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

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Thermal Behavior of Unusual Local-Scale Features on Vesta

On Vesta, the thermal behavior of areas of unusual albedo seen at the local scale can be related to physical properties that can provide information about the origin of those materials. Dawn's Visible and Infrared Mapping Spectrometer (VIR) hyperspectral cubes are used to retrieve surface temperatures and emissivities, with high accuracy as long as temperatures are greater than ~180 K. Data acquired in the Survey phase (23 July through 29 August 2011) show several unusual surface features: 1) high-albedo (bright) and low-albedo (dark) material deposits, 2) spectrally distinct ejecta and pitted materials, 3) regions suggesting finer-grained materials. Some of the unusual dark and bright features were re-observed by VIR in the subsequent High-Altitude Mapping Orbit (HAMO) and Low-Altitude Mapping Orbit (LAMO) phases at increased pixel resolution. In particular, bright and dark surface materials on Vesta, and pitted materials, are currently being investigated by the Dawn team. In this work we present temperature maps and emissivities of several local-scale features that were observed by Dawn under different illumination conditions and different local solar times. To calculate surface temperatures, we applied a Bayesian approach to nonlinear inversion based on the Kirchhoff law and the Planck function, and whose results were compared with those provided by the application of alternative methods. Data from the IR channel of VIR show that bright regions generally correspond to regions with lower thermal emission, i.e. lower temperature, while dark regions correspond to areas with higher thermal emission, i.e. higher temperature. This behavior confirms that many of the dark appearances in the VIS mainly reflect albedo variations, and not, for example, shadowing. During maximum daily insolation, dark features in the equatorial region may rise to temperatures greater than 270 K, while brightest features stop at roughly 258 K for similar local solar times. However, pitted materials, showing relatively low reflectance, have significantly lower temperatures, as a result of differences in composition and/or structure (e.g, average grain size of the surface regolith, porosity, etc.). To complement this work, we provide preliminary values of thermal inertia for some bright and dark features.

Tosi, Federico↗
NASA NTRS2019

Feature Learning for Multispectral Satellite Imagery Classification Using Neural Architecture Search

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN.

Robert Campbell↗
NASA NTRS2019

Measurement of the Splashback Feature Around SZ-Selected Galaxy Clusters With DES, SPT, and ACT

We present a detection of the splashback feature around galaxy clusters selected using the Sunyaev–Zel’dovich (SZ) signal. Recent measurements of the splashback feature around optically selected galaxy clusters have found that the splashback radius, rsp, is smaller than predicted by N-body simulations. A possible explanation for this discrepancy is that rsp inferred from the observed radial distribution of galaxies is affected by selection effects related to the optical cluster-finding algorithms. We test this possibility by measuring the splashback feature in clusters selected via the SZ effect in data from the South Pole Telescope SZ survey and the Atacama Cosmology Telescope Polarimeter survey. The measurement is accomplished by correlating these cluster samples with galaxies detected in the Dark Energy Survey Year 3data. The SZ observable used to select clusters in this analysis is expected to have a tighter correlation with halo mass and to be more immune to projection effects and aperture-induced biases, potentially ameliorating causes of systematic error for optically selected clusters. We find that the measured rsp for SZ-selected clusters is consistent with the expectations from simulations, although the small number of SZ-selected clusters makes a precise comparison difficult. In agreement with previous work, when using optically selected red MaPPer clusters with similar mass and redshift distributions,rspis∼2σsmaller than in the simulations. These results motivate detailed investigations of selection biases in optically selected cluster catalogues and exploration of the splashback feature around larger samples of SZ-selected clusters. Additionally, we investigate trends in the galaxy profile and splashback feature as a function of galaxy colour, finding that blue galaxies have profiles close to a power law with no discernible splashback feature, which is consistent with them being on their first in fall into the cluster.

T Shin↗
NASA NTRSDate not supplied

Nearest-Neighbor Machine Learning Feature Selection for Interpretation of Microbial Molecular Signatures from Isotope Ratio Mass Spectrometry Data

