibit complex brightness temperature variations consistent with variable slopes or surface material and compositional properties.

Titan↗

NASA NTRS2011

The Robonaut 2 Hand - Designed to do Work with Tools

The second generation Robonaut hand has many advantages over its predecessor. This mechatronic device is more dexterous and has improved force control and sensing giving it the capability to grasp and actuate a wider range of tools. It can achieve higher peak forces at higher speeds than the original. Developed as part of a partnership between General Motors and NASA, the hand is designed to more closely approximate a human hand. Having a more anthropomorphic design allows the hand to attain a larger set of useful grasps for working with human interfaces. Key to the hand s improved performance is the use of lower friction drive elements and a redistribution of components from the hand to the forearm, permitting more sensing in the fingers and palm where it is most important. The following describes the design, mechanical/electrical integration, and control features of the hand. Lessons learned during the development and initial operations along with planned refinements to make it more effective are presented.

Bridgwater, L. B.↗

NASA NTRS2011

Precipitation Estimates for Hydroelectricity

Hydroelectric plants require precise and timely estimates of rain, snow and other hydrometeors for operations. However, it is far from being a trivial task to measure and predict precipitation. This paper presents the linkages between precipitation science and hydroelectricity, and in doing so it provides insight into current research directions that are relevant for this renewable energy. Methods described include radars, disdrometers, satellites and numerical models. Two recent advances that have the potential of being highly beneficial for hydropower operations are featured: the Global Precipitation Measuring (GPM) mission, which represents an important leap forward in precipitation observations from space, and high performance computing (HPC) and grid technology, that allows building ensembles of numerical weather and climate models.

Tapiador, Francisco J.↗

NASA NTRS2014

ORDEM 3.0 and the Risk of High-Density Debris

NASA’s Orbital Debris Engineering Model was designed to calculate orbital debris fluxes on spacecraft in order to assess collision risk. The newest of these models, ORDEM 3.0, has a number of features not present in previous models. One of the most important is that the populations and fluxes are now broken out into material density groups. Previous models concentrated on debris size alone, but a particle’s mass and density also determine the amount of damage it can cause. ORDEM 3.0 includes a high-density component, primarily consisting of iron/steel particles that drive much of the risk to spacecraft. This paper will outline the methods that were used to separate and identify the different densities of debris, and how these new densities affect the overall debris flux and risk.

Matney, Mark↗

NASA NTRSDate not supplied

Influence of Heat Flow from the Planetary Interior on Surface Habitability

an primordial and/or radiogenic heat flow from a rocky planet’s interior help maintain a surface environment amenable to life?On rocky Earth-like planets with shallow oceans (≤5 km depth) that are on the outer edge of the habitable zone, the additional energy provided might make the difference between a world supporting regional habitat space and a world uninhabitable by any known life-form. To test this hypothesis, we introduce heat flux from the planetary interior as a forcing to the NASA/GISSROCKE-3D exoplanet GCM, and explore whether it can enhance the habitability of marginal terrestrial rocky planets (e.g.,paleo Earth in a “snowball” climate state).GCM simulations of modern Earth have not included geothermal heat flux as a climate forcing, as it is more than an order of magnitude less than the 2.9 W/m heating caused by anthropogenic greenhouse gases [Flanner, GRL 2009]. Modern Earth measurements in the oceans [Downes et al., GRL 2019] and results from ocean GCMs [Barnes et al., Ocean Model. 2017]suggest that localized higher heat flux can have measurable thermodynamic and dynamic impacts, such as weakening deep ocean stratification and increased poleward ocean heat transports. A similar result with ROCKE-3D would be important, since our snowball Earth simulations without heat flux produce features like ocean stratification that should have hampered the survival of life. We have created a set of spatially variable geothermal heat fluxes at varying horizontal resolutions based on modern Earth heat flow patterns [Davies, Geochem. Geophys. Geosys. 2013] to test the sensitivity of the ROCKE-3D GCM initially in a modern Earth context. Preliminary results show that even modest heat fluxes have a measurable impact on deep ocean temperatures (upto +0.9°C locally) and that this heat can be transported at depth along ocean circulation paths. Estimates of heat flux impacts on vertical ocean mixing and surface ocean conditions require long simulations at the higher spatial resolutions needed to express heat flow at the scale of most ocean ridges. Ongoing simulations are focused on testing the impacts of horizontal resolution and flux scaling on surface environments, especially for “snowball” climate scenarios.

