Identification of human operator models by stochastic approximation
Human operator models parameter estimation by stochastic approximation, considering continuous and sampled data models
Bekey, G. A.↗
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Developing processing techniques for Skylab data
The author has identified the following significant results. The effects of misregistration and the scan-line-straightening algorithm on multispectral data were found to be: (1) there is greatly increased misregistration in scan-line-straightening data over conic data; (2) scanner caused misregistration between any pairs of channels may not be corrected for in scan-line-straightened data; and (3) this data will have few pure field center pixels than will conic data. A program SIMSIG was developed implementing the signature simulation model. Data processing stages of the experiment were carried out, and an analysis was made of the effects of spatial misregistration on field center classification accuracy. Fifteen signatures originally used for classifying the data were analyzed, showing the following breakdown: corn (4 signatures), trees (2), brush (1), grasses, weeds, etc. (5), bare soil (1), soybeans (1), and alfalfa (1).
Nalepka, R. F.↗
Atmospheric definition for Shuttle aerothermodynamic investigations
A procedure has been developed to estimate the freestream atmospheric properties along the Shuttle trajectory during atmospheric re-entry. The procedure utilizes measurement data obtained by NASA, NOAA, and the Air Force, and model data for regions where measured data are nonexistent. The results obtained are used to determine the best atmosphere-relative trajectory parameters during re-entry and to allow for an evaluation of the aerodynamic and aerothermodynamic characteristics of the Shuttle. A discussion of the method employed by the Langley Atmospheric Information Retrieval System, which is a computer code for the determination of atmospheric parameters, is presented. Results obtained from the first two Shuttle flights, STS-1 and STS-2, are also given with applications of these atmospheric parameters.
Price, J. M.↗
Advanced techniques for the storage and use of very large, heterogeneous spatial databases. The representation of geographic knowledge: Toward a universal framework
A new approach to building geographic data models that is based on the fundamental characteristics of the data is presented. An overall theoretical framework for representing geographic data is proposed. An example of utilizing this framework in a Geographic Information System (GIS) context by combining artificial intelligence techniques with recent developments in spatial data processing techniques is given. Elements of data representation discussed include hierarchical structure, separation of locational and conceptual views, and the ability to store knowledge at variable levels of completeness and precision.
Peuquet, Donna J.↗
NSSDC data listing
In a highly summarized way, data available from the National Space Science Data Center (NSSDC) is identified. Most data are offline data sets (on magnetic tape or as film/print products of various sizes) from individual instruments carried on spacecraft; these compose the Satellite Data Listing. Descriptive names, time spans, data form, and quantity of these data sets are identified in the listing, which is sorted alphabetically-first by spacecraft name and then by the principal investigator's or team leader's last name. Several data sets held at NSSDC, not associated with individual spaceflight instruments, are identified in separate listings following the Satellite Data Listing. These data sets make up the Supplementary Data Listings and include composite spacecraft data sets, ground-based data, models, and computer routines. The identifiers used in the Supplementary Data Listings were created by NSSDC and are explained in the pages preceding the listings. Data set form codes are listed. NSSDC offers primarily archival, retrieval, replication, and dissemination services associated with the data sets discussed in the two major listings identified above. NSSDC also provides documentation which enables the data recipient to use the data received. NSSDC is working toward expanding presently limited capabilities for data subsetting and for promotion of data files to online residence for user downloading. NSSDC data holdings span the range of scientific disciplines in which NASA is involved, and include astrophysics, lunar and planetary science, solar physics, space plasma physics, and Earth science. In addition to the functions mentioned above, NSSDC offers data via special services and systems in a number of areas, including Astronomical Data Center (ADC), Coordinated Data Analysis Workshops (CDAWs), NASA Climate Data System (NCDS), Pilot Land Data System (PLDS), and Crustal Dynamics Data Information System (CDDIS). Furthermore, NSSDC has a no-password account on its SPAN/Telenet-accessible VAX through which the NASA Master Directory and selected online data bases are accessible and through which any data described here may be ordered. Astrophysics data support by NSSDC is not limited to the ADC. Each of these special services/systems is described briefly.
