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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Cartographic experiment for Latin America

The author has identified the following significant results. The two experiments clearly demonstrate the practical application of the Skylab photography to update existing maps at an optimum scale of 1:100,000. The photography can even be used, by employing first order photogrammetric instruments, for updating the cultural features in 1:50,000 scale mapping. The S190A imagery has also shown itself to be most economical in preparing new photomap products over previously unmapped areas, such as Concepcion, Paraguay. These maps indicate that Skylab quality imagery is invaluable to the Latin American cartographers in their efforts to provide the mapping products required to develop their countries. In Latin America, where over 5,000 people are employed in map production and where the Latin American governments are expending over $20 million in this effort, the use of such systems to maintain existing mapping and publish new mapping over previously unmapped areas, is of great economic value and could release the conventional Latin American mapping resources to be utilized to produce large scale 1:25,000 and 1:1,000 scale mapping that is needed for specific development projects.

Staples, J. E.↗
NASA NTRS1994

High resolution visible to short-wave near-infrared CCD spectra of Mars during 1990

The 0.4 to 1.0 micron spectrum of Mars is dominated by a steep red, relatively featureless spectral slope. Earlier lower spectral observations interpreted the red color and the lack of absorption features in the spectra as evidence of poorly crystalline ferric oxide minerals. More recent higher spectral resolution observations and reinterpretations of older data sets have revealed measureable spectral structure, however. For example, absorption features near 0.65 and 0.86 micron were detected and spatially mapped in data obtained during the 1988 opposition. These absorptions were interpreted as evidence for crystalline hematite on Mars, occuring as an accessory phase in abundances of 3 to 6 percent in the soil. We are attempting to verify the existence of these subtle crystalline Fe(3+) absorption features and to map their spatial distribution in regions of the planet not imaged in 1988. During the 1990 opposition, we obtained imaging spectroscopic data of Mars from the University of Hawaii 2.24 m telescope at Mauna Kea Observatory. The data were obtained with the Wide Field Grism Spectrograph (WFGS), which uses an 800 x 800 CCD and a transmission grating ruled on a prism. We used a grating blazed at 4800 A in first order to obtain data from 0.50 to 0.94 micron at a spectral resolution of R = 200 to 350. The moon/Mars slit design used had projected dimensions of 0.29 x 153 inches, allowing for high spectral resolution and adequate cross-slit spatial sampling of the Martian disk.

Bell, James F., III↗
NASA NTRS1991

Collection of Magellan Venus Radar Mapping Results

Through computer animation several geological features of Venus are presented in this video. The Sif Mons, a 1.2 mile high volcano and the Gula Mons, a 1.8 mile high volcano are shown. Also, radar images of a rift valley, several impact craters, and a corona can be seen. The video ends with a northeast view of Eistla Regio.

Source record↗
NASA NTRS1987

On the relation of 6.7-micron water vapour features to isentropic distributions of potential vorticity

Currently available isentropic maps of potential vorticity (IPV maps) are of low resolution. In this note, the possibility of using 6.7-micron satellite-derived water vapor measurements to obtain higher resolution information about IPV distributions is examined. While attempts to related the two data sets analytically have so far proved unsuccessful, a qualitative comparison shows very similar features in both. The results obtained here suggest that, over limited areas, it may be possible to use 6.7-micron brightness temperatures to obtain approximate contours for water vapor mixing ratio on a particular isentropic surface in the upper troposphere. For areas free of high clouds, these contours are expected to parallel the IPV contours on this surface. In a case study, the resulting map exhibits dramatically increased horizontal resolution compared with present IPV maps, detailing an intriguing upper-tropospheric feature at subsynoptic scales.

Manney, Gloria L.↗
NASA NTRS1982

The intercrater plains of Mercury and the Moon: Their nature, origin and role in terrestrial planet evolution. Constuction of the paleogeologic maps

The Post Caoris surface was derived from the geologic map by plotting all Class 1 and 2 features. To construct the Caloris surface, Class 3 craters were plotted onto the map, as well as all Class 3 plains. However, if P3 plains were adjacent to P2 units, and appeared continuous with other exposures of P3 material, the P2 unit was assumed to overlie the C3 and P3 material. The younger superposed craters were ignored with respect to the Class 3 surface. The boundaries of P3 materials were then continued under the superposed units, using a minimum of reasonable assumptions. For instance, if P2 and P4 plains were adjacent units, no P3 plains were presumed to lie under the P2 material. Similarly, all C3 craters were considered to have some deposits of impact melt after formation, even if they are mapped containing younger units. C3 craters which were superposed with younger units, C1 or C2 craters, and perhaps P2 plains, were redrawn as if later materials had not been emplaced, i.e., in their post impact, pre-degradation states.

