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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Using AGNESS (A Generalized Network-based Expert System Shell) for matching images

The image correspondence problem is considered the most difficult step in both stereo and motion analysis. Stereo vision is useful in determining the 3-D positions of points on visible surface in a scene. Motion analysis is useful in determining the spatial and temporal relationships of objects in an environment. Besides stereo and motion analysis, there is the image correspondence problem. Most of this work is based on point or local area properties of the observed gray level values in 2-D images. A global and general approach to this problem is described by using a knowledge-based system. The knowledge it uses consists of both physical properties and spatial relationships of the edges and regions extracted from the given images. The physical component depends on features of the edge or region) in isolation. The spatial component involves the set of edges and regions adjacent to a given edge (or region) of the first image and the set of edges and regions adjacent to each potentially matching edge (or region) of the second image; thus the spatial context of each edge or region is considered. A computational network is used to represent this knowledge, it allows the computation of the likelihood of matching two edges or regions with logical and heuristic operators. An expert system shell called AGNESS (A Generalized Network-based Expert System Shell) is used to build a prototype system.

Pong, Ting-Chuen↗
NASA NTRS1993

Linearization of manipulator dynamics using spatial operators

Linearized dynamics models for manipulators are useful in robot analysis, motion planning, and control applications. Techniques from the spatial operator algebra are used to obtain closed form operator expressions for two types of linearized dynamics models, the linearized inverse and forward dynamics models. Spatially recursive algorithms of O(n) and O(n-squared) complexity for the computation of the perturbation vector and coefficient matrices for the linearized inverse dynamics model are developed first. Subsequently, operator factorization and inversion identities are used to develop corresponding closed-form expressions for the linearized forward dynamics model (LFDM). Once again, these are used to develop algorithms of O(n) and O(n-squared) complexity for the computation of the perturbation vector and the coefficient matrices. The algorithms for the LFDM do not require the explicit computation of the mass matrix nor its numerical inversion and are also of lower complexity than the conventional O(n-cubed) algorithms.

Jain, A.↗
NASA NTRS1990

Spatial Resolution and Detectability Limits in Thin-Film X-Ray Microanalysis

The major advantages of performing x-ray microanalysis in the analytical electron microscope (AEM) are the high compositional spatial resolution and the elemental analysis sensitivity. Unfortunately, there is usually a trade-off between these two advantages. This paper discusses the factors involved in the optimization of both spatial resolution and sensitivity during x-ray microanalysis and shows the results of such optimization experiments for several AEM instruments.

Goldstein, J. I.↗
DOE OSTI2023

Change Detection Applications in the Earth Sciences Using UAS-Based Sensing: A Review and Future Opportunities

Over the past decade, advancements in collection platforms such as unoccupied aerial systems (UAS), survey-grade GNSS, sensor packages, processing software, and spatial analytical tools have facilitated change detection analyses at an unprecedented resolution over broader spatial and temporal extents and in environments where such investigations present challenges. These technological improvements, coupled with the accessibility and versatility of UAS technology, have pushed the boundaries of spatial and temporal scales in geomorphic change detection. As a result, the cm-scale analysis of topographic signatures can detect and quantify surface anomalies during geomorphic evolution. This review focuses on the use of UAS photogrammetry for fine spatial (cm) and temporal (hours to days) scale geomorphic analyses, and it highlights analytical approaches to detect and quantify surface processes that were previously elusive. The review provides insight into topographic change characterization with precise spatial validations applied to landscape processes in various fields, such as the cryosphere and geosphere, as well as anthropogenic earth processes and national security applications. This work sheds light on previously unexplored aspects of both natural and human-engineered environments, demonstrating the potential of UAS observations in change detection. Our discussion examines the emerging horizons of UAS-based change detection, including machine learning and LIDAR systems. In addition, our meta-analysis of spatial and temporal UAS-based observations highlights the new fine-scale niche of UAS-photogrammetry. This scale advancement sets a new frontier in change detection, offering exciting possibilities for the future of land surface analysis and environmental monitoring in the field of Earth Science.

