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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

GRAS NRT Precise Orbit Determination: Operational Experience

EUMETSAT launched the meteorological satellite MetOp-A in October 2006; it is the first of the three satellites that constitute the EUMETSAT Polar System (EPS) space segment. This satellite carries a challenging and innovative instrument, the GNSS Receiver for Atmospheric Sounding (GRAS). The goal of the GRAS instrument is to support the production of atmospheric profiles of temperature and humidity with high accuracy, in an operational context, based on the bending of the GPS signals traversing the atmosphere during the so-called occultation periods. One of the key aspects associated to the data processing of the GRAS instrument is the necessity to describe the satellite motion and GPS receiver clock behaviour with high accuracy and within very strict timeliness limitations. In addition to these severe requirements, the GRAS Product Processing Facility (PPF) must be integrated in the EPS core ground segment, which introduces additional complexity from the data integration and operational procedure points of view. This paper sets out the rationale for algorithm selection and the conclusions from operational experience. It describes in detail the rationale and conclusions derived from the selection and implementation of the algorithms leading to the final orbit determination requirements (0.1 mm/s in velocity and 1 ns in receiver clock error at 1 Hz). Then it describes the operational approach and extracts the ideas and conclusions derived from the operational experience.

MartinezFadrique, Francisco M.↗
NASA NTRS2020

Julia Programming Language Benchmark Using a Flight Simulation

Julia’s goal to provide scripting language ease-of-coding with compiled language speed is explored. The runtime speed of the relatively new Julia programming language is assessed against other commonly used languages including Python, Java, and C++. An industry-standard missile and rocket simulation, coded in multiple languages, was used as a test bench for runtime speed. All language versions of the simulation, including Julia, were coded to a highly-developed object-oriented simulation architecture tailored specifically for time-domain flight simulation. A “speed-of-coding” second-dimension is plotted against runtime for each language to portray a space that characterizes Julia’s scripting language efficiencies in the context of the other languages. With caveats, Julia runtime speed was found to be in the class of compiled or semi-compiled languages. However, some factors that affect runtime speed at the cost of ease-of-coding are shown. Julia’s built-in functionality for multi-core processing is briefly examined as a means for obtaining even faster runtime speed. The major contribution of this research to the extensive language benchmarking body-of-work is comparing Julia to other mainstream languages using a complex flight simulation as opposed to benchmarking with single algorithms.

Sells, Ray↗
NASA NTRS2009

Communication Optimizations for a Wireless Distributed Prognostic Framework

Distributed architecture for prognostics is an essential step in prognostic research in order to enable feasible real-time system health management. Communication overhead is an important design problem for such systems. In this paper we focus on communication issues faced in the distributed implementation of an important class of algorithms for prognostics - particle filters. In spite of being computation and memory intensive, particle filters lend well to distributed implementation except for one significant step - resampling. We propose new resampling scheme called parameterized resampling that attempts to reduce communication between collaborating nodes in a distributed wireless sensor network. Analysis and comparison with relevant resampling schemes is also presented. A battery health management system is used as a target application. A new resampling scheme for distributed implementation of particle filters has been discussed in this paper. Analysis and comparison of this new scheme with existing resampling schemes in the context for minimizing communication overhead have also been discussed. Our proposed new resampling scheme performs significantly better compared to other schemes by attempting to reduce both the communication message length as well as number total communication messages exchanged while not compromising prediction accuracy and precision. Future work will explore the effects of the new resampling scheme in the overall computational performance of the whole system as well as full implementation of the new schemes on the Sun SPOT devices. Exploring different network architectures for efficient communication is an importance future research direction as well.

Saha, Sankalita↗
NASA NTRS1999

Comparisons of Monthly Oceanic Rainfall Derived from TMI and SSM/I

A technique for estimating monthly oceanic rainfall rate using multi-channel microwave measurements has been developed. There are three prominent features of this algorithm. First, the knowledge of the form of the rainfall intensity probability density function used to augment the measurements. Second, utilizing a linear combination of the 19.35 and 22.235 GHz channels to de-emphasize the effect of water vapor. Third, an objective technique has been developed to estimate the rain layer thickness from the 19.35 and 22.235 GHz brightness temperature histograms. This technique is applied to the SSM/I data since 1987 to infer monthly rainfall for the Global Precipitation Climatology Project (GPCP). A modified version of this algorithm is now being applied to the TRMM Microwave Imager (TMI) data. TMI data with better spatial resolution and 24 hour sampling (vs. sun-synchronized sampling, which is limited to two narrow intervals of local solar time for DMSP satellites) prompt us to study the similarity and difference between these two rainfall estimates. Six months of rainfall data (January to June 1998) are used in this study. Means and standard deviations are calculated. Paired student t-tests are administrated to evaluate the differences between rainfall estimates from SSM/I and TMI data. Their differences are discussed in the context of global satellite rainfall estimation.