Leake, M. A.↗
NASA NTRS1992

Anomalous scattering behavior of selected impact parabola features: Magellan cycle-to-cycle comparisons

Magellan observations indicate that many venusian impact craters have associated surfaces, typically lower in backscatter and emissivity than the surroundings, that extend up to hundreds of kilometers to the west of craters, in parabolic planforms. During Magellan's second mapping cycle, a number of these parabolic features were imaged for a second time, under a different viewing geometry. In some cases, the SAR backscatter appearance of portions of the parabolic features was quite different in the two datasets. We present a description and preliminary interpretations of the anomalous appearance of these features as observed during Magellan's first and second mapping cycles.

Plaut, Jeffrey J.↗
DOE OSTI2025

Allosteric prediction via convolutional neural networks and protein structural and dynamical features

Allostery is the phenomenon whereby a binding event or covalent modification at one site in a protein modulates function at a distal site, thus changing a protein’s functional state. As such, it is a ubiquitous aspect of protein functional regulation. Computationally predicting allosteric states is important as part of the broader challenge of functional annotation, but it also has practical implications for drug development, as targeting an allosteric site often affords greater specificity compared with targeting an orthosteric site. This study introduces a machine learning approach to predict the allosteric functional state using the small G-protein KRas as the model system, due to its implication in many types of cancer and being well studied as a result with many x-ray crystallographic structures of KRas available with different mutations and ligands bound. Using structural and dynamical features that can be cast as images, namely interatomic distances, contact maps, covariance, and mutual information, supervised learning was performed using convolutional neural networks. Two pretrained convolutional neural network architectures, GoogLeNet and ResNet18, were fine-tuned to classify KRas into active or inactive states based on these features. Across training regimes, atomic contact maps emerged as the most effective structural feature, whereas linearized mutual information outperformed covariance in capturing dynamical correlations relevant to allostery. Models achieved significant validation accuracy, with atomic contact maps yielding up to 90% accuracy. In conclusion, the findings suggest that integrating global structural rearrangements and correlated motion patterns with deep learning can reliably predict protein allosteric states, offering a promising framework for understanding allosteric regulation and developing targeted therapeutics.

Rajeshwar T., Rajitha [Oak Ridge National Laborato↗
DOE OSTI2021

ORCA: Outlier detection and Robust Clustering for Attributed graphs

Here, a framework is proposed to simultaneously cluster objects and detect anomalies in attributed graph data. Our objective function along with the carefully constructed constraints promotes interpretability of both the clustering and anomaly detection components, as well as scalability of our method. In addition, we developed an algorithm called Outlier detection and Robust Clustering for Attributed graphs (ORCA) within this framework. ORCA is fast and convergent under mild conditions, produces high quality clustering results, and discovers anomalies that can be mapped back naturally to the features of the input data. The efficacy and efficiency of ORCA is demonstrated on real world datasets against multiple state-of-the-art techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Data-driven framework↗
DOE OSTI2021

Laboratory earthquake forecasting: A machine learning competition

Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google’s ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data. The more than 4,500 participating teams created and shared more than 400 computer programs in openly accessible notebooks. Complementing the now well-known features of seismic data that map to fault criticality in the laboratory, the winning teams employed unexpected strategies based on rescaling failure times as a fraction of the seismic cycle and comparing input distribution of training and testing data. In addition to yielding scientific insights into fault processes in the laboratory and their relation with the evolution of the statistical properties of the associated seismic data, the competition serves as a pedagogical tool for teaching ML in geophysics. The approach may provide a model for other competitions in geosciences or other domains of study to help engage the ML community on problems of significance.

