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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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The optical depth and reflectance from thin cirrus clouds are calculated from two passes of AVIRIS over a scene in Florida. One of the passes contained partial cirrus cover, the other did not. Optical depths of 0.2 were calculated and the reflectance of the cloud at 1.381 micrometers was 0.026 The detection limit for cirrus is approximately an order of magnitude less in reflectance.
Leaky Lamb wave (LLW) propagation in composite materials has been studied extensively since it was first observed in 1982. The wave is induced using a pitch-catch arrangement and the plate wave modes are detected by searching minima in the reflected spectra.
This study describes and demonstrates different techniques for surfacing daily environmental / hazards data of particulate matter with aerodynamic diameter less than or equal to 2.5 micrometers (PM2.5) for the purpose of integrating respiratory health and environmental data for the Centers for Disease Control and Prevention (CDC s) pilot study of Health and Environment Linked for Information Exchange (HELIX)-Atlanta. It described a methodology for estimating ground-level continuous PM2.5 concentrations using B-Spline and inverse distance weighting (IDW) surfacing techniques and leveraging National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectrometer (MODIS) data to complement The Environmental Protection Agency (EPA) ground observation data. The study used measurements of ambient PM2.5 from the EPA database for the year 2003 as well as PM2.5 estimates derived from NASA s satellite data. Hazard data have been processed to derive the surrogate exposure PM2.5 estimates. The paper has shown that merging MODIS remote sensing data with surface observations of PM2.5 not only provides a more complete daily representation of PM2.5 than either data set alone would allow, but it also reduces the errors in the PM2.5 estimated surfaces. The results of this paper have shown that the daily IDW PM2.5 surfaces had smaller errors, with respect to observations, than those of the B-Spline surfaces in the year studied. However the IDW mean annual composite surface had more numerical artifacts, which could be due to the interpolating nature of the IDW that assumes that the maxima and minima can occur only at the observation points. Finally, the methods discussed in this paper improve temporal and spatial resolutions and establish a foundation for environmental public health linkage and association studies for which determining the concentrations of an environmental hazard such as PM2.5 with good accuracy levels is critical.
This software provides an automated capability to measure and qualify the frequency stability performance of the Deep Space Network (DSN) ground system, using daily spacecraft tracking data. The results help to verify if the DSN performance is meeting its specification, therefore ensuring commitments to flight missions; in particular, the radio science investigations. The rich set of data also helps the DSN Operations and Maintenance team to identify the trends and patterns, allowing them to identify the antennas of lower performance and implement corrective action in a timely manner. Unlike the traditional approach where the performance can only be obtained from special calibration sessions that are both time-consuming and require manual setup, the new method taps into the daily spacecraft tracking data. This new approach significantly increases the amount of data available for analysis, roughly by two orders of magnitude, making it possible to conduct trend analysis with good confidence. The software is built with automation in mind for end-to-end processing. From the inputs gathering to computation analysis and later data visualization of the results, all steps are done automatically, making the data production at near zero cost. This allows the limited engineering resource to focus on high-level assessment and to follow up with the exceptions/deviations. To make it possible to process the continual stream of daily incoming data without much effort, and to understand the results quickly, the processing needs to be automated and the data summarized at a high level. Special attention needs to be given to data gathering, input validation, handling anomalous conditions, computation, and presenting the results in a visual form that makes it easy to spot items of exception/ deviation so that further analysis can be directed and corrective actions followed.
This software provides an automated capability to measure and qualify the frequency stability performance of the Deep Space Network (DSN) ground system, using daily spacecraft tracking data. The results help to verify if the DSN performance is meeting its specification, therefore ensuring commitments to flight missions; in particular, the radio science investigations. The rich set of data also helps the DSN Operations and Maintenance team to identify the trends and patterns, allowing them to identify the antennas of lower performance and implement corrective action in a timely manner. Unlike the traditional approach where the performance can only be obtained from special calibration sessions that are both time-consuming and require manual setup, the new method taps into the daily spacecraft tracking data. This new approach significantly increases the amount of data available for analysis, roughly by two orders of magnitude, making it possible to conduct trend analysis with good confidence. The software is built with automation in mind for end-to-end processing. From the inputs gathering to computation analysis and later data visualization of the results, all steps are done automatically, making the data production at near zero cost. This allows the limited engineering resource to focus on high-level assessment and to follow up with the exceptions/deviations. To make it possible to process the continual stream of daily incoming data without much effort, and to understand the results quickly, the processing needs to be automated and the data summarized at a high level. Special attention needs to be given to data gathering, input validation, handling anomalous conditions, computation, and presenting the results in a visual form that makes it easy to spot items of exception/deviation so that further analysis can be directed and corrective actions followed.