Chang, A. T. C.↗
NASA NTRS2015

Insight into the Physical and Dynamical Processes that Control Rapid Increases in Total Flash Rate

Rapid increases in total lightning (also termed "lightning jumps") have been observed for many decades. Lightning jumps have been well correlated to severe and hazardous weather occurrence. The main focus of lightning jump work has been on the development of lightning algorithms to be used in real‐time assessment of storm intensity. However, in these studies it is typically assumed that the updraft "increases" without direct measurements of the vertical motion, or specification of which updraft characteristic actually increases (e.g., average speed, maximum speed, or convective updraft volume). Therefore, an end‐to‐end physical and dynamical basis for coupling rapid increases in total flash rate to increases in updraft speed and volume must be understood in order to ultimately relate lightning occurrence to severe storm metrics. Herein, we use polarimetric, multi‐Doppler, and lightning mapping array measurements to provide physical context as to why rapid increases in total lightning are closely tied to severe and hazardous weather.

Schultz, Christopher J.↗
NASA NTRS1996

Estimating the Thickness of Sea Ice Snow Cover in the Weddell Sea from Passive Microwave Brightness Temperatures

Passive microwave satellite observations have frequently been used to observe changes in sea ice cover and concentration. Comiso et al. showed that there may also be a direct relationship between the thickness of snow cover (h(sub s)) on ice and microwave emissivity at 90 GHz. Because the in situ experiment of experiment of Comiso et al. was limited to a single station, the relationship is re-examined in this paper in a more general context and using more extensive in situ microwave observations and measurements of h from the Weddell Sea 1986 and 1989 winter cruises. Good relationships were found to exist between h(sub s) sand the emissivity at 90 GHz - 10 GHz and the emissivity at 90 GHz - 18.7 GHz when the standard deviation of h(sub s) was less than 50% of the mean and when h(sub s) was less than 0.25 m. The reliance of these relationships on h(sub s) is most likely caused by the limited penetration through the snow of radiation at 90 GHz. When the algorithm was applied to the Special Sensor Microwave/Imager (SSM/I) satellite data from the Weddell Sea, the resulting mean h(sub s) agreed within 5% of the mean calculated from greater than 1400 in situ observations.

Arrigo, K. R.↗
NASA NTRS2012

A Briefing on Metrics and Risks for Autonomous Decision-Making in Aerospace Applications

Significant technology advances will enable future aerospace systems to safely and reliably make decisions autonomously, or without human interaction. The decision-making may result in actions that enable an aircraft or spacecraft in an off-nominal state or with slightly degraded components to achieve mission performance and safety goals while reducing or avoiding damage to the aircraft or spacecraft. Some key technology enablers for autonomous decision-making include: a continuous state awareness through the maturation of the prognostics health management field, novel sensor development, and the considerable gains made in computation power and data processing bandwidth versus system size. Sophisticated algorithms and physics based models coupled with these technological advances allow reliable assessment of a system, subsystem, or components. Decisions that balance mission objectives and constraints with remaining useful life predictions can be made autonomously to maintain safety requirements, optimal performance, and ensure mission objectives. This autonomous approach to decision-making will come with new risks and benefits, some of which will be examined in this paper. To start, an account of previous work to categorize or quantify autonomy in aerospace systems will be presented. In addition, a survey of perceived risks in autonomous decision-making in the context of piloted aircraft and remotely piloted or completely autonomous unmanned autonomous systems (UAS) will be presented based on interviews that were conducted with individuals from industry, academia, and government.