Andresen, Christian G.↗
NASA NTRS1984

Radiometric analyses of Landsat-4 digital image data

The results of analyses of the radiometric characteristics of digital image data produced by the Landsat 4 MSS and TM sensors are reviewed. The MSS analysis found high quality data comparable to the products of previous Landsats, except for a low-level coherent noise effect having a magnitude of about 0.5 counts in each band and a spatial period of about 3.6 pixels. The TM analysis found excellent spatial resolution, generally high data quality and expected scan-angle effects. Procedures for equalizing detector responses appear to be working as intended. Two low-amplitude artifacts also were discovered. The first is associated with the direction of scan (TM employs bidirectional scanning). The second, a very low employs bidirectional scanning). The second, a very low frequency level-shift artifact which produces image banding that is most pronounced in TM1, could be a serious problem for water-related applications or others where average signal levels are low. Correction procedures are under investigation.

Malila, W. A.↗
NASA NTRS1986

Embedded mesh solution of the 2-D Euler equations - Evaluation of interface formulations

Solution of the steady 2-D Euler equations using mesh embedding, or local grid refinement, with a cell-centered finite volume scheme is investigated. Embedded regions which are topologically similar to the global grid are considered. An isoenergetic model for the governing equations is used in Jameson's finite volume multistage scheme with modifications to the boundary conditions and smoothing. A detailed study of the embedding interface flux and smoothing formulations is conducted. Taylor expansion analysis reveals that local second order spatial accuracy is not possible if a conservative interface flux formulation is used. The analysis also gives constraints for local first order accuracy. An energy stability analysis indicates that downwind weighting of interface fluxes causes local instabilities. Analysis shows that conservative interface smoothing formulations must have a locally convective component, but that correct interface formulations allow globally dissipative smoothing. Embedded mesh solutions obtained with this scheme are presented for a transonic airfoil. They show that if embedding interfaces are close to the shocks, then small modifications in the interface location can have large effects on converge and solution accuracy.

Allmaras, S. R.↗
NASA NTRS1989

Vibrating Beam With Spatially Periodic Stiffness

Report presents theoretical analysis of vibrations of simply supported beam, bending stiffness varying about steady value, sinusoidally with position along length. Problem of practical importance because related to vibrations of twisted-pair electric-power transmission lines. Twists promote nonuniform shedding of vortexes and prevents resonant accumulation of vibrational energy from wind.

Townsend, John S.↗
NASA NTRS1989

Dust around Mira variables: An analysis of IRAS LRS spectra

The spatial extent and spectral appearance of the thin dust shell around Mira variables is determined largely by the dust absorptivity, Q(sub abs)(lambda), and the dust condensation temperature T(sub cond). Both Q(sub abs)(lambda) and T(sub cond) are extracted from IRAS low-resolution spectra (LRS) spectra. In order to do this, the assumption that the ratio of total power in the 10 micron feature to that in the 20 micron feature should be equal to that measured in other amorphous silicates (e.g., synthesized amorphous Mg2SiO4). It was found that T(sub cond) decreases with decreasing strength of the 10 micron feature, from T(sub cond) = 1000 K to 500 K (estimated error 20 percent). A value for the near-infrared dust absorptivity could not be determined. Although this parameter strongly affects the condensation radius, it hardly affects the shape of the LRS spectrum (as long as the optically thin approximation is valid), because it scales the spatial distribution of the dust. Information on the magnitude of the near-infrared dust absorptivity may be deduced from the unique carbon star BM Gem. This star has a LRS spectrum with silicate features indication an inner dust shell temperature of at least 1000 K. However, on the basis of observations in the 1920s-30s one may infer an inner dust shell radius of at least 6x10(exp 12)m. To have this high temperature at such a large distance, the near-infrared absorptivity of the dust must be high.