58 GEOSCIENCES↗
DOE OSTI2021

Tensorized Feature Spaces for Feature Explosion

In this paper 1 1 This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-000R22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan) This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-000R22725., we present a novel framework that uses tensor factorization to generate richer feature spaces for pixel classification in hyperspectral images. In particular, we assess the performance of different tensor rank decomposition methods as compared to the traditional kernel-based approaches for the hyperspectral image classification problem. We propose Orion, which takes as input a hyperspectral image tensor and a rank and outputs an enhanced feature space from the factor matrices of the decomposed tensor. Our method is a feature explosion technique that inherently maps low dimensional input space in $\mathbb{R}^{K}$ to high dimensional space in $\mathbb{R}^{R}$ , where $R\gg K$ , say in the order of 1000x, like a kernel. We show how the proposed method exploits the multi-linear structure of hyperspectral three dimensional tensor. We demonstrate the effectiveness of our method with experiments on three publicly available hyperspectral datasets with labeled pixels and compare their classification performance against traditional linear and non-linear supervised learning methods such as SVM with Linear, Polynomial, RBF kernels, and the Multi-Layer Perceptron model. Finally, we explore the relationship between the rank of the tensor decomposition and the classification accuracy using several hyperspectral datasets with ground truth.

Pasricha, Ravdeep Singh↗
DOE OSTI2026

Supervised Learning-Based Spatial Position Estimation with Vertical Displacement for Hovering UAV Wireless Power Transfer

This study presents a supervised learning-based spatial position estimation approach for wireless power transfer (WPT) systems supporting hovering unmanned aerial vehicle (UAV) charging. Unlike stationary charging scenarios, hovering UAVs introduce continuous lateral misalignment and vertical displacement, leading to variations in magnetic coupling and reduced power transfer efficiency. To address this challenge, the proposed method estimates the relative spatial position of the receiver coil using only electrical measurements obtained at the secondary side. A supervised learning model is trained to map output voltage and current features to spatial coordinates, enabling position awareness without requiring external sensors, vision systems, or communication links. The sensing functionality is inherently integrated into the WPT system, allowing simultaneous power transfer and localization through the same magnetic interface. Experimental validation is conducted on a laboratory-scale prototype under varying lateral offsets and air-gap conditions. In addition, spline-based interpolation is employed to increase spatial data density for training. The results demonstrate that the proposed framework can capture spatial variations associated with both lateral and vertical displacement, providing reliable position estimation under hovering conditions. This work establishes a hardware-efficient, sensorless solution for UAV wireless charging and serves as a baseline for advanced data-driven position estimation methods in dynamic WPT systems.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2021

Few-shot Learning for Post-disaster Structure Damage Assessment

Automating post-disaster damage assessment with remote sensing data is critical for faster surveys of structures impacted by natural disasters. One significant obstacle to training state-of-the-art deep neural networks to support this automation is that large quantities of labelled data are often required. However, obtaining those labels is particularly unrealistic to support post-disaster damage assessment in a timely manner. Few-shot learning methods could help to mitigate this by reducing the amount of labelled data required to successfully train a model while achieving satisfactory results. To this end, we explore a feature reweighting method to the YOLOv3 object detection architecture to achieve few-shot learning of damage assessment models on the xBD dataset. Our results show that the feature reweighting approach yield improved mAP over the baseline with significantly fewer labelled samples. In addition, we use t-SNE to analyze the class-specific reweighting vectors generated by the reweighting module in order to evaluate their inter-class and intra-class similarity. We find that the vectors form clusters based on class, and that these clusters overlap with visually similar classes. Those results show the potential to employ this few-shot learning strategy for rapid damage assessment with post-event remote sensing images.

Bowman, Jordan↗
DOE OSTI2026

Scalability Analysis of Quantum Models for Stress and Emotion Detection

Stress and emotion detection from high-dimensional physiological signals is a challenging task, particularly when aiming for accurate classification across diverse behavioral states. Quantum machine learning (QML) is promising for modeling such high-dimensional data, but scalability is limited by qubit resources and the exponential cost of classical statevector simulation. This work studies the scalability of quantum support vector machines (QSVMs) for binary stress detection and three-class emotion recognition (Negative/Neutral/Positive) under varying qubit counts and angle-encoding strategies. We also present a comparison study with one-feature-per-qubit (1:1) and two-features-per-qubit (2:1) mappings. Experiments are executed on HPC infrastructure using NVIDIA CUDA-Q to evaluate performance, variance, and class-dependent separability at higher-qubit setups. Results show that larger Hilbert spaces can improve peak accuracy but may increase instability. At the same time, dense 2:1 encoding yields more consistent stress detection performance. For emotion recognition, scaling improves discrimination for classes like Negative and Positive more than Neutral. We find that effective QML scaling is task-dependent and benefits more from encoding design than simply increasing qubit count.