Frost, Susan↗
NASA NTRS1993

Studies and simulations of the DigiCipher system

During this period the development of simulators for the various high definition television (HDTV) systems proposed to the FCC was continued. The FCC has indicated that it wants the various proposers to collaborate on a single system. Based on all available information this system will look very much like the advanced digital television (ADTV) system with major contributions only from the DigiCipher system. The results of our simulations of the DigiCipher system are described. This simulator was tested using test sequences from the MPEG committee. The results are extrapolated to HDTV video sequences. Once again, some caveats are in order. The sequences used for testing the simulator and generating the results are those used for testing the MPEG algorithm. The sequences are of much lower resolution than the HDTV sequences would be, and therefore the extrapolations are not totally accurate. One would expect to get significantly higher compression in terms of bits per pixel with sequences that are of higher resolution. However, the simulator itself is a valid one, and should HDTV sequences become available, they could be used directly with the simulator. A brief overview of the DigiCipher system is given. Some coding results obtained using the simulator are looked at. These results are compared to those obtained using the ADTV system. These results are evaluated in the context of the CCSDS specifications and make some suggestions as to how the DigiCipher system could be implemented in the NASA network. Simulations such as the ones reported can be biased depending on the particular source sequence used. In order to get more complete information about the system one needs to obtain a reasonable set of models which mirror the various kinds of sources encountered during video coding. A set of models which can be used to effectively model the various possible scenarios is provided. As this is somewhat tangential to the other work reported, the results are included as an appendix.

Sayood, K.↗
NASA NTRS1994

Goal Directed Model Inversion: Learning Within Domain Constraints

Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome "would have been right if the outcome had been the desired one." The algorithm makes use of these intermediate "successes" to achieve the final goal. A unique and potentially very important feature of this algorithm is the ability to modify the output of the learning module to force upon it a desired syntactic structure. This differs from ordinary supervised learning in the following way: in supervised learning the exact desired output pattern must be provided. In GDMI instead, it is possible to require simply that the output obey certain rules, i.e., that it "make sense" in some way determined by the knowledge domain. The exact pattern that will achieve the desired outcome is then found by the system. The ability to impose rules while allowing the system to search for its own answers in the context of neural networks is potentially a major breakthrough in two ways: 1) it may allow the construction of networks that can incorporate immediately some important knowledge, i.e. would not need to learn everything from scratch as normally required at present, and 2) learning and searching would be limited to the areas where it is necessary, thus facilitating and speeding up the process. These points are illustrated with examples from robotic path planning and parametric design.

Colombano, Silvano P.↗
NASA NTRS1994

Goal Directed Model Inversion: Learning Within Domain Constraints

Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome "would have been right if the outcome had been the desired one." The algorithm makes use of these intermediate "successes" to achieve the final goal. A unique and potentially very important feature of this algorithm is the ability to modify the output of the learning module to force upon it a desired syntactic structure. This differs from ordinary supervised learning in the following way: in supervised learning the exact desired output pattern must be provided. In GDMI instead, it is possible to require simply that the output obey certain rules, i.e., that it "make sense" in some way determined by the knowledge domain. The exact pattern that will achieve the desired outcome is then found by the system. The ability to impose rules while allowing the system to search for its own answers in the context of neural networks is potentially a major breakthrough in two ways: (1) it may allow the construction of networks that can incorporate immediately some important knowledge, i.e., would not need to learn everything from scratch as normally required at present; and (2) learning and searching would be limited to the areas where it is necessary, thus facilitating and speeding up the process. These points are illustrated with examples from robotic path planning and parametric design.

Colombano, Silvano P.↗
NASA NTRS2003

Fractals and Spatial Methods for Mining Remote Sensing Imagery

The rapid increase in digital remote sensing and GIS data raises a critical problem -- how can such an enormous amount of data be handled and analyzed so that useful information can be derived quickly? Efficient handling and analysis of large spatial data sets is central to environmental research, particularly in global change studies that employ time series. Advances in large-scale environmental monitoring and modeling require not only high-quality data, but also reliable tools to analyze the various types of data. A major difficulty facing geographers and environmental scientists in environmental assessment and monitoring is that spatial analytical tools are not easily accessible. Although many spatial techniques have been described recently in the literature, they are typically presented in an analytical form and are difficult to transform to a numerical algorithm. Moreover, these spatial techniques are not necessarily designed for remote sensing and GIS applications, and research must be conducted to examine their applicability and effectiveness in different types of environmental applications. This poses a chicken-and-egg problem: on one hand we need more research to examine the usability of the newer techniques and tools, yet on the other hand, this type of research is difficult to conduct if the tools to be explored are not accessible. Another problem that is fundamental to environmental research are issues related to spatial scale. The scale issue is especially acute in the context of global change studies because of the need to integrate remote-sensing and other spatial data that are collected at different scales and resolutions. Extrapolation of results across broad spatial scales remains the most difficult problem in global environmental research. There is a need for basic characterization of the effects of scale on image data, and the techniques used to measure these effects must be developed and implemented to allow for a multiple scale assessment of the data before any useful process-oriented modeling involving scale-dependent data can be conducted. Through the support of research grants from NASA, we have developed a software module called ICAMS (Image Characterization And Modeling System) to address the need to develop innovative spatial techniques and make them available to the broader scientific communities. ICAMS provides new spatial techniques, such as fractal analysis, geostatistical functions, and multiscale analysis that are not easily available in commercial GIS/image processing software. By bundling newer spatial methods in a user-friendly software module, researchers can begin to test and experiment with the new spatial analysis methods and they can gauge scale effects using a variety of remote sensing imagery. In the following, we describe briefly the development of ICAMS and present application examples.