This paper describes NASA's initial steps for identifying and evaluating candidate Exploration Zones (EZs) and Regions of Interests (ROIs) for the first human crews that will explore the surface of Mars. NASA's current effort to define the exploration of this planet by human crews, known as the Evolvable Mars Campaign (EMC), provides the context in which these EZs and ROIs are being considered. The EMC spans all aspects of a human Mars mission including launch from Earth, transit to and from Mars, and operations on the surface of Mars. An EZ is a collection of ROIs located within approximately 100 kilometers of a centralized landing site. ROIs are areas relevant for scientific investigation and/or development/maturation of capabilities and resources necessary for a sustainable human presence. The EZ also contains one or more landing sites and a habitation site that will be used by multiple human crews during missions to explore and utilize the ROIs within the EZ. With the EMC as a conceptual basis, the EZ model has been refined to a point where specific site selection criteria for scientific exploration and in situ resource utilization can be defined. In 2015 these criteria were distributed to the planetary sciences community and the in situ resource utilization and civil engineering communities as part of a call for EZ proposals. The resulting "First Landing Site/Exploration Zone Workshop for Human Missions to the Surface of Mars" was held in October 2015 during which 47 proposals for EZs and ROIs were presented and discussed. Proposed locations spanned all longitudes and all allowable latitudes (+/- 50 degrees). Proposed justification for selecting one of these EZs also spanned a significant portion of the scientific and resource criteria provided to the community. Several important findings resulted from this Workshop including: (a) a strong consensus that, at a scale of 100 km (radius), multiple places on Mars exist that have both sufficient scientific interest to sustain multiple crews of exploring astronauts, AND potential resource deposits for ISRU indicating the current EZ definition is viable and should be retained for now, (b) new data types (needed for more definitive analysis of EZs) argued strongly for a new orbiter mission, and possibly one or more surface missions, to obtain these data, (c) a general consensus that this Workshop was an excellent start to identifying a place where future human missions to Mars can productively explore this planet and learn to live and work there for the long term. Building on these findings, HEOMD and SMD are: (a) refining the EZ selection criteria and overall selection process to improve on lessons learned from the first EZ workshop, (b) using these proposed locations to develop "reference EZs" for assessment purposes (primarily engineering assessments), (c) gathering data and conducting analyses to better understanding the different potential sources for water, including the ease of extraction and purification, and (d) assessing trends in additional data that are needed to better characterize EZs proposed at the workshop and how these data needs impact the design and operation of future robotic Mars missions.
Introduction to Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) Giovanni … is a Web-based visualization and analysis system that provides 22 different visualization and analysis options, operating on thousands of Earth science data variables generated by satellite instrument observations and from related model datasets Giovanni … was originally conceived as a data exploration tool, but its ease-of-use, analytical capabilities (spatial and temporal subsetting, multi-period averaging, data mapping and time-series, and more) have led to its use as a multi-discipline research tool Giovanni … provided unprecedented access to NASA Earth science data for many different disciplines, AND is still providing a simple way to find, analyze, visualize, and utilize such data for a wide spectrum of research topics
The increasing number of space missions have resulted in an augmented density of artificial objects positioned in orbital domains near Earth. Knowing the material composition of the resident space object can be of value in object identification and risk assessment using remote sensing techniques. To perform this task, it is increasingly imperative to optically characterize spacecraft materials to identify unique material-specific spectroscopic markers. In this work, a variety of materials frequently utilized by the aerospace industry in spacecraft design and construction were analyzed using reflectance spectroscopy. The collected data provide a spectral characterization baseline for modern-day and historical spacecraft materials. The color index was computed for standard astronomical and suggested theoretical filter passbands and compared to each other in their capability to discriminate materials belonging to different classes. The color index was calculated from reflectance spectra of common spacecraft materials in their pristine, as-received conditions that fell under different family groupings. Different color-index combinations were studied using color-color diagrams. Visual and quantitative analysis of the color-color diagrams were used to evaluate the possibility of discriminating materials from one another by means of optical measurements. Results of the analysis show that polyimide and photovoltaic materials are most easy to discriminate from all other materials via color indices using different filter passbands evaluated in this study.
