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NASA NTRS1994

The PSR 1800-21/G8.7-0.1 association: A view from ROSAT

We present the first imaging X-ray observation of the W30 complex which contains the Galactic SNR G8.7-0.1 and the young radio pulsar PSR 1800-21 as well as a number of discrete ultracompact H II regions. The pulsar PSR 1800-21 is detected as a count rate of 1.5 +/- 0.5/ks and diffuse emission from the northern extent of G8.7-0.1 observed. The data support an association of G8.7-0.1 and PSR 1800-21, and the peculiar morphology can be explained if the supernova which gave birth to PSR 1800-21 occurred within, or very near, a molecular cloud. The suggested scenario removes the requirement of a large transverse velocity for the pulsar, one of the greatest dificulties with the association.

Finley, John P.↗

NASA NTRS1993

MAX-91: Polarimetric SAR results on Montespertoli site

The polarimetric Synthetic Aperture Radar (SAR) is a powerful sensor for high resolution ocean and land mapping and particularly for monitoring hydrological parameters in large watersheds. There is currently much research in progress to assess the SAR operational capability as well as to estimate the accuracy achievable in the measurements of geophysical parameters with the presently available airborne and spaceborne sensors. An important goal of this research is to improve our understanding of the basic mechanisms that control the interaction of electro-magnetic waves with soil and vegetation. This can be done both by developing electromagnetic models and by analyzing statistical relations between backscattering and ground truth data. A systematic investigation, which aims at a better understanding of the information obtainable from the multi-frequency polarimetric SAR to be used in agro-hydrology, is in progress by our groups within the framework of SIR-C/X-SAR Project and has achieved a most significant milestone with the NASA/JPL Aircraft Campaign named MAC-91. Indeed this experiment allowed us to collect a large and meaningful data set including multi-temporal multi-frequency polarimetric SAR measurements and ground truth. This paper presents some significant results obtained over an agricultural flat area within the Montespertoli site, where intensive ground measurements were carried out. The results are critically discussed with special regard to the information associated with polarimetric data.

Baronti, S.↗

DOE OSTI2023

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Neural networks (NNs) are currently changing the computational paradigm on how to combine data with mathematical laws in physics and engineering in a profound way, tackling challenging inverse and ill-posed problems not solvable with traditional methods. However, quantifying errors and uncertainties in NN-based inference is more complicated than in traditional methods. This is because in addition to aleatoric uncertainty associated with noisy data, there is also uncertainty due to limited data, but also due to NN hyperparameters, overparametrization, optimization and sampling errors as well as model misspecification. Although there are some recent works on uncertainty quantification (UQ) in NNs, there is no systematic investigation of suitable methods towards quantifying the total uncertainty effectively and efficiently even for function approximation, and there is even less work on solving partial differential equations and learning operator mappings between infinite-dimensional function spaces using NNs. In this work, we present a comprehensive framework that includes uncertainty modeling, new and existing solution methods, as well as evaluation metrics and post-hoc improvement approaches. Further, to demonstrate the applicability and reliability of our framework, we present an extensive comparative study in which various methods are tested on prototype problems, including problems with mixed input-output data, and stochastic problems in high dimensions. In the Appendix, we include a comprehensive description of all the UQ methods employed. Further, to help facilitate the deployment of UQ in Scientific Machine Learning research and practice, we present and develop in [1] an open-source Python library (github.com/Crunch-UQ4MI/neuraluq), termed NeuralUQ, that is accompanied by an educational tutorial and additional computational experiments.