Lam, Nina↗
NASA NTRS1992

Optimistic barrier synchronization

Barrier synchronization is fundamental operation in parallel computation. In many contexts, at the point a processor enters a barrier it knows that it has already processed all the work required of it prior to synchronization. The alternative case, when a processor cannot enter a barrier with the assurance that it has already performed all the necessary pre-synchronization computation, is treated. The problem arises when the number of pre-sychronization messages to be received by a processor is unkown, for example, in a parallel discrete simulation or any other computation that is largely driven by an unpredictable exchange of messages. We describe an optimistic O(log sup 2 P) barrier algorithm for such problems, study its performance on a large-scale parallel system, and consider extensions to general associative reductions as well as associative parallel prefix computations.

Nicol, David M.↗
NASA NTRS2023

Using Satellite-Based Terrestrial Water Storage Data: A Review

Land water storage plays a key role for the Earth’s climate, natural ecosystems, and human activities. Since the launch of the first Gravity Recovery and Climate Experiment (GRACE) mission in 2002, spaceborne observations of changes in terrestrial water storage (TWS) have provided a unique, global perspective on natural and human-induced changes in freshwater resources. Even though they have become much used within the broader Earth system science community, space-based TWS datasets still incorporate important and case-specific limitations which may not always be clear to users not familiar with the underlying processing algorithms. Here, we provide an accessible and illustrated overview of the measurement concept, of the main available data products, and of some frequently encountered technical terms and concepts. We summarize concrete recommendations on how to use TWS data in combination with other hydrological or climatological datasets, and guidance on how to avoid possible pitfalls. Finally, we provide an overview of some of the main applications of GRACE TWS data in the fields of hydrology and climate science. This review is written with the intention of supporting future research and facilitating the use of satellite-based terrestrial water storage datasets in interdisciplinary contexts.

Terrestrial water storage↗
NASA NTRS1998

Detection of Non-Symmetrical Damage in Smart Plate-Like Structures

A two-dimensional model for in-plane vibrations of a cantilever plate with a non-symmetrical damage is used in the context of defect identification in materials with piezoelectric ceramic patches bonded to their surface. These patches can act both as actuators and sensors in a self-analyzing fashion, which is a characteristic of smart materials. A Galerkin method is used to approximate the dynamic response of these structures. The natural frequency shifts due to the damage are estimated numerically and compared to experimental data obtained from tests on cantilever aluminum plate-like structures damaged at different locations with defects of different depths. The damage location and extent are determined by an enhanced least square identification method. Efficacy of the frequency shift based algorithms is demonstrated using experimental data.

Blanks, H. T.↗
NASA NTRS2014

Jet Noise Shielding Provided by a Hybrid Wing Body Aircraft

One approach toward achieving NASA's aggressive N+2 noise goal of 42 EPNdB cumulative margin below Stage 4 is through the use of novel vehicle configurations like the Hybrid Wing Body (HWB). Jet noise measurements from an HWB acoustic test in NASA Langley's 14- by 22-Foot Subsonic Tunnel are described. Two dual-stream, heated Compact Jet Engine Simulator (CJES) units are mounted underneath the inverted HWB model on a traversable support to permit measurement of varying levels of shielding provided by the fuselage. Both an axisymmetric and low noise chevron nozzle set are investigated in the context of shielding. The unshielded chevron nozzle set shows 1 to 2 dB of source noise reduction (relative to the unshielded axisymmetric nozzle set) with some penalties at higher frequencies. Shielding of the axisymmetric nozzles shows up to 6.5 dB of reduction at high frequency. The combination of shielding and low noise chevrons shows benefits beyond the expected additive benefits of the two, up to 10 dB, due to the effective migration of the jet source peak noise location upstream for increased shielding effectiveness. Jet noise source maps from phased array results processed with the Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) algorithm reinforce these observations.