Mass spectrometry (MS) promises to be a powerful tool for potential biosignature detection during astrobiological missions on ocean worlds in our solar system. Accurate and generalizable machine learning methods could enhance science return on investment by predicting seawater chemistry and classifying isotopic biosignatures, either as a signature consistent with microbial life (biotic) or as a novelty (unclassified/unique). However, machine learning models are likely to be complex and involve interactions between MS features, making biosignatures difficult to interpret. Feature selection methods provide biological and chemical context that help interpret the mechanisms of machine learning models, but these methods also need the ability to detect complex interactions. Previously, we developed a machine learning feature selection algorithm called nearest-neighbor projected distance regression (NPDR) that has the ability to identify important model features that involve complex interactions and automatically reduce correlation and the dimensionality in a high-dimensional variable space. The standard distance metrics used in NPDR – Manhattan and Euclidean – assume the multivariate data are isotropic, which is often violated in real data due to differences in the covariance between variables. Thus, we extend NPDR to include a random forest distance, and other anisotropic distance metrics, for computing nearest neighbors. We also augment the isotope-ratio MS data with time-series features from the raw MS signal to improve biotic classification. We test NPDR on our novel experimental ocean world seawater analog MS data. We measure isotope fractionations of volatile CO 2 that could be measured in exospheres or plumes. Samples include baseline abiotic conditions using a range of possible seawater chemistry consistent with Europa and Enceladus, and biotic samples that include microbes in these seawaters. We use penalized NPDR with random forest proximity to identify interpretable microbial molecular signatures. We compare features with random forest importance, and we train a classifier that discriminates between biotic and abiotic samples with high accuracy. These ML-trained ocean-world analog MS data could be used to assist in identifying biosignatures during future missions.

geochemistry↗
NASA NTRSDate not supplied

Feature Selection in High-Dimensional Space with Applications to Gene Expression Data

Recent years have seen rapid growth in high-dimensional datasets. Most existing machine learning (ML) algorithms fail in high-dimensional settings where many features could be redundant. A critical process of feature selection is thus applied in such a setting that helps in identifying the most relevant features while removing redundant ones. With the increase in high dimensionality, one is also faced with problems of efficiency and interpretation in performing such selection methods. Therefore, this paper proposes a “novel” feature selection framework that uses an ensemble of interpretable ML algorithms to perform feature selection and the ranking of final features. Finally, this framework is applied to a gene expression dataset obtained through collaboration with the National Aeronautics and Space Administration (NASA)’s Biological and Physical Sciences (BPS) team and helps identify important and relevant genes contributing to specific target attributes through classification tasks.

Nishan Pantha↗
DOE OSTI2025

Systematic feature design for cycle life prediction of lithium-ion batteries during formation

Optimization of the formation step in lithium-ion battery manufacturing is challenging due to limited physical understanding of solid-electrolyte interphase formation and the long testing time (∼100 days) for cells to reach the end of life. We propose a systematic feature-design framework that requires minimal domain knowledge for accurate cycle life prediction during formation. By only using two simple Q (V) features designed from our framework, extracted from formation data without any additional diagnostic cycles, we achieved an average of 9.87% error for cycle life prediction. Here, the physics-based investigation guided by the two designed features shows that the voltage ranges identified by our framework capture the effects of formation temperature and microscopic-particle resistance heterogeneity. By designing highly predictive, robust, and interpretable features, our approach can accelerate industrial battery formation research, leveraging the interplay between data-driven feature design and mechanistic understanding.

25 ENERGY STORAGE↗
DOE OSTI2025

Data-Driven State of Health Estimation for Second-Life Batteries Using Interpolated Synthetic Data and Feature Selection

Accurate estimation of the State of Health (SOH) for second-life batteries (SLBs) is crucial given their increasing use in energy storage applications. Precise SOH prediction is essential for safe operation and robust battery management systems. A major challenge is the limited availability of datasets for building reliable degradation models. To address this, synthetic data generation through linear interpolation is performed to extend the available data, making it more representative of real-world battery operating conditions. By analyzing feature correlation with SOH, the most relevant features are selected for the model. The proposed approach employs a convolutional neural network (CNN) model trained on this interpolated, feature-selected dataset, using time series data of voltage, temperature, and current over a cycle. By focusing on highly correlated features, the model achieves over 95% accuracy, with mean absolute error and root mean squared error up to 2.27% and 2.64%, respectively, in SOH estimation for two battery datasets tested. These results highlight the potential of combining synthetic data generation and feature selection to enhance SOH predictions, showcasing the superior performance of the proposed CNN model for both new batteries and SLBs.

feature selection↗
DOE OSTI2024

Identifying critical features of iron phosphate particle for lithium preference

One-dimensional (1D) olivine iron phosphate (FePO 4 ) is widely proposed for electrochemical lithium (Li) extraction from dilute water sources, however, significant variations in Li selectivity were observed for particles with different physical attributes. Understanding how particle features influence Li and sodium (Na) co-intercalation is crucial for system design and enhancing Li selectivity. Here, we investigate a series of FePO 4 particles with various features and revealed the importance of harnessing kinetic and chemo-mechanical barrier difference between lithiation and sodiation to promote selectivity. The thermodynamic preference of FePO 4 provides baseline of selectivity while the particle features are critical to induce different kinetic pathways and barriers, resulting in different Li to Na selectivity from 6.2 × 10 2 to 2.3 × 10 4 . Importantly, we categorize the FePO 4 particles into two groups based on their distinctly paired phase evolutions upon lithiation and sodiation, and generate quantitative correlation maps among Li preference, morphological features, and electrochemical properties. By selecting FePO 4 particles with specific features, we demonstrate fast (636 mA/g) Li extraction from a high Li source (1: 100 Li to Na) with (96.6 ± 0.2)% purity, and high selectivity (2.3 × 10 4 ) from a low Li source (1: 1000 Li to Na) with (95.8 ± 0.3)% purity in a single step.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Effect of sintering temperature on feature resolution and flexural strength of ceramics fabricated through vat photopolymerization additive manufacturing

