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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2013

Parameter Estimation for Compact Binary Coalescence Signals with the First Generation Gravitational-Wave Detector Network

Compact binary systems with neutron stars or black holes are one of the most promising sources for ground-based gravitational-wave detectors. Gravitational radiation encodes rich information about source physics; thus parameter estimation and model selection are crucial analysis steps for any detection candidate events. Detailed models of the anticipated waveforms enable inference on several parameters, such as component masses, spins, sky location and distance, that are essential for new astrophysical studies of these sources. However, accurate measurements of these parameters and discrimination of models describing the underlying physics are complicated by artifacts in the data, uncertainties in the waveform models and in the calibration of the detectors. Here we report such measurements on a selection of simulated signals added either in hardware or software to the data collected by the two LIGO instruments and the Virgo detector during their most recent joint science run, including a blind injection where the signal was not initially revealed to the collaboration. We exemplify the ability to extract information about the source physics on signals that cover the neutron-star and black-hole binary parameter space over the component mass range 1M25M and the full range of spin parameters. The cases reported in this study provide a snapshot of the status of parameter estimation in preparation for the operation of advanced detectors.

Aasi, J.↗
NASA NTRS2002

Large Scale Deformation of the Western U.S. Cordillera

Over the past couple of years, with support from NASA, we used a large collection of data from GPS, VLBI, SLR, and DORIS networks which span the Western U.S. Cordillera (WUSC) to precisely quantify present-day large-scale crustal deformations in a single uniform reference frame. Our work was roughly divided into an analysis of these space geodetic observations to infer the deformation field across and within the entire plate boundary zone, and an investigation of the implications of this deformation field regarding plate boundary dynamics. Following the determination of the first generation WUSC velocity solution, we placed high priority on the dissemination of the velocity estimates. With in-kind support from the Smithsonian Astrophysical Observatory, we constructed a web-site which allows anyone to access the data, and to determine their own velocity reference frame.

Bennett, Richard A.↗
NASA NTRS2002

Large Scale Deformation of the Western U.S. Cordillera

Over the past couple of years, with support from NASA, we used a large collection of data from GPS, VLBI, SLR, and DORIS networks which span the Westem U.S. Cordillera (WUSC) to precisely quantify present-day large-scale crustal deformations in a single uniform reference frame. Our work was roughly divided into an analysis of these space geodetic observations to infer the deformation field across and within the entire plate boundary zone, and an investigation of the implications of this deformation field regarding plate boundary dynamics. Following the determination of the first generation WUSC velocity solution, we placed high priority on the dissemination of the velocity estimates. With in-kind support from the Smithsonian Astrophysical Observatory, we constructed a web-site which allows anyone to access the data, and to determine their own velocity reference frame.

Bennett, Richard A.↗
NASA NTRS2005

PlanWorks: A Debugging Environment for Constraint Based Planning Systems

Numerous planning and scheduling systems employ underlying constraint reasoning systems. Debugging such systems involves the search for errors in model rules, constraint reasoning algorithms, search heuristics, and the problem instance (initial state and goals). In order to effectively find such problems, users must see why each state or action is in a plan by tracking causal chains back to part of the initial problem instance. They must be able to visualize complex relationships among many different entities and distinguish between those entities easily. For example, a variable can be in the scope of several constraints, as well as part of a state or activity in a plan; the activity can arise as a consequence of another activity and a model rule. Finally, they must be able to track each logical inference made during planning. We have developed PlanWorks, a comprehensive system for debugging constraint-based planning and scheduling systems. PlanWorks assumes a strong transaction model of the entire planning process, including adding and removing parts of the constraint network, variable assignment, and constraint propagation. A planner logs all transactions to a relational database that is tailored to support queries for of specialized views to display different forms of data (e.g. constraints, activities, resources, and causal links). PlanWorks was specifically developed for the Extensible Universal Remote Operations Planning Architecture (EUROPA(sub 2)) developed at NASA, but the underlying principles behind PlanWorks make it useful for many constraint-based planning systems. The paper is organized as follows. We first describe some fundamentals of EUROPA(sub 2). We then describe PlanWorks' principal components. We then discuss each component in detail, and then describe inter-component navigation features. We close with a discussion of how PlanWorks is used to find model flaws.