Doty, Michael J.↗
NASA NTRS2020

Intelligent Contingency Management for Urban Air Mobility

The third aviation revolution is seeking to enable transportation where users have access to immediate and flexible air travel; the users dictate trip origin, destination and timing. One of the major components of this vision is urban air mobility (UAM) for the masses. UAM means a safe and efficient system for vehicles to move passengers and cargo within a city. In order to reach UAM’s full market potential the vehicle will have to be autonomous. One of the primary challenges of autonomous flight is dealing with off-nominal events, both common and unforeseen; thus, intelligent contingency management (ICM) is one of the enabling technologies. In this context, the vehicle has to be aware of its internal state and external environment at all times, ascertain its capability and make decisions about mission completion or modification. All of these functions require data to model and assess the environment and then take actions based on these models. Necessarily, there is uncertainty associated with the data and the models generated from it. Since we are dealing with safety-critical systems, one of the main challenges of ICM is to generate sufficient data and to minimize its uncertainty to enable practical and safe decision making. We propose an overall architecture that incorporates deterministic and learning algorithms together to assess vehicle capabilities, project these into the future and make decisions on mission management level. A layered approach allows for mature parts and technologies to be integrated into early highly automated vehicles before the final state of autonomy is reached.

data-driven systems↗
NASA NTRS2008

Rotor Airloads Prediction Using Unstructured Meshes and Loose CFD/CSD Coupling

The FUN3D unsteady Reynolds-averaged Navier-Stokes solver for unstructured grids has been modified to allow prediction of trimmed rotorcraft airloads. The trim of the rotorcraft and the aeroelastic deformation of the rotor blades are accounted for via loose coupling with the CAMRAD II rotorcraft computational structural dynamics code. The set of codes is used to analyze the HART-II Baseline, Minimum Noise and Minimum Vibration test conditions. The loose coupling approach is found to be stable and convergent for the cases considered. Comparison of the resulting airloads and structural deformations with experimentally measured data is presented. The effect of grid resolution and temporal accuracy is examined. Rotorcraft airloads prediction presents a very substantial challenge for Computational Fluid Dynamics (CFD). Not only must the unsteady nature of the flow be accurately modeled, but since most rotorcraft blades are not structurally stiff, an accurate simulation must account for the blade structural dynamics. In addition, trim of the rotorcraft to desired thrust and moment targets depends on both aerodynamic loads and structural deformation, and vice versa. Further, interaction of the fuselage with the rotor flow field can be important, so that relative motion between the blades and the fuselage must be accommodated. Thus a complete simulation requires coupled aerodynamics, structures and trim, with the ability to model geometrically complex configurations. NASA has recently initiated a Subsonic Rotary Wing (SRW) Project under the overall Fundamental Aeronautics Program. Within the context of SRW are efforts aimed at furthering the state of the art of high-fidelity rotorcraft flow simulations, using both structured and unstructured meshes. Structured-mesh solvers have an advantage in computation speed, but even though remarkably complex configurations may be accommodated using the overset grid approach, generation of complex structured-mesh systems can require months to set up. As a result, many rotorcraft simulations using structured-grid CFD neglect the fuselage. On the other hand, unstructured-mesh solvers are easily able to handle complex geometries, but suffer from slower execution speed. However, advances in both computer hardware and CFD algorithms have made previously state-of-the-art computations routine for unstructured-mesh solvers, so that rotorcraft simulations using unstructured grids are now viable. The aim of the present work is to develop a first principles rotorcraft simulation tool based on an unstructured CFD solver.

Biedron, Robert T.↗
NASA NTRS2020

Efficient Active Learning for New Domains

The promise of active learning is to reduce the number of labeled examples required by supervised machine learning algorithms. The largest potential benefits lie in entirely new domains, for which no labeled examples yet exist. Yet to date, most active learning studies are retroactive and demonstrate the benefits that could have been gained if active learning had been used. What are the barriers to true adoption and utilization of active learning? We focus on two: (1) the cold start or class discovery problem, in which active learning methods may struggle to make progress with zero labeled examples, and (2) the cost of having the classifier in the loop to select the next example to be labeled. We assess different active learning approaches in the context of these two barriers and conclude with recommendations for how to employ active learning in new domains. As an example, we report on the use of active learning on a large, novel data set of Mars surface images.

Lu, Steven↗