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At least 1,387 records · Page 77

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NASA NTRS2020

Visible-near Infrared Spectral Indices for Mapping Mineralogy and Chemistry with OSIRIS-Rex

The primary objective of the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) mission is to return to Eartha pristine sample of carbonaceous material from the primitive asteroid (101955) Bennu. To support compositional mapping of Bennu as part of sample site selection and characterization, we tested 95 spectral indices on visible to near infrared laboratory reflectance data from minerals and carbonaceous meteorites. Our aim was to determine which indices reliably identify spectral features of interest. Most spectral indices had high positive detection rates when applied to spectra of pure, single-component materials. The meteorite spectra have fewer and weaker absorption features and, as a result, fewer detections with the spectral indices. Indices targeting absorptions at 0.7 and 2.7–3lm, which are attributable to hydrated minerals, were most successful for the meteorites. Based on these results, we identified a set of 17 indices that are most likely to be useful at Bennu. These indices detect olivines, pyroxenes, carbonates, water/OH-bearing minerals, serpentines, ferric minerals, and organics. Particle size and albedo are known to affect band depth but had a negligible impact on interpretive success with spectral indices. Preliminary analysis of the disk-integrated Bennu spectrum with these indices is consistent with expectations given the observed absorption near 3lm. Our study prioritizes spectral indices to be used for OSIRIS-REx spectral analysis and mapping and informs the reliability of all index-derived data products, including a science value map for sample site selection.

Hannah H. Kaplan↗

NASA NTRS2020

Visible–near infrared spectral indices for mapping mineralogy and chemistry with OSIRIS-REx

The primary objective of the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) mission is to return to Earth a pristine sample of carbonaceous material from the primitive asteroid (101955) Bennu. To support compositional mapping of Bennu as part of sample site selection and characterization, we tested 95 spectral indices on visible to near infrared laboratory reflectance data from minerals and carbonaceous meteorites. Our aim was to determine which indices reliably identify spectral features of interest. Most spectral indices had high positive detection rates when applied to spectra of pure, single-component materials. The meteorite spectra have fewer and weaker absorption features and, as a result, fewer detections with the spectral indices. Indices targeting absorptions at 0.7 and 2.7–3 µm, which are attributable to hydrated minerals, were most successful for the meteorites. Based on these results, we identified a set of 17 indices that are most likely to be useful at Bennu. These indices detect olivines, pyroxenes, carbonates, water/OH-bearing minerals, serpentines, ferric minerals, and organics. Particle size and albedo are known to affect band depth but had a negligible impact on interpretive success with spectral indices. Preliminary analysis of the disk-integrated Bennu spectrum with these indices is consistent with expectations given the observed absorption near 3 lm. Our study prioritizes spectral indices to be used for OSIRIS-REx spectral analysis and mapping and informs the reliability of all index-derived data products, including a science value map for sample site selection.

Hannah H. Kaplan↗

NASA NTRS1985

Systematic estimation of forecast and observation error covariances in four-dimensional data assimilation

A two-part algorithm is presented for reliably computing weather forecast model and observational error covariances during data assimilation. Data errors arise from instrumental inaccuracies and sub-grid scale variability, whereas forecast errors occur because of modeling errors and the propagation of previous analysis errors. A Kalman filter is defined as the primary algorithm for estimating the forecast and analysis error convariance matrices. A second algorithm is described for quantifying the noise covariance matrices of any degree to obtain accurate values for the observational error covariances. Numerical results are provided from a linearized one-dimensional shallow-water model. The results cover observational noise covariances, initial instrumental errors and erroneous model values.

Dee, D. P.↗

NASA NTRS1990

Neural network approaches versus statistical methods in classification of multisource remote sensing data

Neural network learning procedures and statistical classificaiton methods are applied and compared empirically in classification of multisource remote sensing and geographic data. Statistical multisource classification by means of a method based on Bayesian classification theory is also investigated and modified. The modifications permit control of the influence of the data sources involved in the classification process. Reliability measures are introduced to rank the quality of the data sources. The data sources are then weighted according to these rankings in the statistical multisource classification. Four data sources are used in experiments: Landsat MSS data and three forms of topographic data (elevation, slope, and aspect). Experimental results show that two different approaches have unique advantages and disadvantages in this classification application.

Benediktsson, Jon A.↗

NASA NTRS1993

Multi-filter spectrophotometry of quasar environments

A many-filter photometric technique for determining redshifts and morphological types, by fitting spectral templates to spectral energy distributions, has good potential for application in surveys. Despite success in studies performed on simulated data, the results have not been fully reliable when applied to real, low signal-to-noise data. We are investigating techniques to improve the fitting process.