Daley, Patrick↗
NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.

Aiden C. Szeto↗
NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.

Aiden Szeto↗
NASA NTRS1995

Coronal structures deduced from photospheric magnetic field and He I lambda 10830 observations

The National Solar Observatory synoptic program provides an extensive and unique data base of high-resolution full-disk observations of the line-of-sight photospheric magnetic fields and of the He I lambda 10830 equivalent width. These data have been taken nearly daily for more than 21 years since 1974 and provide the opportunity to investigate the behavior of the magnetic fields in the photosphere and those inferred for the corona spanning on the time scales of a day to that of a solar cycle. The intensity of structures observed in He I lambda 10830 are strongly modulated by overlying coronal radiation; areas with low coronal emission are generally brighter in He I lambda 10830, while areas with high coronal emission are darker. For this reason, He I lambda 10830 was selected in the mid-1970's as way to identify and monitor coronal holes, magnetic fields with an open configuration, and the sources of high-speed solar wind streams. The He I lambda 10830 spectroheliograms also show a wide variety of other structures from small-scale, short-lived dark points (less than 30 arc-sec, hours) to the large-scale, long-lived two 'ribbon' flare events that follow the filament eruptions (1000 arc-sec, days). Such structures provide clues about the connections and changes in the large-scale coronal magnetic fields that are rooted in concentrations of magnetic network and active regions in the photosphere. In this paper, what observations of the photospheric magnetic field and He I lambda 10830 can tell us about the short- and long-term evolution of the coronal magnetic fields will be discussed, focussing on the quiet Sun and coronal holes. These data and what we infer from them will be compared with direct observations of the coronal structure from the Yohkoh Soft X-ray Telescope.

Harvey, Karen L.↗
NASA NTRS1989

Carrier phase ambiguity resolution for the Global Positioning System applied to geodetic baselines up to 2000 km

A technique for resolving the ambiguities in the GPS carrier phase data (which are biased by an integer number of cycles) is described which can be applied to geodetic baselines up to 2000 km in length and can be used with dual-frequency P code receivers. The results of such application demonstrated that a factor of 3 improvement in baseline accuracy could be obtained, giving centimeter-level agreement with coordinates inferred by very-long-baseline interferometry in the western United States. It was found that a method using pseudorange data is more reliable than one using ionospheric constraints for baselines longer than 200 km. It is recommended that future GPS networks have a wide spectrum of baseline lengths (ranging from baselines shorter than 100 km to those longer than 1000 km) and that GPS receivers be used which can acquire dual-frequency P code data.

Blewitt, Geoffrey↗
NASA NTRSDate not supplied

SafeDNN: Understanding and Verifying Neural Networks

The SafeDNN project at NASA Ames explores analysis techniques and tools to ensure that systems that use Deep Neural Networks (DNN) are safe, robust and interpretable. Research directions we are pursuing include: symbolic execution for DNN analysis, label-guided clustering to automatically identify input regions that are robust, parallel and compositional approaches to improve formal SMT-based verification, property inference and automated program repair for DNNs, adversarial training and detection, probabilistic reasoning for DNNs. In this talk I will highlight some of the research advances from SafeDNN, that were already published.

Corina Pasareanu↗
NASA NTRS2016

Binary Black Hole Mergers in the First Advanced LIGO Observing Run

The first observational run of the Advanced LIGO detectors, from September 12, 2015 to January 19, 2016, saw the first detections of gravitational waves from binary black hole mergers. In this paper we present full results from a search for binary black hole merger signals with total masses up to 100M solar mass and detailed implications from our observations of these systems. Our search, based on general-relativistic models of gravitational wave signals from binary black hole systems, unambiguously identified two signals, GW150914 and GW151226, with a significance of greater than 5 alpha over the observing period. It also identified a third possible signal, LVT151012, with substantially lower significance, which has a 87 probability of being of astrophysical origin. We provide detailed estimates of the parameters of the observed systems. Both GW150914 and GW151226 provide an unprecedented opportunity to study the two-body motion of a compact-object binary in the large velocity, highly nonlinear regime. We do not observe any deviations from general relativity, and place improved empirical bounds on several high-order post-Newtonian coefficients. From our observations we infer stellar-mass binary black hole merger rates lying in the range 9-240 Gpc-3 yr-1. These observations are beginning to inform astrophysical predictions of binary black hole formation rates, and indicate that future observing runs of the Advanced detector network will yield many more gravitational wave detections.