Heat flow↗

NASA NTRSDate not supplied

Solar Array Performance Modeling for NASA’s Artemis Missions

NASA’s Artemis exploration campaign includes multiple elements that utilize photovoltaic power generation. Artemis mission profiles feature a variety of events that can limit solar array generation and stress electrical power system (EPS) performance, including eclipses, propulsive or navigational maneuvers which constrain the positioning of the solar arrays, and spiral trajectories with significant radiation degradation. These features make predicting the performance of the power system particularly important and requires accurate modeling of power generation by the solar arrays under a variety of conditions. One of the tools used to predict spacecraft EPS performance is the System Power Analysis for Capability Evaluation (SPACE) model developed at NASA Glenn Research Center. SPACE is used by NASA to model EPS performance for the Orion crew transport vehicle and the Power and Propulsion Element (PPE) of the Gateway space station. This presentation will provide an overview of how SPACE calculates solar array performance, including degradation factors considered and environmental conditions that drive the solar array designs. Additionally, Orion array performance predictions generated by SPACE will be compared to flight data collected during the Artemis I mission.

Photovoltaic↗

DOE OSTI2021

Quantifying Turbine-Level Risk to Golden Eagles Using a High-Fidelity Updraft Model and a Stochastic Behavioral Model

To minimize the effects of wind farms on Golden Eagle (Aquila chrysaetos) populations while enabling sustainable development of renewable energy resources, it is important to understand how eagles interact with atmospheric flows, terrain features, and anthropogenic structures. Models that predict migratory flight paths provide one tool that helps us grasp how the location of wind farms may influence interactions and impacts on migrating Golden Eagles. The current state-of-the-art in predicting migratory flight paths uses a deterministic fluid-flow analogy to predict eagle trajectory using only an orographic updraft potential computed from topographical features. This model does not take into account variables, such as thermal updrafts and time varying atmospheric conditions that are known to influence migratory behavior. In this work, we improve on the model with the objective of developing tools that advance our understanding of how atmospheric flows and terrain features affect migratory eagle behavior and their interactions with wind farms. Specifically, we 1) incorporate both orographic and thermal updraft information in simulating eagle flight paths; 2) incorporate stochasticity into eagle travel patterns to better capture the influence of exogenous factors on, and the inherent stochasticity of eagle behavior; 3) consider spatio-temporal atmospheric data at wind-farm-scale when computing updraft potential; and 4) account for how atmospheric conditions and the direction of migration change seasonally and how these changes affect eagle migratory flight behavior. We tested the model using a 50km by 50km region with 50 m resolution in the western United States. We simulated 900 independent, probabilistic eagle tracks during southerly and northerly migration, assuming eagles solely rely on orographic updrafts. The preliminary results indicate that the inclusion of finer resolution atmospheric data allows for the inclusion of realistic conditions that an eagle experiences. The stochasticity in eagle tracks provides a platform to include uncertainty in eagle decision making and help produce robust eagle presence maps. We will deploy updraft and downdraft velocities computed using a high-fidelity, wind farm scale, computational fluid dynamics solver under development at National Renewable Energy Laboratory. This work is a first step in the development of a predictive and generalizable eagle behavior model at the wind farm scale that does not rely on empirical data collection. Although the current model is intended for migratory eagles, we will extend and refine this model to inform the development of additional behavioral modes, including resident eagle behavior. This modeling approach improves our ability to understand eagle use of the landscape at a fine scale, and it is our hope that this work will ultimately help advance strategies that minimize the impact of wind development on Golden Eagle populations.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2021