Craven, Sally E.↗

NASA NTRS2012

A Systems Modeling Approach for Risk Management of Command File Errors

The main cause of commanding errors is often (but not always) due to procedures. Either lack of maturity in the processes, incompleteness of requirements or lack of compliance to these procedures. Other causes of commanding errors include lack of understanding of system states, inadequate communication, and making hasty changes in standard procedures in response to an unexpected event. In general, it's important to look at the big picture prior to making corrective actions. In the case of errors traced back to procedures, considering the reliability of the process as a metric during its' design may help to reduce risk. This metric is obtained by using data from Nuclear Industry regarding human reliability. A structured method for the collection of anomaly data will help the operator think systematically about the anomaly and facilitate risk management. Formal models can be used for risk based design and risk management. A generic set of models can be customized for a broad range of missions.

probabilistic risk↗

DOE OSTI2018

Pattern Mining and Anomaly Detection based on the Power System Synchrophasor Measurements

Real-time monitoring of power system dynamics using phasor measurement units (PMUs) data improves situational awareness and system reliability and potentially can help prevent blackouts due to early anomaly detection. The study presented in the paper is based on the actual PMU measurements of the U.S. western interconnection system. Given the nonlinear and nonstationary PMUs data, we developed a robust anomaly detection framework. Wavelet-based multi-resolution analysis, combined with moving-window-based outlier detection and anomaly scoring, were deployed to identify potential PMU events. The candidate events were evaluated using spatiotemporal correlation analysis, and then classified for a better understanding of the event types. The results demonstrated successful anomaly detection and classification, compared with the recorded real-world events.

Ren, Huiying↗

DOE OSTI2026

Integrated Approach to Ancillary PV Component Reliability Assessment (Final Report)

In this project, we have established a nondestructive, generalized methodology that (1) fuses rich field data with advanced ML for proactive reliability forecasting, (2) dramatically reduces experimental iterations via synthetic dataset generation, and (3) achieves unprecedented regression precision in both anomaly detection and component-level degradation assessment—paving the way for truly predictive maintenance of grid-tied PV inverters under diverse outdoor conditions.

14 SOLAR ENERGY↗

DOE OSTI2025

Exploring the interplay between distributed wind generators and solar photovoltaic systems

This study investigates the spatial and temporal dynamics of wind and solar energy generation across the continental United States, focusing on energy availability, reliability, variability, and cooperation. Using data from the National Renewable Energy Laboratory, we analyze the performance of wind turbines and photovoltaic systems, revealing distinct patterns in energy production and reliability. The classification of wind and solar zones based on energy availability and reliability provides valuable insights for renewable energy planning and grid integration strategies. Overall, this methodology contributes to understanding wind and solar interactions, which will lead to informing effective renewable energy deployment and grid integration efforts.

14 SOLAR ENERGY↗

NASA NTRS2014

Software Suite to Support In-Flight Characterization of Remote Sensing Systems

A characterization software suite was developed to facilitate NASA's in-flight characterization of commercial remote sensing systems. Characterization of aerial and satellite systems requires knowledge of ground characteristics, or ground truth. This information is typically obtained with instruments taking measurements prior to or during a remote sensing system overpass. Acquired ground-truth data, which can consist of hundreds of measurements with different data formats, must be processed before it can be used in the characterization. Accurate in-flight characterization of remote sensing systems relies on multiple field data acquisitions that are efficiently processed, with minimal error. To address the need for timely, reproducible ground-truth data, a characterization software suite was developed to automate the data processing methods. The characterization software suite is engineering code, requiring some prior knowledge and expertise to run. The suite consists of component scripts for each of the three main in-flight characterization types: radiometric, geometric, and spatial. The component scripts for the radiometric characterization operate primarily by reading the raw data acquired by the field instruments, combining it with other applicable information, and then reducing it to a format that is appropriate for input into MODTRAN (MODerate resolution atmospheric TRANsmission), an Air Force Research Laboratory-developed radiative transport code used to predict at-sensor measurements. The geometric scripts operate by comparing identified target locations from the remote sensing image to known target locations, producing circular error statistics defined by the Federal Geographic Data Committee Standards. The spatial scripts analyze a target edge within the image, and produce estimates of Relative Edge Response and the value of the Modulation Transfer Function at the Nyquist frequency. The software suite enables rapid, efficient, automated processing of ground truth data, which has been used to provide reproducible characterizations on a number of commercial remote sensing systems. Overall, this characterization software suite improves the reliability of ground-truth data processing techniques that are required for remote sensing system in-flight characterizations.

