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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 289 records · Page 16
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NASA NTRS2003

MARIE Measurements and Model Predictions of Solar Modulation of Galactic Cosmic Rays at Mars

Recent data from the MARIE (Martian Radiation Environment Experiment) instrument on board the 2001 Mars Odyssey spacecraft currently in Mars orbit are presented. It is shown that the short-term modulations of galactic cosmic rays (GCR) are well described by correlating the so lar modulation parameter, <1>, with Earth-based neutron monitor counts using a 85-day time lag and the NASA Models - HZETRN (High Z and Energy Transport) and QMSFRG (Quantum Multiple Scattering theory of nuclear Fragmentation). The dose rates observed by the MARIE instrument are within 10% of the model calculations.

Saganti, P. B.↗
NASA NTRS2012

Modeled and Measured Dynamics of a Composite Beam with Periodically Varying Foam Core

The dynamics of a sandwich beam with carbon fiber composite facesheets and foam core with periodic variations in material properties are studied. The purpose of the study is to compare finite element predictions with experimental measurements on fabricated beam specimens. For the study, three beams were fabricated: one with a compliant foam core, a second with a stiffer core, and a third with the two cores alternating down the length of the beam to create a periodic variation in properties. This periodic variation produces a bandgap in the frequency domain where vibrational energy does not readily propagate down the length of the beam. Mode shapes and natural frequencies are compared, as well as frequency responses from point force input to velocity response at the opposite end of the beam.

Cabell, Randolph H.↗
NASA NTRS2012

Near-Nadiral Normalized Radar Cross Section of the SEA Surface at Ku, Ka, and W-Bands: Comparison of Measurements and Models

Understanding the relationship between wind speed and direction and the near-nadiral normalized radar cross section (NRCS) of the sea surface is important in many oceanographic and atmospheric remote sensing applications: (1) wind speed retrievals in traditional altimeter systems (2) assistance in calibration and path integrated attenuation processing for atmospheric profiling radars The desired wind speed (and direction in some cases) retrieval requires a clear understanding of the relationship between the relevant geophysical quantities and the observed NRCS Such understanding is available from existing electromagnetic models, but the presence of many such models, as well as implicit descriptions of the sea surface, motivates continued evaluation of model performance.

CloudSat↗
NASA NTRS2014

Improving FermiI Orbit Determination and Prediction in an Uncertain Atmospheric Drag Environment

Orbit determination and prediction of the Fermi Gamma-ray Space Telescope trajectory is strongly impacted by the unpredictability and variability of atmospheric density and the spacecrafts ballistic coefficient. Operationally, Global Positioning System point solutions are processed with an extended Kalman filter for orbit determination, and predictions are generated for conjunction assessment with secondary objects. When these predictions are compared to Joint Space Operations Center radar-based solutions, the close approach distance between the two predictions can greatly differ ahead of the conjunction. This work explores strategies for improving prediction accuracy and helps to explain the prediction disparities. Namely, a tuning analysis is performed to determine atmospheric drag modeling and filter parameters that can improve orbit determination as well as prediction accuracy. A 45 improvement in three-day prediction accuracy is realized by tuning the ballistic coefficient and atmospheric density stochastic models, measurement frequency, and other modeling and filter parameters.

orbit prediction↗
NASA NTRS2014

Improving Fermi Orbit Determination and Prediction in an Uncertain Atmospheric Drag Environment

Orbit determination and prediction of the Fermi Gamma-ray Space Telescope trajectory is strongly impacted by the unpredictability and variability of atmospheric density and the spacecraft's ballistic coefficient. Operationally, Global Positioning System point solutions are processed with an extended Kalman filter for orbit determination, and predictions are generated for conjunction assessment with secondary objects. When these predictions are compared to Joint Space Operations Center radar-based solutions, the close approach distance between the two predictions can greatly differ ahead of the conjunction. This work explores strategies for improving prediction accuracy and helps to explain the prediction disparities. Namely, a tuning analysis is performed to determine atmospheric drag modeling and filter parameters that can improve orbit determination as well as prediction accuracy. A 45% improvement in three-day prediction accuracy is realized by tuning the ballistic coefficient and atmospheric density stochastic models, measurement frequency, and other modeling and filter parameters.