11 physics-informed neural networks↗

NASA NTRS1992

Lidar Studies of Extinction in Clouds in the ECLIPS Project

The Experimental Cloud Lidar Pilot Study (ECLIPS) project has now had two active phases in 1989 and 1991. A number of laboratories around the world have taken part in the study. The observations have yielded new data on cloud height and structure, and have yielded some useful new information on the retrieval of cloud optical properties, together with the uncertainties involved. Clouds have a major impact on the climate of the earth. They have the effect of reducing the mean surface temperature from 30 C for a cloudless planet to a value of about 15 C for present cloud conditions. However, it is not at all certain how clouds would react to a change in the planetary temperature in the event of climate change due to a radiative forcing from greenhouse gases. Clouds both reflect out sunlight (negative feedback) and enhance the greenhouse effect (positive feedback), but the ultimate sign of cloud feedback is unknown. Because of these uncertainties, campaigns to study clouds intensely were initiated. The International Satellite Cloud Climatology (ISCPP) and the FIRE Campaigns (cirrus and stratocumulus) are examples. The ECLIPS was set up similarly to the above experiments to obtain information specifically on cloud base, but also cloud top (where possible), optical properties, and cloud structure. ECLIPS was designed to allow as many laboratories as possible globally to take part to get the largest range of clouds. It involves observations with elastic backscatter lidar, supported by infrared fluxes at the ground and radiosonde data, as basic instrumentation. More complex experiments using beam filter radiometers, solar pyranometers, and satellite data and often associated with other campaigns were also encouraged to join ECLIPS. Two periods for observation were chosen, Sep. - Dec. 1989 and Apr. - Jul. 1992 into which investigators were requested to fit 30 days of observations. These would be either continuous, or arranged to coincide with NOAA satellite overpasses to obtain AVHRR data. The distribution of the ECLIPS international effort as in 1991 is shown. The main gaps in the global distribution are in the tropics and the Southern Hemisphere.

Martin, C.↗

NASA NTRS1961

Application of Monte Carlo Technique for Determining Maneuvering Loads from Statistical Information on Airplane Motions

Results of a statistical analysis of horizontal-tail loads on a fighter airplane are presented. The data were obtained from a number of operational training missions with flight at altitudes up to about 50,000 feet and at Mach numbers up to 1.22. The analysis was performed to determine the feasibility of calculating horizontal-tail load from data on the flight conditions and airplane motions. In the analysis the calculated loads are compared with the measured loads for the different types of missions performed. The loads were calculated by two methods: a direct approach and a Monte Carlo technique. The procedures used and some of the problems associated with the data analysis are discussed. frequencies of occurrence of tail loads of given magnitudes are derived from statistical information on the flight quantities. In the direct method, a time history of tail load is calculated from time-history measurements of the flight quantities. The Monte Carlo method could be useful for extending loads information for design of prospective airplanes . For the Monte Carlo method, the The results indicate that the accuracy of loads, regardless of the method used for calculation, is largely dependent on the knowledge of the pertinent airplane aerodynamic characteristics and center-of-gravity location. In addition, reliable Monte Carlo results require an adequate sample of statistical data and a knowledge of the more important statistical dependencies between the various flight conditions and airplane motions.

Hamer, Harold A.↗

NASA NTRS2015

Description, Usage, and Validation of the MVL-15 Modified Vortex Lattice Analysis Capability

MVL-15 is the most recent version of the Modified Vortex-Lattice (MVL) code developed within the Aerodynamics Systems Analysis Branch (ASAB) at NASA LaRC. The term "modified" refers to the primary modification of the core vortex-lattice methodology: inclusion of viscous aerodynamics tables that are linked to the linear solution via iterative processes. The inclusion of the viscous aerodynamics inherently converts the MVL-15 from a purely analytic linearized method to a semi-empirical blend which retains the rapid execution speed of the linearized method while empirically characterizing the section aerodynamics at all spanwise lattice points. The modification provides a means to assess non-linear effects on lift that occur at angles of attack near stall, and provides a means to determine the drag associated with the application of design strategies for lift augmentation such as the use of flaps or blowing. The MVL-15 code is applicable to the analyses of aircraft aerodynamics during cruise, but it is most advantageously applied to the analysis of aircraft operating in various high-lift configurations. The MVL methodology has been previously conceived and implemented; the initial concept version was delivered to the ASAB in 2001 (van Dam, C.), subsequently revised (Gelhausen, P. and Ozoroski, T. 2002 / AVID Inc., Gelhausen, P., and Roberts, M. 2004), and then overhauled (Ozoroski, T., Hahn, A. 2008). The latest version, MVL-15 has been refined to provide analysis transparency and enhanced to meet the analysis requirements of the Environmentally Responsible Aviation (ERA) Project. Each revision has been implemented with reasonable success. Separate applications of the methodology are in use, including a similar in-house capability, developed by Olson, E. that is tailored for structural and acoustics analyses. A central premise of the methodology is that viscous aerodynamic data can be associated with analytic inviscid aerodynamic results at each spanwise wing section, thereby providing a pathway to map viscous data to the inviscid results. However, a number of factors can sidetrack the analysis consistency during various stages of this process. For example, it should be expected that the final airplane lift curve and drag polar results depend strongly on the geometry and aerodynamics of the airfoil section; however, flap deflections and flap chord extensions change the local reference geometry of the input airfoil, the airplane wing, the tabulated non-dimensional viscous aerodynamics, and the spanwise links between the linear and the viscous aerodynamics. These changes also affect the bound circulation and therefore, calculation and integration of the induced angle of attack and induced drag. MVL-15 is configured to ensure these types of challenges are properly addressed. This report is a comprehensive manual describing the theory, use, and validation of the MVL-15 analysis tool. Section 3 summarizes theoretical, procedural, and characteristic features of MVL-15, and includes a list of the files required to setup, execute, and summarize an analysis. Section 4, Section 5, Section 6, and Section 7 combine to comprise the User's Guide portions of this report. The MVL-15 input and output files are described in Section 4 and Section 5, respectively; the descriptions are supplemented with example files and information about the file formats, parameter definitions, and typical parameter values. Section 6 describes the Wing Geometry Setup Utility and the 2d-Variants Utility files that simplify and assist setting up a consistent set of MVL-15 geometry and aerodynamics input parameters and input files. Section 7 describes the use of the 3d-Results Presentation Utility file that can be used to automatically create summary tables and charts from the MVL-15 output files. Section 8 documents the Validation Results of an extensive and varied validation test matrix, including results of an airplane analysis representative of the ERA Program. A start-to-finish example of the airplane analysis procedure is described in Section 7.