Stanley, Thomas↗

DOE OSTI2024

Cloud-based Testbed for Adaptive Under-Frequency Load Shedding with High DER Penetration

Increasing penetration of distributed energy resources and behind-the-meter renewables may soon disrupt the efficacy of critical protection schemes, such as under-frequency load shedding (UFLS). Improved data exchange and coordination across the transmission-distribution boundary will be required to maintain reliability of bulk electric system. Standards-based data integration platforms using agreed-upon semantic vocabularies, such as the Common Information Model, will be key to enabling adaptive protection schemes requiring synthesized data from both the bulk power system and behind-the-meter resources. This paper introduces a cloud-based open-source data integration environment and UFLS clustering algorithm being developed to enable adaptive relay coordination between transmission and distribution utilities in the state of Vermont.

Anderson, Alexander A.↗

DOE OSTI2023

Deep neural network uncertainty quantification for LArTPC reconstruction

We evaluate uncertainty quantification (UQ) methods for deep learning applied to liquid argon time projection chamber (LArTPC) physics analysis tasks. As deep learning applications enter widespread usage among physics data analysis, neural networks with reliable estimates of prediction uncertainty and robust performance against overconfidence and out-of-distribution (OOD) samples are critical for their full deployment in analyzing experimental data. While numerous UQ methods have been tested on simple datasets, performance evaluations for more complex tasks and datasets are scarce. Here we assess the application of selected deep learning UQ methods on the task of particle classification using the PiLArNet monte carlo 3D LArTPC point cloud dataset. We observe that UQ methods not only allow for better rejection of prediction mistakes and OOD detection, but also generally achieve higher overall accuracy across different task settings. We assess the precision of uncertainty quantification using different evaluation metrics, such as distributional separation of prediction entropy across correctly and incorrectly identified samples, receiver operating characteristic curves (ROCs), and expected calibration error from observed empirical accuracy. We conclude that ensembling methods can obtain well calibrated classification probabilities and generally perform better than other existing methods in deep learning UQ literature.

47 OTHER INSTRUMENTATION↗

DOE OSTI2019

Acoustic- and elastic-waveform inversion with total generalized p-variation regularization

Geophysical models usually contain both sharp interfaces and smooth variations, and it is difficult to accurately account for both of these two types of medium parameter variations using conventional full-waveform inversion methods. We develop a novel full-waveform inversion method for acoustic and elastic waves using a total generalized p-variation regularization scheme to address these challenging problems. We decompose the full-waveform inversion into two subproblems and solve these two minimization subproblems using an alternating-direction minimization strategy. One significant advantage of the total generalized p-variation regularization scheme is that it can simultaneously reconstruct sharp interfaces and smooth background variations of geophysical parameters. Such capability can also effectively suppress the noises in source-encoded inversion and sparse-data inversion, or inversion of noisy data. We demonstrate the advantages of our full-waveform inversion method using a checkerboard model, a modified elastic SEG/EAGE overthrust model, and a land field seismic data set. Our conclusions of synthetic and field seismic data demonstrate that our method reconstructs both smooth background variations and sharp interfaces of subsurface geophysical properties accurately, and provides a useful tool for accurate and reliable inversion of field seismic data.

58 GEOSCIENCES↗

NASA NTRS1993

An extended laser flash technique for thermal diffusivity measurement of high-temperature materials

Knowledge of thermal diffusivity data for high-temperature materials (solids and liquids) is very important in analyzing a number of processes, among them solidification, crystal growth, and welding. However, reliable thermal diffusivity versus temperature data, particularly those for high-temperature liquids, are still far from complete. The main measurement difficulties are due to the presence of convection and the requirement for a container. Fortunately, the availability of levitation techniques has made it possible to solve the containment problem. Based on the feasibility of the levitation technology, a new laser flash technique which is applicable to both levitated liquid and solid samples is being developed. At this point, the analysis for solid samples is near completion and highlights of the technique are presented here. The levitated solid sample which is assumed to be a sphere is subjected to a very short burst of high power radiant energy. The temperature of the irradiated surface area is elevated and a transient heat transfer process takes place within the sample. This containerless process is a two-dimensional unsteady heat conduction problem. Due to the nonlinearity of the radiative plus convective boundary condition, an analytic solution cannot be obtained. Two options are available at this point. Firstly, the radiation boundary condition can be linearized, which then accommodates a closed-form analytic solution. Comparison of the analytic curves for the temperature rise at different points to the experimentally-measured values will then provide the thermal diffusivity values. Secondly, one may set up an inverse conduction problem whereby experimentally obtained surface temperature history is used as the boundary conditions. The thermal diffusivity can then be elevated by minimizing the difference between the real heat flux boundary condition (radiation plus convection) and the measurements. Status of an experimental study directed at measuring the thermal diffusivity of high-temperature solid samples of pure Nickel and Inconel 718 superalloys are presented. Preliminary measurements showing surface temperature histories are discussed.