Abstract Not Provided
Research on harvesting, collection, and storage has matured, and our understanding of feedstock supply challenges has led us to the point of our current goals. The goals of this project include using storage time to collect impactful data of critical feedstock properties in storage and to characterize the feedstock before delivery. This data will inform delivery scheduling of biomass and relevant compositional information to minimize variations in the material quality, which are critical to the preprocessing and conversion performance.
Abstract not provided.
Accurately estimated auto-spectral density functions are essential for characterization of turbulent flows, and they also have applications in computational fluid dynamics modeling, site and inflow characterization for hydrokinetic turbines, and inflow turbulence generation. The Acoustic Doppler Velocimeter (ADV) provides single-point temporally resolved data, that are used to characterize turbulent flows in rivers, seas, and oceans. However, ADV data are susceptible to contamination from various sources, including instrument noise, which is the intrinsic limit to the accuracy of acoustic velocity measurements. Due to the presence of instrument noise, the spectra obtained are altered at high frequencies. The focus of this study is to develop a robust and effective method for accurately estimating auto-spectral density functions from ADV data by reducing or removing the spectral contribution derived from instrument noise. For this purpose, the “Noise Auto-Correlation” (NAC) approach was developed, which exploits the correlation properties of instrument noise to identify and remove its contribution from spectra. The spectra estimated using the NAC approach exhibit increased fidelity and a slope of -5/3 in the inertial range, which is typically observed for turbulent flows. Finally, this study also compares the effectiveness of low-pass Gaussian filters in removing instrument noise with that of the NAC approach. For the data used in this study, both the NAC and Gaussian filter approaches are observed to be capable of removing instrument noise at higher frequencies from the spectra. However, the NAC results are closer to the expected frequency power of -5/3 in the inertial sub-range.
This paper reports computational simulations and analysis in support of calibration and flow characterization tests in a high enthalpy arc-jet facility at NASA Ames Research Center. These tests were conducted in the NASA Ames 10-MW TP3 facility using flat-faced stagnation calorimeters at six conditions corresponding to the steps of a simulated flight heating profile. Data were obtained using a conical nozzle test configuration in which the models were placed in a free jet downstream of the nozzle. Experimental surveys of arc-jet test flow with pitot pressure and heat flux probes were also performed at these arc-heater conditions, providing assessment of the flow uniformity and valuable data for the flow characterization. Two different sets of pitot pressure and heat probes were used: 9.1-mm sphere-cone probes (nose radius of 4.57 mm or 0.18 in) with null-point heat flux gages, and 15.9-mm (0.625 in) diameter hemisphere probes with Gardon gages. The probe survey data clearly show that the test flow in the TP3 facility is not uniform at most conditions (not even axisymmetric at some conditions), and the extent of non-uniformity is highly dependent on various arc-jet parameters such as arc current, mass flow rate, and the amount of cold-gas injection at the arc-heater plenum. The present analysis comprises computational fluid dynamics simulations of the nonequilibrium flowfield in the facility nozzle and test box, including the models tested. Comparisons of computations with the experimental measurements show reasonably good agreement except at the extreme low pressure conditions of the facility envelope.
This paper reports computational simulations and analysis in support of calibration and flow characterization tests in a high enthalpy arc-jet facility at NASA Ames Research Center. These tests were conducted in the NASA Ames 10-MW TP3 facility using flat-faced stagnation calorimeters at six conditions corresponding to the steps of a simulated flight heating profile. Data were obtained using a conical nozzle test configuration in which the models were placed in a free jet downstream of the nozzle. Experimental surveys of arc-jet test flow with pitot pressure and heat flux probes were also performed at these arc-heater conditions, providing assessment of the flow uniformity and valuable data for the flow characterization. Two different sets of pitot pressure and heat probes were used: 9.1-mm sphere-cone probes (nose radius of 4.57 mm or 0.18 in) with null-point heat flux gages, and 15.9-mm (0.625 in) diameter hemisphere probes with Gardon gages. The probe survey data clearly show that the test flow in the TP3 facility is not uniform at most conditions (not even axisymmetric at some conditions), and the extent of non-uniformity is highly dependent on various arc-jet parameters such as arc current, mass flow rate, and the amount of cold-gas injection at the arc-heater plenum. The present analysis comprises computational fluid dynamics simulations of the nonequilibrium flowfield in the facility nozzle and test box, including the models tested. Comparisons of computations with the experimental measurements show reasonably good agreement except at the extreme low pressure conditions of the facility envelope.