Ozoroski, Thomas A.↗

DOE OSTI2025

Scalable edge clustering of dynamic graphs via weighted line graphs

Timestamped relational datasets consisting of records (or connections) between pairs of entities are ubiquitous in network science. For applications like peer-to-peer communication, email, various social network interactions, and computer network security, it is useful to organize these records into groups based on how and when they are occurring. Weighted line graphs offer a natural way to model how records are related in such datasets but for large real-world graph topologies, building and utilizing the line graph is prohibitively expensive. Here, we present the framework to cluster the edges of a dynamic graph via the associated line graph that contains two major contributions. The first is a method to work with the line graph implicitly and the second is a distributed scale implementation of an agglomerative hierarchical graph clustering algorithm. We outline a novel hierarchical dynamic graph edge clustering approach that efficiently breaks massive relational datasets into small sets of edges containing events at various timescales. This is in stark contrast to traditional graph clustering algorithms that prioritize highly connected (clique-like) community structures. Our approach relies on constructing a sufficient subgraph of a weighted line graph and applying a hierarchical agglomerative clustering. This approach is related to scalable techniques from spatial clustering, nonlinear-dimension reduction, topological data analysis, and draws particular inspiration from HDBSCAN. As an edge clustering, this method yields an overlapping node clustering. Our algorithm is parallelizable and we demonstrate efficient clustering of a billion-scale, real-world dynamic graph into small edge sets that correlate in topology and time. The entire clustering process for a graph with tens of billions of edges takes just a few minutes of run time on 256 nodes of a distributed compute environment. We argue how the output of the edge clustering is useful for a multitude of data visualization and powerful machine learning tasks, both involving the original massive dynamic graph data and metadata associated with the nodes and edges. Finally, we describe how this approach can be extended to dynamic hypergraphs and dynamic graphs/hypergraphs with unstructured data living on vertices and edges.

Data Analysis↗

DOE OSTI2020

Sister Rod Destructive Examinations (FY20) Appendix B: Segmentation, Defueling, Metallograhic Data and Total Cladding Hydrogen

As a part of the US DOE-NE High Burnup Spent Fuel Data Project, ORNL is performing destructive examinations (DEs) of high burnup (>45 gigawatt days per metric ton uranium) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called the “sister rods,” are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4, low-tin Zircaloy-4, ZIRLO®, and M5®. The DEs are being conducted to obtain a baseline of the HBU rod’s condition prior to dry storage and are focused on understanding overall SNF rod strength and durability. Both composite fuel and empty cladding are being tested. While the data generated can be used for multiple purposes, a primary goal for obtaining the post-irradiation examination data and the associated measured mechanical properties is to support SNF dry storage licensing and relicensing activities.This report documents the status of the ORNL Phase 1 DE activities related to: rough segmentation, defueling, optical microscopy, and cladding total hydrogen measurements for 7 Phase 1 sister rods and outlines the DE tasks performed and the data collected to date. The results of these detailed examinations and others are summarized in the Sister Rod DE Status Report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRSDate not supplied