orbit prediction↗
NASA NTRS2015

AG Channel Measurement and Modeling Results for Over-Water and Hilly Terrain Conditions

This report describes work completed over the past year on our project, entitled "Unmanned Aircraft Systems (UAS) Research: The AG Channel, Robust Waveforms, and Aeronautical Network Simulations." This project is funded under the NASA project "Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS)." In this report we provide the following: an update on project progress; a description of the over-freshwater and hilly terrain initial results on path loss, delay spread, small-scale fading, and correlations; complete path loss models for the over-water AG channels; analysis for obtaining parameter statistics required for development of accurate wideband AG channel models; and analysis of an atypical AG channel in which the aircraft flies out of the ground site antenna main beam. We have modeled the small-scale fading of these channels with Ricean statistics, and have quantified the behavior of the Ricean K-factor. We also provide some results for correlations of signal components, both intra-band and inter-band. An updated literature review, and a summary that also describes future work, are also included.

Matolak, David W.↗
NASA NTRS2015

Measurement and Modeling of Electromagnetic Scattering by Particles and Particle Groups

Small particles forming clouds of interstellar and circumstellar dust, regolith surfaces of many solar system bodies, and cometary atmospheres have a strong and often controlling effect on many ambient physical and chemical processes. Similarly, aerosol and cloud particles exert a strong influence on the regional and global climates of the Earth, other planets of the solar system, and exoplanets. Therefore, detailed and accurate knowledge of physical and chemical characteristics of such particles has the utmost scientific importance.

Interstellar matter↗
NASA NTRS2016

Measuring and Modeling Shared Visual Attention

Multi-person teams are sometimes responsible for critical tasks, such as flying an airliner. Here we present a method using gaze tracking data to assess shared visual attention, a term we use to describe the situation where team members are attending to a common set of elements in the environment. Gaze data are quantized with respect to a set of N areas of interest (AOIs); these are then used to construct a time series of N dimensional vectors, with each vector component representing one of the AOIs, all set to 0 except for the component corresponding to the currently fixated AOI, which is set to 1. The resulting sequence of vectors can be averaged in time, with the result that each vector component represents the proportion of time that the corresponding AOI was fixated within the given time interval. We present two methods for comparing sequences of this sort, one based on computing the time-varying correlation of the averaged vectors, and another based on a chi-square test testing the hypothesis that the observed gaze proportions are drawn from identical probability distributions.We have evaluated the method using synthetic data sets, in which the behavior was modeled as a series of activities, each of which was modeled as a first-order Markov process. By tabulating distributions for pairs of identical and disparate activities, we are able to perform a receiver operating characteristic (ROC) analysis, allowing us to choose appropriate criteria and estimate error rates.We have applied the methods to data from airline crews, collected in a high-fidelity flight simulator (Haslbeck, Gontar Schubert, 2014). We conclude by considering the problem of automatic (blind) discovery of activities, using methods developed for text analysis.

attention↗
NASA NTRS2016

Measuring and Modeling Shared Visual Attention

Multi-person teams are sometimes responsible for critical tasks, such as flying an airliner. Here we present a method using gaze tracking data to assess shared visual attention, a term we use to describe the situation where team members are attending to a common set of elements in the environment. Gaze data are quantized with respect to a set of N areas of interest (AOIs); these are then used to construct a time series of N dimensional vectors, with each vector component representing one of the AOIs, all set to 0 except for the component corresponding to the currently fixated AOI, which is set to 1. The resulting sequence of vectors can be averaged in time, with the result that each vector component represents the proportion of time that the corresponding AOI was fixated within the given time interval. We present two methods for comparing sequences of this sort, one based on computing the time-varying correlation of the averaged vectors, and another based on a chi-square test testing the hypothesis that the observed gaze proportions are drawn from identical probability distributions. We have evaluated the method using synthetic data sets, in which the behavior was modeled as a series of "activities," each of which was modeled as a first-order Markov process. By tabulating distributions for pairs of identical and disparate activities, we are able to perform a receiver operating characteristic (ROC) analysis, allowing us to choose appropriate criteria and estimate error rates. We have applied the methods to data from airline crews, collected in a high-fidelity flight simulator (Haslbeck, Gontar & Schubert, 2014). We conclude by considering the problem of automatic (blind) discovery of activities, using methods developed for text analysis.