Slijkhuis, S.↗
NASA NTRS2007

SPOT-VEG Based Analysis of Siberian Silkmoth Outbreak

The spatial and temporal dynamics of an outbreak of the Siberian silkmoth were correlated with topographic features of the affected area using SPOT-VEG data and a high resolution digital elevation model (DEM). In 2002-2003 an outbreak affected approximately 20,000 ha in the South Siberian mountains of Russia. The outbreak began between the elevations of approximately 430- 480 m and on southwest slopes with steepness < 5 degrees. As the pest searched for food it moved up and down slope, resulting in an elevation distribution split within a range of approximately 390-540 m and slope steepness up to 15 degrees. In the final phase the azimuth distribution of damaged stands became even. The correlation between the initial phase and topographic features can be used to prioritize monitoring forest areas most vulnerable to destruction by pests.

Kharuk, Viatcheslav I.↗
NASA NTRS2011

Temporal mapping and analysis

A compositing process for selecting spatial data collected over a period of time, creating temporal data cubes from the spatial data, and processing and/or analyzing the data using temporal mapping algebra functions. In some embodiments, the temporal data cube is creating a masked cube using the data cubes, and computing a composite from the masked cube by using temporal mapping algebra.

O'Hara, Charles G.↗
NASA NTRS1976

Use of LANDSAT imagery for wildlife habitat mapping in northeast and eastcentral Alaska

The author has identified the following significant results. There is strong indication that spatially rare feature classes may be missed in clustering classifications based on 2% random sampling. Therefore, it seems advisable to augment random sampling for cluster analysis with directed sampling of any spatially rare features which are relevant to the analysis.

Lent, P. C.↗
DOE OSTI2020

Hollow fiber mid-ID spectrometer with UV laser ablation sampling for fine spatial reoslution of isotope ratios in solids

We describe a system that combines isotope ratio analysis via mid-infrared (Mid-IR) laser absorption spectroscopy with fine spatial resolution sampling using a UV pulsed laser. The UV laser ablates a pit in a solid on the order of 10 microns in diameter. The sample-derived particulates resulting from laser ablation pass through a micro-combustor, and the resulting gas is analyzed using Mid-IR laser absorption spectroscopy in a capillary absorption spectrometer (CAS). The CAS uses a hollow fiber optic waveguide with a reflective inner coating and a small internal volume on the order of 1ml. The hollow fiber both guides the laser light from source to detector and contains the gas sample at reduced pressure. Near unity overlap between the laser beam and sample enables sensitive analysis with ultra-small sample size. A prototype system has been demonstrated to enable stable carbon isotopic analysis (d13C) with 1 per mil precision using <1 picomole of carbon and is currently being used to study nutrient exchange in soil/root/microbial rhizosphere studies. The smaller sample size of this system is enabling fine spatial resolution analysis (on the order of 10 microns), which is roughly an order of magnitude smaller than was possible with an isotope ratio mass spectrometer (IRMS). In addition to rhizosphere studies, the system can provide a useful tool for fine scale isotope analysis with applications in biological, forensic, and environmental science.

capillary absorption spectroscopy, laser ablation,↗
NASA NTRS2016

Modeling Forest Biomass and Growth: Coupling Long-Term Inventory and Lidar Data

Combining spatially-explicit long-term forest inventory and remotely sensed information from Light Detection and Ranging (LiDAR) datasets through statistical models can be a powerful tool for predicting and mapping above-ground biomass (AGB) at a range of geographic scales. We present and examine a novel modeling approach to improve prediction of AGB and estimate AGB growth using LiDAR data. The proposed model accommodates temporal misalignment between field measurements and remotely sensed data-a problem pervasive in such settings-by including multiple time-indexed measurements at plot locations to estimate AGB growth. We pursue a Bayesian modeling framework that allows for appropriately complex parameter associations and uncertainty propagation through to prediction. Specifically, we identify a space-varying coefficients model to predict and map AGB and its associated growth simultaneously. The proposed model is assessed using LiDAR data acquired from NASA Goddard's LiDAR, Hyper-spectral & Thermal imager and field inventory data from the Penobscot Experimental Forest in Bradley, Maine. The proposed model outperformed the time-invariant counterpart models in predictive performance as indicated by a substantial reduction in root mean squared error. The proposed model adequately accounts for temporal misalignment through the estimation of forest AGB growth and accommodates residual spatial dependence. Results from this analysis suggest that future AGB models informed using remotely sensed data, such as LiDAR, may be improved by adapting traditional modeling frameworks to account for temporal misalignment and spatial dependence using random effects.