A Data Analysis and Simulation Study of Urban Air Mobility

For the Urban Air Mobility (UAM) industry, NASA has defined a series of UAM Maturity Levels (UML) corresponding to increasingly more complex and operationally dense UAM operations. In support of the gradual progression towards higher UML levels, NASA is currently conducting a set of UAM air traffic simulations—collectively referred to as X4. This paper describes a set of system effectiveness measures, and their associated metrics, for data analysis of X4 simulations. The descriptions, rationales, and calculation procedures for two metrics to be used in data analysis of simulation results, the number of predicted demand-capacity imbalances and the pre-departure delays, are described. Results from data analysis of one set of simulation runs are presented to demonstrate how these metrics support the assessment of performance of the system architecture for X4 simulations and the verification of experiment requirements.

Urban Air Mobility↗

NASA NTRSDate not supplied

A Data Analysis Approach for Simulations of Urban Air Mobility Operations

Urban Air Mobility↗

DOE OSTI2025

Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance

Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.

42 ENGINEERING↗

NASA NTRS1982

Association of spectral development patterns with development stages of corn

Association is made between the development stages of corn as defined by Hanway and the temporal-spectral development pattern of corn in a transformed data space derived from Landsat-MSS band reflectance values, using field-collected reflectance and associated data. Results indicate that the spectral vegetation index used (a reflectance equivalent of Tasseled Cap Greenness) reaches a maximum well before the stage at which corn is expected to achieve its peak leaf area index. Possible physiological and canopy geometry related causes for this and other results are presented.

Crist, E. P.↗

NASA NTRS2012

The Shape of Long Outbursts in U Gem Type Dwarf Novae from AAVSO Data

We search the American Association of Variable Star Observers (AAVSO) archives of the two best studied dwarf novae in an attempt to find light curves for long out bursts that are extremely well-characterized. The systems are U Gem and S8 Cyg. Our goal is to search for embedded precursors such as those that have been found recently in the high fidelity Kepler data for superoutbursts of some members of the 8U UMa subclass of dwarf novae. For the vast majority of AAV80 data, the combination of low data cadence and large errors associated with individual measurements precludes one from making any strong statement about the shape of the long outbursts. However, for a small number of outbursts, extensive long term monitoring with digital photometry yields high fidelity light curves. We report the finding of embedded precursors in two of three candidate long outbursts. This reinforces van Paradijs' finding that long outbursts in dwarf novae above the period gap and superoutbursts in systems below the period gap constitute a unified class. The thermal-tidal instability to account for superoutbursts in the SU UMa stars predicts embedded precursors only for short orbital period dwarf novae, therefore the presence of embedded precursors in long orbital period systems - U Gem and SS Cyg - argues for a more general mechanism to explain long outbursts.

Cannizzo, John K.↗

NASA NTRS1999

Information-Theoretic Assessment of Sample Imaging Systems

By rigorously extending modern communication theory to the assessment of sampled imaging systems, we develop the formulations that are required to optimize the performance of these systems within the critical constraints of image gathering, data transmission, and image display. The goal of this optimization is to produce images with the best possible visual quality for the wide range of statistical properties of the radiance field of natural scenes that one normally encounters. Extensive computational results are presented to assess the performance of sampled imaging systems in terms of information rate, theoretical minimum data rate, and fidelity. Comparisons of this assessment with perceptual and measurable performance demonstrate that (1) the information rate that a sampled imaging system conveys from the captured radiance field to the observer is closely correlated with the fidelity, sharpness and clarity with which the observed images can be restored and (2) the associated theoretical minimum data rate is closely correlated with the lowest data rate with which the acquired signal can be encoded for efficient transmission.