Although ceramic additive manufacturing (AM) could be used to fabricate complex, high-resolution parts for diverse, functional applications, one ongoing challenge is optimizing the post-process, particularly sintering, conditions to consistently produce geometrically accurate and mechanically robust parts. This study aims to investigate how sintering temperature affects feature resolution and flexural properties of silica-based parts formed by vat photopolymerization (VPP) AM. Test artifacts were designed to evaluate features of different sizes, shapes and orientations, and three-point bend specimens printed in multiple orientations were used to evaluate mechanical properties. Sintering temperatures were varied between 1000°C and 1300°C. Deviations from designed dimensions often increased with higher sintering temperatures and/or larger features. Higher sintering temperatures yielded parts with higher strength and lower strain at break. Many features exhibited defects, often dependent on geometry and sintering temperature, highlighting the need for further analysis of debinding and sintering parameters. To the best of the authors’ knowledge, this is the first time test artifacts have been designed for ceramic VPP. This work also offers insights into the effect of sintering temperature and print orientation on flexural properties. These results provide design guidelines for a particular material, while the methodology outlined for assessing feature resolution and flexural strength is broadly applicable to other ceramics, enabling more predictable part performance when considering the future design and manufacture of complex ceramic parts.

36 MATERIALS SCIENCE↗
DOE OSTI2026

F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding

Interactive time-varying volume visualization is challenging due to its complex spatiotemporal features and sheer size of the dataset. Recent works transform the original discrete time-varying volumetric data into continuous Implicit Neural Representations (INR) to address the issues of compression, rendering, and super-resolution in both spatial and temporal domains. However, training the INR takes a long time to converge, especially when handling large-scale time-varying volumetric datasets. In this work, we proposed F-Hash, a novel feature-based multi-resolution Tesseract encoding architecture to greatly enhance the convergence speed compared with existing input encoding methods for modeling time-varying volumetric data. The proposed design incorporates multi-level collision-free hash functions that map dynamic 4D multi-resolution embedding grids without bucket waste, achieving high encoding capacity with compact encoding parameters. Our encoding method is agnostic to time-varying feature detection methods, making it a unified encoding solution for feature tracking and evolution visualization. Experiments show the F-Hash achieves state-of-the-art convergence speed in training various time-varying volumetric datasets for diverse features. We also proposed an adaptive ray marching algorithm to optimize the sample streaming for faster rendering of the time-varying neural representation.

deep learning↗
NASA NTRS1974

Skylab-3 handheld photography alphabetized geographical features list

The data was thoroughly researched using the Times Index-Gazetteer of the World, the Times Atlas of the World, and the National Atlas of the United States of America to ensure correct spelling and location of named features. The spelling and major geographical features applied to smaller features (minor) such as cities, towns, mountain peaks, etc., are in accordance with these publications. It is understood that some political boundaries and names of countries are subject to change. The data was written on NASA keypunch transmittal sheets and punched into data cards. These data cards were then machine sorted alphabetically by major feature and minor feature.

Mcniel, J. L.↗
NASA NTRS1973

Comparative evaluation of spatial features in automatic land use classification from photographic imagery

Five spatial features have been evaluated for their applicability in automatic land use classification of photographic imagery. Data arrays of approximately 10,000 square meters were classified on the basis of an 8 by 8 point data array. Statistical features, information features, sequency features and texture features were evaluated using a distance to prototype classifier and an adaptive classifier. The results indicate approximately 70% accuracy in the classification.

Herzog, J. H.↗
NASA NTRS1975

Cosmic ray exposure ages of features and events at the Apollo landing sites

Cosmic-ray exposure ages of lunar samples have been used to date surface features related to impact cratering and downslope movement of material. Only when multiple samples related to a feature have the same rare-gas exposure age or when a single sample has the same Kr-81 -Kr and track-exposure age can a feature be considered as reliably dated. Based on these criteria, there are only five well-dated lunar features: Cone Crater (Apollo 14), 26 m.y,; North Ray Crater (Apollo 16), 50 m.y.; South Ray Crater (Apollo 16), 2 m.y.; the emplacement of the Station 6 boulders (Apollo 17), 22 m.y.; and the emplacement of the Station 7 boulder (Apollo 17), 28 m.y. Other features are tentatively dated or have limits set on their ages: Bench Crater (Apollo 12), upper limit of 99 m.y.; Baby Ray Crater (Apollo 16), upper limit of 2 m.y.; Shorty Crater (Apollo 17), approximately 30 m.y.; Camelot Crater (Apollo 17) upper limit of 140 m.y.; the emplacement of the Station 2 boulder 1 (Apollo 17), 45 to 55 m.y.; and the slide which generated the light mantle (Apollo 17), lower limit of 50 m.y.