Shen, F.↗

NASA NTRS2011

Ares I and Ares I-X Stage Separation Aerodynamic Testing

The aerodynamics of the Ares I crew launch vehicle (CLV) and Ares I-X flight test vehicle (FTV) during stage separation was characterized by testing 1%-scale models at the Arnold Engineering Development Center s (AEDC) von Karman Gas Dynamics Facility (VKF) Tunnel A at Mach numbers of 4.5 and 5.5. To fill a large matrix of data points in an efficient manner, an injection system supported the upper stage and a captive trajectory system (CTS) was utilized as a support system for the first stage located downstream of the upper stage. In an overall extremely successful test, this complex experimental setup associated with advanced postprocessing of the wind tunnel data has enabled the construction of a multi-dimensional aerodynamic database for the analysis and simulation of the critical phase of stage separation at high supersonic Mach numbers. Additionally, an extensive set of data from repeated wind tunnel runs was gathered purposefully to ensure that the experimental uncertainty would be accurately quantified in this type of flow where few historical data is available for comparison on this type of vehicle and where Reynolds-averaged Navier-Stokes (RANS) computational simulations remain far from being a reliable source of static aerodynamic data.

Pinier, Jeremy T.↗

DOE OSTI2023

Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions

Abstract Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery. In this work, we develop a deep learning synthesizability model ( SynthNN ) that leverages the entire space of synthesized inorganic chemical compositions. By reformulating material discovery as a synthesizability classification task, SynthNN identifies synthesizable materials with 7× higher precision than with DFT-calculated formation energies. In a head-to-head material discovery comparison against 20 expert material scientists, SynthNN outperforms all experts, achieves 1.5× higher precision and completes the task five orders of magnitude faster than the best human expert. Remarkably, without any prior chemical knowledge, our experiments indicate that SynthNN learns the chemical principles of charge-balancing, chemical family relationships and ionicity, and utilizes these principles to generate synthesizability predictions. The development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials.

36 MATERIALS SCIENCE↗

DOE OSTI2024

SIGHT: Stacked Integration of Geospatial Hierarchical Typologies for Inferring Building Characteristics

Building characteristics are often absent in building stock datasets, particularly in regions most vulnerable to climate change and requiring effective disaster management strategies. Traditional machine learning approaches, while widely used to predict building attributes, typically neglect the spatial context of the data, leading to less accurate and reliable outcomes. To address these challenges, this paper introduces a novel algorithm, the Stacked Integration of Geospatial Hierarchical Typologies. This algorithm adapts a meta-learning framework to incorporate geospatial context into the predictive modeling process. We demonstrate the utility of the algorithm through two primary use cases: building use type classification and building height prediction. The algorithm consistently achieved or exceeded a 0.94 macro average F1 score across five geographically distinct countries for building use type classification. For building height prediction, it accurately predicted heights with a root mean square error of 3.01 in a comprehensive study using roughly 3.6 million buildings in Japan. These results underscore the benefits of integrating spatial hierarchies into machine learning models, enhancing both predictive accuracy and reliability in geospatial modeling. This work introduces a new algorithm to address the pervasive data sparsity issue in existing building stock datasets.

Adams, Daniel [ORNL] (ORCID:0000000196950577)↗

NASA NTRS1972

Reliability of hybrid microcircuit discrete components

Data accumulated during 4 years of research and evaluation of ceramic chip capacitors, ceramic carrier mounted active devices, beam-lead transistors, and chip resistors are presented. Life and temperature coefficient test data, and optical and scanning electron microscope photographs of device failures are presented and the failure modes are described. Particular interest is given to discrete component qualification, power burn-in, and procedures for testing and screening discrete components. Burn-in requirements and test data will be given in support of 100 percent burn-in policy on all NASA flight programs.

Allen, R. V.↗