Huck, Friedrich O.↗

DOE OSTI2020

Sister Rod Destructive Examinations (FY20)

As a part of DOE NE High Burnup Spent Fuel Data Project, Oak Ridge National Laboratory (ORNL) is performing destructive examinations (DEs) of high burnup (HBU) (>45 GWd/MTU) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called sister rods or sibling rods, are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4 (Zirc-4), low-tin Zirc-4, ZIRLO, and M5. The DEs are being conducted to obtain a baseline of the HBU rod’s condition before dry storage and are focused on understanding overall SNF rod strength and durability. Composite fuel and defueled cladding will be tested to derive material properties. Although the data generated can be used for multiple purposes, one primary goal for obtaining the post-irradiation examination data and the associated measured mechanical properties is to support SNF dry storage licensing and relicensing activities by (1) addressing identified knowledge gaps and (2) enhancing the technical basis for post-storage transportation, handling, and subsequent disposition. This report documents the status of the ORNL Phase I DEs of 8 sister rods and outlines the DE tasks performed and the data collected to date, as guided by the sister rod test plans.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2016

Global Carbon Budget 2016

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere the global carbon budget is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates and consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models. We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as +/- 1(sigma), reflecting the current capacity to characterize the annual estimates of each component of the global carbon budget. For the last decade available (2006-2015), EFF was 9.3+/-0.5 GtC/yr, ELUC 1.0+/-0.5 GtC/yr,GATM 4.5+/-0.1 GtC/yr, SOCEAN 2.6+/-0.5 GtC/yr, and SLAND 3.1+/-0.9 GtC/yr. For year 2015 alone, the growth in EFF was approximately zero and emissions remained at 9.9+/-0.5 GtC/yr, showing a slowdown in growth of these emissions compared to the average growth of 1.8/yr that took place during 2006-2015.Also, for 2015, ELUC was 1.3+/-0.5 GtC/yr, GATM was 6.3+/-0.2 GtC/yr, SOCEAN was 3.0+/-0.5 GtC/yr, and SLAND was 1.9+/-0.9 GtC/yr. GATM was higher in 2015 compared to the past decade (2006-2015), reflecting a smaller SLAND for that year. The global atmospheric CO2 concentration reached 399.4+/-0.1 ppm averaged over 2015. For 2016, preliminary data indicate the continuation of low growth in EFF with +0.2% (range of -1.0 to +1.8% ) based on national emissions projections for China and USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. In spite of the low growth of EFF in 2016, the growth rate in atmospheric CO2 concentration is expected to be relatively high because of the persistence of the smaller residual terrestrial sink (SLAND) in response to El Nino conditions of 2015-2016. From this projection of EFF and assumed constant ELUC for 2016, cumulative emissions of CO2 will reach 565+/-55 GtC (2075+/-205 GtCO2) for 1870-2016, about 75% from EFF and 25% from ELUC. This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set.

Quéré, Corinne Le↗

NASA NTRS1977

On the development of earth observation satellite systems

Subsequent to the launching of the first LANDSAT by NASA, Japan has recognized the importance of data from earth observation satellites, has conducted studies, and is preparing to develop an independent system. The first ocean observation satellite will be launched in 1983, the second in 1985. The first land observation satellite is scheduled to be launched in 1987 and by 1990 Japan intends to have both land and ocean observation systems in regular operation. The association reception and data processing systems are being developed.

Source record↗

DOE OSTI2020

Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion

Robust Subspace Recovery (RoSuRe) algorithm was recently introduced as a principled and numerically efficient algorithm that unfolds underlying Unions of Subspaces (UoS) structure, present in the data. The union of Subspaces (UoS) is capable of identifying more complex trends in data sets than simple linear models. In this work, we build on and extend RoSuRe to prospect the structure of different data modalities individually. We propose a novel multi-modal data fusion approach based on group sparsity which we refer to as Robust Group Subspace Recovery (RoGSuRe). Relying on a bi-sparsity pursuit paradigm and non-smooth optimization techniques, the introduced framework learns a new joint representation of the time series from different data modalities, respecting an underlying UoS model. We subsequently integrate the obtained structures to form a unified subspace structure. The proposed approach exploits the structural dependencies between the different modalities data to cluster the associated target objects. The resulting fusion of the unlabeled sensors’ data from experiments on audio and magnetic data has shown that our method is competitive with other state of the art subspace clustering methods. The resulting UoS structure is employed to classify newly observed data points, highlighting the abstraction capacity of the proposed method.

47 OTHER INSTRUMENTATION↗