Babcock, Chad↗
DOE OSTI2026

Data, model inputs, and analysis scripts associated with a manuscript on stream intermittency controls across spatial scales in Pacific Northwest watersheds

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript "Hydroclimatic Memory and Watershed Template Shape Stream Intermittency: Multi-scale Attribution Using Process-based Simulation and Explainable ML" by Niroula et al. (2026), submitted to Water Resources Research (WRR). The study investigates the dominant controls on stream intermittency across local, reach, and watershed scales using a coupled process-based simulation and explainable machine-learning framework. Long-term daily simulations from the Advanced Terrestrial Simulator (ATS) were used to generate wetness states and ponded-depth responses over river-corridor cells. These ATS outputs were then aggregated across scales and used to train XGBoost (eXtreme Gradient Boosting) models. SHAP (SHapley Additive exPlanations) was applied to quantify the relative importance of hydroclimatic forcings, watershed template attributes, and antecedent-memory effects in shaping intermittency behavior. The analysis is carried out for three contrasting Pacific Northwest watersheds: Oak Creek (OCW), American River Watershed (ARW), and H.J. Andrews (HJA). Across these testbeds, the package contains ATS-ready watershed inputs, ATS run configuration and selected output files, model-evaluation data products, intermittency-analysis datasets, machine-learning target-feature tables, SHAP outputs, and notebooks used to organize, analyze, and visualize results. At a high level, the package documents a workflow in which ATS provides the physically based simulation backbone and explainable machine learning is used as a post-processing attribution tool. The contents are intended to support interpretation of the manuscript figures and results, provide context for how intermittency metrics were generated at multiple scales, and preserve the key artifacts needed to understand and reuse the analysis workflow. The package contains a high-level directory summary file (`summary.txt`) and four main content folders (1) `evaluation_plots` contains evaluation figures and supporting evaluation datasets; (2) `intermittency_plots` contains intermittency-focused analysis notebook and prepared datasets; (3) `ml-training-and-shap_values_plots` contains ML training inputs, SHAP outputs, and figure-generation notebooks; and (4) `watershed_mesh_and_ats_input` contains ATS model setup materials, forcing inputs, geometry, and selected run files. More specifically, the `evaluation_plots` folder contains the notebook used for ATS evaluation plotting and site-specific evaluation datasets. These include evapotranspiration and water-balance products for three watersheds, as well as an Oak Creek field-measurement discharge file. The `intermittency_plots` folder contains the notebook used for intermittency analysis and the prepared datasets used to analyze intermittent and non-intermittent wetness behavior across the study watersheds. The `ml-training-and-shap_values_plots` folder contains notebooks and outputs for the machine-learning and explainability workflow. This includes the main XGBoost and SHAP notebook(s), a beeswarm plotting notebook, target-feature tables for machine-learning training, SHAP summary tables, and per-sample SHAP value archives. The `watershed_mesh_and_ats_input` folder contains ATS-related watershed inputs and supporting materials. This includes mesh and shape products, ATS-readable LAI and meteorological forcing inputs, selected ATS spinup and transient-run files, and a watershed workflow example notebook. Subdirectories are organized by watershed where applicable.All files are .cpg (codepage files), .csv (comma-separated values), .dbf (database files), .exo (Exodus mesh format), .h5 (HDF5 format), .ipynb (Jupyter notebooks), .pkl (Python pickle), .prj (projection files), .sh (shell scripts), .shp (shapefile geometry), .shx (shapefile index), .txt (text files), or .xml (markup data).