Horowitz, Richard↗
Ground testing for the no-vent fill of cryogenic tanks - Results of tests for a 71 cubic foot tank
NASA Lewis Research has been investigating the no-vent till method, since it is a promising approach to transfer liquid while handling the problems of low-g venting. This paper reports the results of a test series for filling a 71 cu ft tank with liquid hydrogen without venting. Twenty two tests were conducted, ten with a bottom orifice as the inlet and 12 with a spray bar. Parameters investigated included inlet saturation pressures of approximately 5, 15, and 25 psia; transfer pressures of 20, 30, and 45 psia; and various starting wall temperatures. Of the tests, only the one run at the highest wall temperature (238 R) failed to fill the tank. Test results are compared to a thermodynamic equilibrium model. Overall model-data agreement was good except for the tendency of the model to overshoot during the initial wall cool-down of the higher starting wall temperature fills.
Chato, David J.↗
Ground testing for the no-vent fill of cryogenic tanks: Results of tests for a 71 cubic foot tank
NASA Lewis Research has been investigating the no-vent fill method, since it is a promising approach to transfer liquid while handling the problems of low-g venting. This paper reports the results of a test series for filling a 71 cu ft tank with liquid hydrogen without venting. 22 tests were conducted, 10 with a bottom orifice as the inlet and 12 with a spray bar. Parameters investigated included inlet saturation pressures of approximately 5, 15, and 25 psia, transfer pressures of 20, 30, and 45 psia, and various starting wall temperatures. Of the tests, only the one run at the highest wall temperature (238 R) failed to fill the tank. Test results are compared to a thermodynamic equilibrium model. Overall model-data agreement was good except for the tendency of the model to overshoot during the initial wall cool down of the higher starting wall temperature fills.
Chato, David J.↗
Near-simultaneous ROSAT and Ginga observations of the 1991 X-ray transient in Musca
The quasi simultaneous Rosat/Ginga observations of the Musca X-ray transient are reported. During its all sky survey, Rosat observed the Musca 1991 X-ray transient on 24-25 Jan. 1991, two weeks after outbursts, for about 170 seconds. The intensity was found to be nearly 6 Crab in the Rosat band. A combined fit of Rosat and Ginga data from 25 Jan. 1991 with a multitemperature disk blackbody model data plus a power law component results in a maximal temperature of the disk of about kT = 0.96 keV at an absorbing column of N(sub H) = 2.2 x 10(exp 21)/sq cm. A minimum distance to the black hole binary of at least 4 to 5 kpc was derived. Including the accretion disk inclination angle of i = 26 +/- 25 degrees determined from the shape of the positron annihilation line, and the quiescent optical brightness together with the most probable spectral type of the companion, a black hole mass of M = (6 +/- 1.5) of the solar mass and a distance of about (11 +/- 3) kpc, were derived. Additional Rosat observations in Aug. 1991 and Mar. 1992 suggest that the exponential intensity decay until 240 days after the outburst is followed by a steeper decline between 240 and 410.
Greiner, J.↗
Time Manager Software for a Flight Processor
Data analysis is a process of inspecting, cleaning, transforming, and modeling data to highlight useful information and suggest conclusions. Accurate timestamps and a timeline of vehicle events are needed to analyze flight data. By moving the timekeeping to the flight processor, there is no longer a need for a redundant time source. If each flight processor is initially synchronized to GPS, they can freewheel and maintain a fairly accurate time throughout the flight with no additional GPS time messages received. How ever, additional GPS time messages will ensure an even greater accuracy. When a timestamp is required, a gettime function is called that immediately reads the time-base register.