Arvidson, R.↗
NASA NTRS1975

Geological significance of features observed in Colorado from orbital altitudes

Three major investigations using LANDSAT and Skylab imagery concerned with analyses of color anomalies and linear features of central Colorado are discussed. The studies conducted are concerned with the geological significance of spectral and spatial features on the images. Color anomalies in Skylab photographs were analyzed and evaluated for locating indicators of mineralization. The relationships were determined of all linear features in a LANDSAT image to the rock joint systems and the detectable larger geologic structures; techniques for extracting that geologic information are indicated. Some anomalous megalinear features in LANDSAT and Skylab images are analyzed which transect major structures and, their associated geologic features are described.

Sawatzky, D. L.↗
NASA NTRS1975

Small-scale imagery: A useful tool for mapping geological features in the Texas Gulf Coastal plain

A sample of satellite imagery includes photographs and multispectral scanner imagery from the Apollo, LANDSAT, and Skylab spacecraft over an area west of Houston, Texas. Large circular features and lineaments can readily be mapped. One of the circular features identified by drainage, vegetation, and soil-tone anomalies, occurs in Fort Bend County, Texas, and covers an area about 25 miles in diameter. Another circular feature lies to the north, immediately west of Houston, and is about 15 miles in diameter. These features represent the surface expression of deeply-buried positive structures. It is recommended that regional geological studies begin with photointerpretations of high-altitude aircraft and synoptic space imagery, space imagery provides the synoptic view of rapid evaluation of large-scale features, and, at enlarged scales, the resolution required for detailed study of selected areas of structures.

Amsbury, D. L.↗
NASA NTRS1977

High-resolution rocket spectra of the 1920-A and 1720-A features in the spectrum of Zeta Tauri

High-resolution ultraviolet spectrograms of the B-shell star Zeta Tau reveal two features characteristic of B supergiants, one at 1720 A and the other at 1920 A. The presence of these features in the spectrum of this object shows that they are indicative of an extended atmosphere - either the tenuous atmosphere of a supergiant or the envelope surrounding a rapidly rotating main-sequence star - and are therefore not purely luminosity criteria. The high spectral resolution allows an identification of the contributors to these features. The dominant contributor to the 1920-A feature is Fe III, while the primary contributor to the 1720-A feature is Al II.

Heap, S. R.↗
NASA NTRS1979

Laboratory simulation of infrared astrophysical features

Laboratory infrared emission and absorption spectra have been taken of terrestrial silicates, meteorites, and lunar soils in the form of micrometer and submicrometer grains. The emission spectra were taken in a way that imitates telescopic observations. The purpose was to see which materials best simulate the 10-micron astrophysical feature. The emission spectra of dunite, fayalite, and Allende give a good fit to the 10-micron broadband emission feature of comets Bennett and Kohoutek. A study of the effect of grain size on the presence of the 10-micron emission feature of dunite shows that for particles larger than 37 microns no feature is seen. The emission spectrum of the Murray meteorite, a Type 2 carbonaceous chrondrite, is quite similar to the intermediate-resolution spectrum of comet Kohoutek in the 10-micron region. Hydrous silicates or amorphous magnesium silicates in combination with high-temperature condensates, such as olivine or anorthite, would yield spectra that match the intermediate-resolution spectrum of comet Kohoutek in the 10-micron region. Glassy olivine and glassy anorthite in approximately equal proportions would also give a spectrum that is a good fit to the cometary 10-micron feature.

Rose, L. A.↗
NASA NTRS1979

Earth feature identification and tracking technology development

The paper discusses needs for smart sensing in terrestrial and atmospheric remote sensing as related to current technology and a scheduled Shuttle experiment. An approach is outlined involving Shuttle-borne experiments to develop earth feature identification and tracking technology including a Feature Identification and Location Experiment (FILE) scheduled for flight on the NASA Shuttle with an objective of classifying earth features into categories of bare land, water, vegetation, and clouds, snow, and ice. The plan for evolution of the FILE-related technology leads to capabilities for pointing instruments to predetermined sites, reacquiring earth features or landmarks, and tracking features such as coastlines and rivers. Technology concepts relative to an overall system transfer function is discussed, and the development status outlined.

Wilson, R. G.↗