Magnetization dynamics fingerprints of an excitonic condensate t 2 g 4 magnet

The competition between spin-orbit coupling (SOC) λ and electron-electron interaction U leads to a plethora of novel states of matter, extensively studied in the context of t 2 g 4 and t 2 g 5 materials, such as ruthenates and iridates. Excitonic magnets—the antiferromagnetic state of bounded electron-hole pairs-are prominent examples of phenomena driven by those competing energy scales. Interestingly, recent theoretical studies predicted that excitonic magnets can be found in the ground state of SOC t 2 g 4 Hubbard models. Here we present a detailed computational study of the magnetic excitations in that excitonic magnet, employing one-dimensional chains (via density matrix renormalization group) and small two-dimensional clusters (via Lanczos). Specifically, first we show that the low-energy spectrum is dominated by a dispersive (acoustic) magnonic mode, with extra features arising from the λ = 0 state in the phase diagram. Second, and more importantly, we found a novel magnetic excitation forming a high-energy optical mode with the highest intensity at wave-vector q → 0 . In the excitonic condensation regime at large U , we also have found a novel high-energy π mode composed solely of orbital excitations. Overall, these features do not appear all together in any of the neighboring states in the phase diagram and thus constitute unique fingerprints of the t 2 g 4 excitonic magnet, of importance in the analysis of neutron and resonant inelastic x-ray scattering experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1994

Non-LTE model atmosphere analysis of Nova Cygni 1992

We use spherically symmetric non-local thermodynamic equilibrium (non-LTE), line-blanketed, expanding model atmospheres to analyze the International Ultraviolet Explorer (IUE) and optical spectra of Nova Cygni 1992 during the early phases of its outburst. We find that the first IUE spectrum obtained just after discovery on 1992 February 20, is best reproduced by a model atmosphere with a steep density gradient and homologous expansion, whereas the IUE and optical spectra obtained on February 24 show an extended, optically thick, wind structure. Therefore, we distinguish two phases of the early evolution of the nova photosphere: the initial, rapid, 'fireball' phase and the subsequent, much longer, optically thick 'wind' phase. The importance of line-blanketing in nova spectra is demonstrated. Our preliminary abundance analysis implies that hydrogen is depeleted in the ejecta, corresponding to abundance enhancements of Fe by a factor of approximately 2 and of CNO by more than a factor of 10 when compared to solar abundances. The synthetic spectra reproduce both the observed pseudo-continua as well as most of the observed features from the UV to the optical spectral range and demonstrate the importance of obtaining nearly simultaneous UV and optical spectra for performing accurate analyses of expanding stellar atmospheres (for both novae and supernovae).

Hauschildt, P. H.↗

NASA NTRS1980

Some problems of human adaptation and ecology under the aspect of general pathology

The main problems of human adaptation at the level of the body and the population in connection with the features of current morbidity of the population and certain demographic processes are analyzed. The concepts of health and adaptation of the individual and human populations are determined. The importance of the anthropo-ecological approach to the investigation of the adaptation process of human populations is demonstrated. Certain features of the etiopathogenesis of diseases are considered in connection with the population-ecological regularities of human adaptation. The importance of research on general pathology aspects of adaptation and the ecology of man for planning, and organization of public health protection is discussed.

Kaznacheyev, V. P.↗

DOE OSTI2023

Using limited neural networks to assess relative mechanistic influence on shock heating in granular solids

The rapid compaction of granular media results in localized heating that can induce chemical reactions, phase transformations, and melting. However, there are numerous mechanisms in play that can be dependent on a variety of microstructural features. Machine learning techniques such as neural networks offer a ubiquitous method to develop models for physical processes. Limiting what kind of microstructural information is used as an input and assessing normalized changes in network error, the relative importance of different mechanisms can be inferred. Here we utilize binned, initial density information as network inputs to predict local shock heating in a granular high explosive trained from large-scale molecular dynamics simulations. In this study, the spatial extent of the density field used in the network is altered to assess the importance and relevant length scales of the physical mechanisms in play, where different microstructural features result in different predictive capabilities.