Zoerne, Roger↗
NASA's ATM Ontology: Semantic Integration and Querying Across NAS Data Sources
NASA is developing an Air Traffic Management (ATM) Ontology as part of an advanced prototyping activity that demonstrates the utility of semantic technologies for integrating, querying, and searching over various sources of heterogeneous ATM data. The ontology encodes an overarching data model that functions as the backbone upon which to overlay data from multiple sources published by FAA, NOAA, NASA, and others. The integrated data can be queried to produce results not achievable using any single source alone. The ontology incorporates flight data, weather data, traffic management advisory data, airport delay data, and national airspace infrastructure data for a very limited spatial and temporal slice of airspace operations (one day of operations at a major airport).
air traffic management↗
Post-flight Analysis of Atmospheric Properties from Mars 2020 Entry, Descent, and Landing
The Mars 2020 spacecraft landed the Perseverance rover and Ingenuity helicopter successfully in Jezero crater on Feb. 18, 2021. The entry, descent, and landing (EDL) sequence of the spacecraft largely leveraged the previous 2012 Mars Science Laboratory (MSL) mission. The atmospheric modeling approach for Mars 2020 was also borrowed from MSL. It consisted of two mesoscale atmospheric models of the target site during the Martian season of landing, and a statistical model of the pressure, density, temperature, and winds based on the mesoscale model data. This paper briefly describes the pre-flight atmospheric models used for Mars 2020, but focuses on the post-flight assessment of these models and comparison to near-landing day orbiter sounder and other onboard atmospheric measurements. Observations from post-flight analysis showed that density was under-predicted in the upper atmosphere, but within the altitudes covered by the mesoscale models, the pre-flight modeling matched post-flight results, including for quantities like wind velocities. Potential improvements to address the upper atmosphere and other deficiencies of the Mars 2020 pre-flight model are also discussed.
Villar, Gregorio↗
Data Mining for Science of the Sun-Earth Connection as a Single System
Establishing the Sun-Earth connection requires overcoming the challenges of exploring the data from past and current missions and leveraging tools and models (data mining) to create an efficient system treatment of the Sun and heliosphere. However, solar and heliospheric environment data constitute a vast source of information whose potential is far from being optimally exploited. In the next decade, the solar and heliospheric community will have to manage the increasing amount of information coming from new missions, improve reanalysis of data from past and current missions, and create new data products from the application of new methodologies. This complex task is further complicated by practical challenges such as different datasets and catalogs in different formats that may require different pre-processing and analysis tools, and the need for numerous analysis approaches that are not all fully optimized for large volumes of data. While several ongoing efforts aim at addressing these problems, the available datasets and tools are not always used to their full potential often due to lack of awareness of available resources. In this paper, we summarize the issues raised and goals discussed by members of the community during recent conference sessions focused on data mining for science.
Sun-Earth connection↗
Data-Driven Mathematical Modeling of the Effect of Particle Size Distribution on the Transitory Reaction Kinetics of Hot Metal Desulfurization
The aim of this work was to develop a prediction model for hot metal desulfurization. More specifically, the study aimed at finding a set of explanatory variables that are mandatory in prediction of the kinetics of the lime-based transitory desulfurization reaction and evolution of the sulfur content in the hot metal. The prediction models were built through multivariable analysis of process data and phenomena-based simulations. The model parameters for the suggested model types are identified by solving multivariable least-squares cost functions with suitable solution strategies. One conclusion we arrived at was that in order to accurately predict the rate of desulfurization, it is necessary to know the particle size distribution of the desulfurization reagent. It was also observed that a genetic algorithm can be successfully applied in numerical parameter identification of the proposed model type. It was found that even a very simplistic parameterized expression for the first-order rate constant provides more accurate prediction for the end content of sulfur compared to more complex models, if the data set applied for the modeling contains the adequate information.