Abbott, B. P.↗
NASA NTRSDate not supplied

PM 2.5 Concentrations over Major Metropolitan Regions Inferred from Airborne High Spectral Resolution Lidar Measurements Using Machine Learning Regression

We use measurements of near-surface aerosol backscatter, extinction, and depolarization acquired by four NASA Langley Research Center airborne High Spectral Resolution Lidars (HSRLs) to develop a machine learning regression methodology to infer PM2.5 concentrations at the surface and aloft. These airborne HSRL measurements were acquired over major metropolitan regions in the United States and Asia during more than 170 flights since 2010. Hourly surface PM2.5 measurements from the EPA air quality system and similar networks in other countries acquired within 10 km and 15 minutes of these near-surface HSRL measurements are used to train models that compute PM2.5 concentrations from the HSRL measurements. We examine several regression methods and find that exponential Gaussian Process algorithms consistently give the best performance in terms of the lowest root-mean-square (RMS) errors and the highest correlations. Model performance varies significantly depending on various combinations of HSRL aerosol measurements (e.g., aerosol backscatter, extinction, depolarization, backscatter color ratios, lidar ratios, aerosol optical thickness) and retrievals (e.g., mixed layer height, aerosol type) used in the regressions. Models that use near-surface measurements of aerosol backscatter and aerosol intensive properties such as depolarization, backscatter color ratio, and lidar ratio typically give the best performance with RMS errors around 4 mg/m3 and correlation coefficients above 0.9. HSRL measurements were often acquired when the aircraft flew systematic “raster-scan” patterns for several hours over these cities. These flight patterns enabled measurements of the spatial, temporal, and vertical variabilities in the distributions of aerosol backscatter and aerosol intensive properties and allowed us to derive the corresponding variabilities in PM2.5 concentrations. We present examples of such variabilities over urban areas in the United States as well as Asia. We describe also how the distribution of surface PM2.5 varies with aerosol type and use these retrievals to examine model simulations of surface PM2.5 in these metropolitan regions. We also discuss how this methodology may be applied to measurements from satellite lidars such as CALIOP on CALIPSO and ATLID on EarthCARE.

lidar↗
NASA NTRS2001

Inferring Instantaneous, Multivariate and Nonlinear Sensitivities for the Analysis of Feedback Processes in a Dynamical System: Lorenz Model Case Study

A new approach is presented for the analysis of feedback processes in a nonlinear dynamical system by observing its variations. The new methodology consists of statistical estimates of the sensitivities between all pairs of variables in the system based on a neural network modeling of the dynamical system. The model can then be used to estimate the instantaneous, multivariate and nonlinear sensitivities, which are shown to be essential for the analysis of the feedbacks processes involved in the dynamical system. The method is described and tested on synthetic data from the low-order Lorenz circulation model where the correct sensitivities can be evaluated analytically.

Aires, Filipe↗
NASA NTRS2000

Remotely Sensed Precipitation from the January 2000 East Coast Blizzard

Initial precipitation forecasts of the January 2000 blizzard were unable to adequately alert emergency response along the eastern seaboard. Cyclogenesis, over the Gulf of Mexico was a contributing cause of that problem. We present distributions of precipitation derived from spaceborne microwave measurements from the Special Sensor Microwave/ Imager (SSM/I) and the TRMM Microwave Imager (TMI) supplemented by NWS operational radar and the National lightning Detection Network (NLDN) Long-Range (LR) product. The combined NWS radar and NLDN-LR data revealed the location of precipitation between 1/23 and 1/27/2000 over the US, the Gulf and the Atlantic Ocean. The remotely sensed precipitation distributions are compared to the precipitation inferred from coincident TMI observations.