Advanced Terrestrial Simulator↗
DOE OSTI2024

Impacts of Spatial Resolution in a High-Fidelity Capacity Expansion Model: An ERCOT Case Study

Capacity expansion models are important tools in examining the evolution of the electric power sector. Embedded in these tools are many modeling choices with consequential impacts on computational burden and associated analysis. In this study, we adjust the spatial resolution of the Regional Energy Deployment System (ReEDS) to understand the implications of higher-fidelity modeling on energy system projections and model solve times. The native ReEDS regions capture the contiguous United States in 134 balancing areas whereas the regions in the higher-resolution version are defined by over 3,000 U.S. counties. Using both resolutions, we conduct a case study of the Texas Interconnection (The Electric Reliability Council of Texas [ERCOT]) to explore differences in model projections and to inform appropriate applications of high spatial resolution in a large-scale, applied capacity expansion model.

county↗
NASA NTRS1980

Landsat analysis of tropical forest succession employing a terrain model

Landsat multispectral scanner (MSS) data have yielded a dual classification of rain forest and shadow in an analysis of a semi-deciduous forest on Mindonoro Island, Philippines. Both a spatial terrain model, using a fifth side polynomial trend surface analysis for quantitatively estimating the general spatial variation in the data set, and a spectral terrain model, based on the MSS data, have been set up. A discriminant analysis, using both sets of data, has suggested that shadowing effects may be due primarily to local variations in the spectral regions and can therefore be compensated for through the decomposition of the spatial variation in both elevation and MSS data.

Barringer, T. H.↗
DOE OSTI2023

Charge decay in the spatial afterglow of plasmas and its impact on diffusion regimes

Abstract The spatial afterglow is a region at the boundary of a non-equilibrium plasma where charged species relax into ambient equilibrium. In many applications, the spatial afterglow is the part of the plasma that interacts with surfaces, such as suspended particles or a material substrate. However, compared to the bulk plasma, there has been little effort devoted to studying the properties of the spatial afterglow, and a fundamental analysis has not yet been developed. Here, we apply double Langmuir probe measurements and develop an advection-diffusion-recombination model to provide a detailed description of charged species in the spatial afterglow over a wide range of pressures, temperatures, plasma dimensions, and flow rates. We find that the density of charged species in the spatial afterglow decays by orders of magnitude, which leads to a transition from ambipolar to free diffusion. These insights can be used to explain or predict experimental observations of phenomena, such as the charging of dust grains and the dose of charged species to a biomaterial.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2013

Application of Spectral Analysis Techniques in the Intercomparison of Aerosol Data: 1. an EOF Approach to the Spatial-Temporal Variability of Aerosol Optical Depth Using Multiple Remote Sensing Data Sets

Many remote sensing techniques and passive sensors have been developed to measure global aerosol properties. While instantaneous comparisons between pixel-level data often reveal quantitative differences, here we use Empirical Orthogonal Function (EOF) analysis, also known as Principal Component Analysis, to demonstrate that satellite-derived aerosol optical depth (AOD) data sets exhibit essentially the same spatial and temporal variability and are thus suitable for large-scale studies. Analysis results show that the first four EOF modes of AOD account for the bulk of the variance and agree well across the four data sets used in this study (i.e., Aqua MODIS, Terra MODIS, MISR, and SeaWiFS). Only SeaWiFS data over land have slightly different EOF patterns. Globally, the first two EOF modes show annual cycles and are mainly related to Sahara dust in the northern hemisphere and biomass burning in the southern hemisphere, respectively. After removing the mean seasonal cycle from the data, major aerosol sources, including biomass burning in South America and dust in West Africa, are revealed in the dominant modes due to the different interannual variability of aerosol emissions. The enhancement of biomass burning associated with El Niño over Indonesia and central South America is also captured with the EOF technique.

variability↗