36 MATERIALS SCIENCE↗

NASA NTRS2017

A Minimal State Augmentation Algorithm for Vision-Based Navigation without Using Mapped Landmarks

This paper describes MAVeN (Minimal State Augmentation Algorithm for Vision-Based Navigation), which is a new algorithm for vision-based navigation that has only 21 states, yet is able to track features in successive camera images and use them to propagate estimates of the spacecraft position and velocity. The filter dimension drops to 12 if attitude information is already available. The low filter dimension makes MAVeN a very reliable and practical algorithm for real-time flight implementation. The main idea is to project observed features onto a rough shape model of the ground surface, which are then used by the filter as pseudo-landmarks. The shape model is assumed to be known beforehand, as would be obtained from prior surveillance of the landing site from orbit. MAVeN does not require pre-mapped landmarks, so it is able to navigate terrain that has not been previously observed up close. This property is especially important for close proximity operations in small body missions where ground surface features are being seen for the first time at close range. MAVeN is also able to hover motionless above the ground without position error growth, which is unusual for this class of vision-based navigation algorithms.

San Martin, A. Miguel↗

NASA NTRS1981

Drumlin fields and glaciated mountains - A contrast in geomorphic perception from Seasat radar images

Digitally correlated Seasat synthetic-aperture radar (SAR) images of the Alaska Range, Alaska, and the drumlin-drift belt in Ireland are analyzed for the perception and identification of geomorphic features. The two terrains display strongly contrasted types of glacial topography whose identification in each case is related to the geometry of the Seasat imaging radar. Identification of terrain shape and form is important within the caveats imposed by the intrinsic distortions on the radar images. Image texture serves coarsely to distinguish topography. Image tones are scene-dependent and do not uniquely identify specific targets. Extensive alignments of linear and curvilinear features provide some of the more important image information from which to make geologic interpretations in each case.

Ford, J. P.↗

NASA NTRS1992

Variations in lithospheric thickness on Venus

Recent analyses of Magellan data have indicated many regions exhibiting topograhic flexure. On Venus, flexure is associated predominantly with coronae and the chasmata with Aphrodite Terra. Modeling of these flexural signatures allows the elastic and mechanical thickness of the lithosphere to be estimated. In areas where the lithosphere is flexed beyond its elastic limit the saturation moment provides information on the strength of the lithosphere. Modeling of 12 flexural features on Venus has indicated lithospheric thicknesses comparable with terrestrial values. This has important implications for the venusian heat budget. Flexure of a thin elastic plate due simultaneously to a line load on a continuous plate and a bending moment applied to the end of a broken plate is considered. The mean radius and regional topographic gradient are also included in the model. Features with a large radius of curvature were selected so that a two-dimensional approximation could be used. Comparisons with an axisymmetric model were made for some features to check the validity of the two-dimensional assumption. The best-fit elastic thickness was found for each profile crossing a given flexural feature. In addition, the surface stress and bending moment at the first zero crossing of each profile were also calculated. Flexural amplitudes and elastic thicknesses obtained for 12 features vary significantly. Three examples of the model fitting procedures are discussed.

Johnson, C. L.↗

DOE OSTI2023

Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits

Abstract Invasive plant pathogenic fungi have a global impact, with devastating economic and environmental effects on crops and forests. Biosurveillance, a critical component of threat mitigation, requires risk prediction based on fungal lifestyles and traits. Recent studies have revealed distinct genomic patterns associated with specific groups of plant pathogenic fungi. We sought to establish whether these phytopathogenic genomic patterns hold across diverse taxonomic and ecological groups from the Ascomycota and Basidiomycota, and furthermore, if those patterns can be used in a predictive capacity for biosurveillance. Using a supervised machine learning approach that integrates phylogenetic and genomic data, we analyzed 387 fungal genomes to test a proof-of-concept for the use of genomic signatures in predicting fungal phytopathogenic lifestyles and traits during biosurveillance activities. Our machine learning feature sets were derived from genome annotation data of carbohydrate-active enzymes (CAZymes), peptidases, secondary metabolite clusters (SMCs), transporters, and transcription factors. We found that machine learning could successfully predict fungal lifestyles and traits across taxonomic groups, with the best predictive performance coming from feature sets comprising CAZyme, peptidase, and SMC data. While phylogeny was an important component in most predictions, the inclusion of genomic data improved prediction performance for every lifestyle and trait tested. Plant pathogenicity was one of the best-predicted traits, showing the promise of predictive genomics for biosurveillance applications. Furthermore, our machine learning approach revealed expansions in the number of genes from specific CAZyme and peptidase families in the genomes of plant pathogens compared to non-phytopathogenic genomes (saprotrophs, endo- and ectomycorrhizal fungi). Such genomic feature profiles give insight into the evolution of fungal phytopathogenicity and could be useful to predict the risks of unknown fungi in future biosurveillance activities.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Comparing event generator predictions and ab initio calculations of ν- 12 C neutral-current quasielastic scattering at 1 GeV