Chang, D.-E.↗
NASA NTRS2002

Combined Use of Satellite and Surface Observations to Infer the Imaginary Part of Refractive Index of Saharan Dust

We present a method for retrieval of the imaginary part of refractive index of desert dust aerosol in the near UV part of spectrum. The method uses Total Ozone Mapping Spectrometer (TOMS) measurements of the top of the atmosphere radiances at 331 and 360 run and aerosol optical depth provided by the Aerosol Robotic Network (AERONET). Obtained values of imaginary part of refractive index retrieved for Saharan dust aerosol at 360 nm are significantly lower than previously reported values. The average retrieved values vary between 0.0054 and 0.0066 for different geographical locations. Our findings are in good agreement with the results of several recent investigations.

Sinyuk, Alexander↗
NASA NTRS1993

Warm neutral halos around molecular clouds. V - OH (1665 and 1667 MHz) observations

Ten strip maps of 1665- and 1667-MHz OH emission, traversing the outer boundaries of five molecular clouds, were made. The OH emission is found to be significantly extended relative to CO, from which it inferred that OH is to be found in abundance in the shell of partly atomic, partly molecular gas surrounding the dense molecular clouds. The fractional OH abundance is calculated using existing H I and CO observations, and detailed source models which include a complete chemistry network and a radiative transfer code. It is concluded that the extended OH is formed, not by the exothermic reaction of O with H3(+), but by the endothermic reaction, H(+) + O yields H + O(+).

Wannier, Peter G.↗
NASA NTRS1972

Improvement of synoptic scale moisture and wind field analyses using the Nimbus 4 THIR 6.7 micron observations

The Nimbus 4 temperature-humidity infrared radiometer (THIR) monitors radiation in the 6.5 to 7.2 micron water vapor absorption with a 23 kilometer spatial resolution at the sub-satellite point. Radiation monitored in this spectral region results primarily from emission in the 250 to 500 millibar region of the upper troposphere. The THIR 6.7 micron observations are readily available in photofacsimile imagery form which shows very distinctive patterns associated with spatial variations in atmospheric water vapor. These radiometric observations were combined in several instances with moisture values measured in the upper troposphere by the standard radiosonde network. In each instance, the result is a much more consistent analysis showing increased spatial detail that agrees with the radiometric observations and does not compromise the conventional data. The improved moisture analyses show relatively dry and moist tongues that are very difficult or impossible to infer from the conventional data alone. The patterns in the moisture fields can be tracked over 12 and 24 hour periods. In addition, by keeping in mind the advective properties of the moisture field, success was achieved in improving streamline analyses at the 400 mb level over data sparse regions on a global scale.

Steranko, J.↗
NASA NTRS1973

Application of Nimbus 4 THIR 6.7-micron observations to regional and global moisture and wind field analyses.

The Nimbus 4 temperature-humidity infrared radiometer (THIR) monitors radiation in the 6.5 to 7.2-micron water vapor absorption region with a 23-km spatial resolution at the subsatellite point. Radiation monitored in this spectral region results primarily from emission in the 250 to 500-mb region of the upper troposphere. The THIR 6.7-micron observations are readily available in photofacsimile imagery form which shows very distinctive patterns associated with spatial variations in atmospheric water vapor. These radiometric observations have been combined in several instances with moisture values measured in the upper troposphere by the standard radiosonde network. In each instance, the result is a much more consistent analysis showing increased spatial detail that agrees with the radiometric observations and does not compromise the conventional data. The improved moisture analyses show relatively dry and moist tongues that are very difficult or impossible to infer from the conventional data alone.

Steranka, J.↗
NASA NTRS1991

First and subsequent return stroke properties of cloud-to-ground lightning

Lightning properties obtained by a network of magnetic direction finders and by electric field measurements for distances from 50 to 500 km are compared for three summer thunderstorms in Sweden. The data from direct field recordings indicate 31, 17, and 26 pcts. of negative subsequent return strokes with peak current (as inferred from the peak electric field) higher than the first. Electric fields from first strokes are compared with normalized amplitudes registered by the magnetic direction finding system. The efficiency of detection by the magnetic direction finding system is discussed in terms of the percentage of lightning flashes observed by electric field measurements that are not localized. Statistics of the number of strokes per flash and the interstroke time intervals are presented.

Namasivayam, S.↗