A fast 3-D object recognition algorithm that can be used as a quick-look subsystem to the vision system for the Special-Purpose Dexterous Manipulator (SPDM) is described. Global features that can be easily computed from range data are used to characterize the images of a viewer-centered model of an object. This algorithm will speed up the processing by eliminating the low level processing whenever possible. It may identify the object, reject a set of bad data in the early stage, or create a better environment for a more powerful algorithm to carry the work further.
The research reported here is a part of NASA's Synthetic Vision System (SVS) project for the development of a High Speed Civil Transport Aircraft (HSCT). One of the components of the SVS is a module for detection of potential obstacles in the aircraft's flight path by analyzing the images captured by an on-board camera in real-time. Design of such a module includes the selection and characterization of robust, reliable, and fast techniques and their implementation for execution in real-time. This report describes the results of our research in realizing such a design. It is organized into three parts. Part I. Data modeling and camera characterization; Part II. Algorithms for detecting airborne obstacles; and Part III. Real time implementation of obstacle detection algorithms on the Datacube MaxPCI architecture. A list of publications resulting from this grant as well as a list of relevant publications resulting from prior NASA grants on this topic are presented.
We present the application of a novel method of time-series analysis, the Hilbert-Huang Transform, to the search for gravitational waves. This algorithm is adaptive and does not impose a basis set on the data, and thus the time-frequency decomposition it provides is not limited by time-frequency uncertainty spreading. Because of its high time-frequency resolution it has important applications to both signal detection and instrumental characterization. Applications to the data analysis of the ground and space based gravitational wave detectors, LIGO and LISA, are described.
The TROPoe (formerly called AERIoe) algorithm (Turner and Löhnert 2014, 2021; Turner and Blumberg 2019) retrieves profiles of temperature and water vapor mixing ratio, together with cloud properties for a single-layer cloud (i.e., LWP, effective radius), from AERI-observed infrared radiance spectrum. The data can be used to characterize the evolution of the planetary boundary layer and boundary layer clouds. This data set was collected at the ARM SGP Central Facility (CF) during and after the MicroPulse DIAL Demonstration (MPDDEMO) field campaign. The TROPoe retrieval was run at 5-minute resolution, but retrievals can be performed at the maximum temporal resolution of the AERI instrument (order 30 s) -- if this higher temporal resolution is desired, please contact Dave Turner. This is a physical-iterative retrieval method. The retrieval of thermodynamic profiles from spectral infrared radiance observations is an ill-posed problem, and thus constraints need to be included in the retrieval algorithm to provide physically plausible results. Here, we use a climatology of radiosonde profiles collected at the ARM SGP site during the summer as our prior information in an optimal estimation framework. Surface meteorology and output from the rapid-refresh (RAP) numerical weather prediction model (above 4 km) were used as additional constraints by the retrieval. As the method uses an optimal estimation framework, a full error covariance matrix of each solution is included in the output file. The 1-sigma uncertainty of each retrieved variable, which is derived from the error covariance matrix, is included for each scientific field and is named "sigma_X", where "X" is the name of the scientific field (e.g., 'temperature'). The information content in the AERI observations, which is in the "dfs" field, on the thermodynamic profiles is primarily concentrated in the lowest 3 km or up to cloud base; the retrieved data should not be used above that level (or used with caution). There is an overall "qc_flag" field that is set when a retrieval should not be trusted. However, the logic that sets this flag did not work correctly, and it should not be used. Instead, consider all retrievals that have a value for "converged_flag" greater than 0 as valid. If you have any questions, contact Dave Turner. This is Release_2_12 of the TROPoe algorithm.