attention↗
NASA NTRS2017

Ice Accretion Roughness Measurements and Modeling

Roughness on aircraft ice accretions is very important to the overall ice accretion process and to the resulting degradation in aircraft aerodynamic performance. Roughness enhances the local convection leading to more rapid ice accumulation rates, and roughness generates local flow perturbations that lead to higher skin friction. This paper presents 1) a review of the developments in ice shape three-dimensional laser scanning developed at NASA Glenn, 2) a review of the approach of McClain and Kreeger employed to characterize ice roughness evolution on an airfoil surface, and 3) a review of the experimental efforts that have been performed over the last five years to characterize, scale, and model ice roughness evolution physics.

SLD↗
NASA NTRS2015

Measuring and Modeling Shared Visual Attention

Multi-person teams are sometimes responsible for critical tasks, such as flying an airliner. Here we present a method using gaze tracking data to assess shared visual attention, a term we use to describe the situation where team members are attending to a common set of elements in the environment. Gaze data are quantized with respect to a set of N areas of interest (AOIs); these are then used to construct a time series of N dimensional vectors, with each vector component representing one of the AOIs, all set to 0 except for the component corresponding to the currently fixated AOI, which is set to 1. The resulting sequence of vectors can be averaged in time, with the result that each vector component represents the proportion of time that the corresponding AOI was fixated within the given time interval.We present two methods for comparing sequences of this sort, one based on computing the time varying correlation of the averaged vectors, and another based on a chi-square test testing the hypothesis that the observed gaze proportions are drawn from identical probability distributions.We have evaluated the method using synthetic data sets, in which the behavior was modeled as a series of activities, each of which was modeled as a first-order Markov process. By tabulating distributions for pairs of identical and disparate activities, we are able to perform a receiver operating characteristic (ROC) analysis, allowing us to choose appropriate criteria and estimate error rates. Using these criteria, we have applied the methods to data from airline crews, collected in a high-fidelity flight simulator (Gontar Hoermann, 2014). We conclude by considering the problem of automatic (blind) discovery of activities, using methods developed for text analysis.

Mulligan, Jeffrey B.↗
NASA NTRSDate not supplied

The Atmosphere Observing System (AOS): Synergistic Aerosol, Cloud, Convection and Precipitation Measurement and Modeling Systems

The 2017 Decadal Survey (DS) highlighted Earth System Science themes, science and application questions, and several high priority objectives that have led to the inclusion of Aerosols (A) and Clouds-Convection-Precipitation (CCP) as Designated Observables (DOs). On June 1, 2018, several NASA centers (GSFC, LaRC, JPL, MSFC, GRC and ARC) submitted a joint Study Plan to the NASA Earth Science Division for the Aerosol (A) and Cloud, Convection, and Precipitation (CCP) Pre-formulation Study (ACCP), with the ACCP Study concluding in early 2021. The new mission now in pre-phase A is being referred to as the Atmosphere Observing System (AOS), an integral part of NASA’s Earth System Observatory (ESO) strategy. The DS and the ACCP team recognized the science merit in combining the A and CCP DOs for both enhancing the ability to address a number of science objectives and also to provide an expanded capability to address additional objectives beyond those of the individual DOs. A critical element of the ACCP observing strategy is to make extensive use of new passive and active sensors as well as of the so-called Program-of-Record (PoR), complemented by a fully integrated sub-orbital component. In order to achieve maximum benefit, all these observations need to be integrated into comprehensive observing and modeling/data assimilation systems. Such an approach requires comprehensive model-data synthesis capabilities that needs to be conceived in conjunction with the space-based and suborbital components of AOS. In this presentation we will summarize the major science goals of AOS including cloud feedbacks, atmospheric convection, emphasizing aerosol processes and aerosol radiative effects, and the synergistic aspects of clouds-precipitation-aerosol interactions. We will describe examples of how AOS data will be used across space and time to better initialize forecasts and train modeling systems, and to infuse models and data assimilation systems with AOS data for advancing operational predictions and to generate expanded hindcasts and reconstruction of the climate record.

Arlindo da Silva↗