36 MATERIALS SCIENCE↗
Estimating a Service-Life Distribution Based on Production Counts and a Failure Database
A manufacturer wanted to compare the service-life distributions of two similar products. These concern product lifetimes after installation (not manufacture). For each product, there were available production counts and an imperfect database providing information on failing units. In the real case, these units were expensive repairable units warrantied against repairs. Failure (of interest here) was relatively rare and driven by a different mode/mechanism than ordinary repair events (not of interest here). Approach: Data models for the service life based on a standard parametric lifetime distribution and a related limited failure population were developed. These models were used to develop expressions for the likelihood of the available data that properly accounts for information missing in the failure database. Results: A Bayesian approach was employed to obtain estimates of model parameters (with associated uncertainty) in order to investigate characteristics of the service-life distribution. Custom software was developed and is included as Supplemental Material to this case study. One part of a responsible approach to the original case was a simulation experiment used to validate the correctness of the software and the behavior of the statistical methodology before using its results in the application, and an example of such an experiment is included here. Because of confidentiality issues that prevent use of the original data, simulated data with characteristics like the manufacturer’s proprietary data are used to illustrate some aspects of our real analyses. Lastly, we also note that, although this case focuses on rare and complete product failure, the statistical methodology provided is directly applicable to more standard warranty data problems involving typically much larger warranty databases where entries are warranty claims (often for repairs) rather than reports of complete failures.
97 MATHEMATICS AND COMPUTING↗
Data-driven wind turbine wake modeling via probabilistic machine learning
Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions and the interaction between wakes. Physics-based models that capture the wake flow field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced-order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional latent space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.
Deep neural networks↗
Integration of Condition-Based, Diagnostic, Prognostic, And Anomaly Detection Data into Reliability Models to Support a Predictive Maintenance Context
Reliability data employed in plant reliability models are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating actual health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). This paper proposes a reliability modeling approach that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. We show how state-of-the art condition-based, diagnostic, prognostic, and anomaly detection models can be linked to system reliability models not in probability terms, but in terms of margin where margin is defined as the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Then, we show how the propagation of margin data from the asset to the system level is performed through classical reliability models such as fault trees or reliability block diagrams. The described method is in fact able to propagate heterogenous health data from the asset to the system level in order to analytically assess system health.
97 MATHEMATICS AND COMPUTING↗
Observing System Simulations for the AOS Mission
The Earth System Observatory (ESO) is NASA’s response to the recommendations of the 2017 Earth Sciences Decadal Survey conducted by the US National Academy of Sciences, Engineering and Medicine. The ESO is being conceived as a set of fully integrated missions addressing 4 main Earth science focus areas including aerosols, clouds, convection and precipitation (jointly re-ferred to as AOS, the Atmosphere Observing System). ESO ground breaking observations will provide critical measurements to address societally relevant problems in climate change, natural hazard mitiga-tion, fighting forest fires, and improving real-time agricultural processes. A critical element of the AOS observing strategy is to make extensive use of new passive and active sen-sors 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 compre-hensive 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, at-mospheric convection, emphasizing aerosol processes and aerosol radiative effects, and the synergistic aspects of clouds-precipitation-aerosol interactions. We will describe examples of the observing system simulation capabilities being developed for AOS, including global storm resolving nature runs, detailed instrument and retrieval simulators, as well as fast retrieval emulators for instrumenting climate models. This simulation environment, being developed under NASA’s open-source science initiative, will permit us to explore 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, well before launch.
Arlindo da Silva↗
High speed turboprop aeroacoustic study (counterrotation). Volume 2: Computer programs
The isolated counterrotating high speed turboprop noise prediction program developed and funded by GE Aircraft Engines was compared with model data taken in the GE Aircraft Engines Cell 41 anechoic facility, the Boeing Transonic Wind Tunnel, and in the NASA-Lewis 8 x 6 and 9 x 15 wind tunnels. The predictions show good agreement with measured data under both low and high speed simulated flight conditions. The installation effect model developed for single rotation, high speed turboprops was extended to include counter rotation. The additional effect of mounting a pylon upstream of the forward rotor was included in the flow field modeling. A nontraditional mechanism concerning the acoustic radiation from a propeller at angle of attack was investigated. Predictions made using this approach show results that are in much closer agreement with measurement over a range of operating conditions than those obtained via traditional fluctuating force methods. The isolated rotors and installation effects models were combined into a single prediction program. The results were compared with data taken during the flight test of the B727/UDF (trademark) engine demonstrator aircraft.
Whitfield, C. E.↗