The measurement of neutrino oscillations and exotic physics searches are important parts of the physics program in the near future, with new state-of-the-art experiments planned within the next decade. Future and modern experiments in these fields will make use of nuclear targets. Event generators (EGs) are software used in the analysis of neutrino oscillation experiments. EGs are used to predict kinematic observables for a range of neutrino energies. These simulations make use of simple models of nucleon dynamics. As such, they may fail to capture features of more rigorous theoretical calculations. This work compares EG performance to nuclear theory calculations by comparing observables generated in the two frameworks. We provide a common set of definitions between theory and experiment and assess the adequacy of the implementation of two nuclear physics models in EG simulations. Neutral-current quasielastic (NCQE) scattering events for neutrinos and antineutrinos on a 12 C target are simulated with a specific EG, NEUT, used by the T2K experiment for its analysis. The simulated cross sections are compared to analytic calculations from nuclear theory within the factorization scheme. We compare the NEUT implementation of two different models on nuclear spectral functions: the relativistic Fermi gas (RFG) and the correlated basis spectral function (CBF) to analytic calculations of the same models in the factorization scheme. For both nuclear physics models, we compare the appearance of features in the distributions relevant to experimental analyses. The peak of the cross section dσ/(dΩ dω) is consistent in energy transfer, ω, for RFG and CBF simulations. Qualitatively, the shape of the simulated distribution is similar to the one obtained through theory calculations; however, there are some discrepancies between the theory calculations and the NEUT simulation. While the EG simulations and analytic calculations with the same model of nuclear dynamics show similar overall features, there are still differences between the two. These results demonstrate the importance of benchmarking EGs so their nuclear physics implementations can be improved for the analysis of future experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

A neural network for determination of latent dimensionality in Nonnegative Matrix Factorization

Non-negative Matrix Factorization (NMF) has proven to be a powerful unsupervised learning method for uncovering hidden features in complex and noisy datasets with applications in data mining, text recognition, dimension reduction, face recognition, anomaly detection, blind source separation, and many other fields. An important input for NMF is the latent dimensionality of the data, that is, the number of hidden features, K, present in the explored dataset. Unfortunately, and this quantity is rarely known a priori. The existing methods for determining latent dimensionality, such as Automatic Relevance Determination (ARD), are mostly heuristic and utilize different characteristics to estimate the number of hidden features. However, all of them require human presence to make a final determination of K. Here we utilize a supervised machine learning approach in combination with a recent method for model determination, called NMFk, to determine the number of hidden features automatically. NMFk performs a set of NMF simulations on an ensemble of matrices, obtained by bootstrapping the initial dataset, and estimates which K produces stable groups of latent features that reconstruct the initial dataset well. We then train a Multi-Layter Perceptron (MLP) classifier network to determine the correct number of latent features utilizing the statistics and characteristics of the NMF solution, obtained from NMFk. In order to train the MLP classifier, a training set of 58,660 matrices with predetermined latent features were factorized with NMFk. The MLP classifier in conjunction with NMFk maintains a greater than 95% success rate when applied to a held out test set. Additionally, when applied to two well-known benchmark datasets, the swimmer and MIT face data, NMFk/MLP correctly recovers the established number of hidden features. Finally, we compare the accuracy of our method to the ARD, AIC and Stability-based methods.

97 MATHEMATICS AND COMPUTING↗