Onim, Md. Saif Hassan [University of Tennessee, Kn↗
DOE OSTI2022

Manifold Learning-Based Polynomial Chaos Expansions for High-Dimensional Surrogate Models

In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantities of interest of the computational or analytical model. For this purpose, we employ Grassmannian diffusion maps, a two-step nonlinear dimension reduction technique which allows us to reduce the dimensionality of the data and identify meaningful geometric descriptions in a parsimonious and inexpensive manner. Polynomial chaos expansion is then used to construct a mapping between the stochastic input parameters and the diffusion coordinates of the reduced space. An adaptive clustering technique is proposed to identify an optimal number of clusters of points in the latent space. The similarity of points allows us to construct a number of geometric harmonic emulators which are finally utilized as a set of inexpensive pretrained models to perform an inverse map of realizations of latent features to the ambient space and thus perform accurate out-of-sample predictions. Thus, the proposed method acts as an encoder-decoder system which is able to automatically handle very high-dimensional data while simultaneously operating successfully in the small-data regime. The method is demonstrated on two benchmark problems and on a system of advection-diffusion-reaction equations which model a first-order chemical reaction between two species. In all test cases, the proposed method is able to achieve highly accurate approximations which ultimately lead to the significant acceleration of UQ tasks.

42 ENGINEERING↗
NASA NTRS1972

An interdisciplinary analysis of ERTS data for Colorado mountain environments using ADP techniques

There are no author-identified significant results in this report. Research efforts have been placed on: (1) location, acquisition, and preparation of baseline information necessary for the computer analysis, and (2) refinement of techniques for analysis of MSS data obtained from ERTS-1. Analysis of the first frame of data collected by the ERTS-1 multispectral scanner system over the Lake Texoma area has proven very valuable for determining the best procedures to follow in working with and analyzing ERTS data. Progress on the following projects is described: (1) cover type mapping; (2) geomorphology; and hydrologic feature surveys.

Hoffer, R. M.↗
NASA NTRS1973

Satellite geological and geophysical remote sensing of Iceland

The author has identified the following significant results. Rhyolitic rocks in the Torfajokull geothermal area and geothermally altered ground in the Torfajokull and Reykjanes geothermal areas can also be delineated. The fallout pattern of tephra from the 1970 eruption from the volcano Hekla can be mapped where sufficient depth of deposition destroyed the vegetation. Standard NDPF 70mm negatives (3rd generation) were used to print enlargements of the volcanic islands of Surtsey and Heimaey. Sufficient resolution is maintained to directly update maps to the 1:100,000 scale. Low-sun angle imagery ( 10 deg) of Iceland has been particularly valuable in mapping new structural and volcanic features hidden beneath glacialic ice in Iceland's neovolcanic zone. ERTS-1 imagery has shown the marked differences in volcanic landforms in Iceland from a regional viewpoint. The regional distribution of crater rows, moberg ridges, table (moberg) mountains, calderas, central volcanoes, and shield volcanoes shows striking differences in volcanic geomorphology in different parts of Iceland's neovolcanic zone.

Williams, R. S., Jr.↗
NASA NTRS1975

A decametric wavelength radio telescope for interplanetary scintillation observations

A phased array, electrically steerable radio telescope (with a total collecting area of 18 acres), constructed for the purpose of remotely sensing electron density irregularity structure in the solar wind, is presented. The radio telescope is able to locate, map, and track large scale features of the solar wind, such as streams and blast waves, by monitoring a large grid of natural radio sources subject to rapid intensity fluctuation (interplanetary scintillation) caused by the irregularity structure. Observations verify the performance of the array, the receiver, and the scintillation signal processing circuitry of the telescope.

Cronyn, W. M.↗