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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Experimental platforms for investigating feature-driven jets for HED mix model validation

High-energy-density (HED) systems, such as inertial confinement fusion (ICF), are susceptible to hydrodynamic instabilities that can significantly affect both experimental results and modeling predictions. Isolated features, such as fill tubes or divots in the capsule, can cause material to jet as a result of the compressive shock exciting the Richtmyer–Meshkov instability, and serve as one of the primary degradation mechanisms in ICF yield. Simulations of feature-driven jets and how they mix require extensive experimental validation, particularly for understanding to what degree the initial size and shape of a feature influence jet dynamics, and how much instability feeds through downstream layers. A better understanding of feature-driven jetting can improve our mix modeling capabilities and increase hydrodynamic simulation accuracy. This manuscript describes a series of experimental platforms fielded by Los Alamos National Laboratory as a part of the Mshock Omega 60 and ModCons Omega EP campaigns to explore feature-driven jetting. These platforms are designed to benchmark jet evolution and growth as a function of initial feature size and shape, investigate jet-layer interactions leading to instability feedthrough, and will be used to characterize jet-jet interactions resulting from clusters of features. In conclusion, preliminary results for both platforms are shown. The ModCons experiments are on-going, and a discussion of future work directions is included.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

A bioinspired approach for adaptive solid-solid phase change material coatings with optimized surface features for passive thermal regulation

The necessity to reduce global energy consumption calls for innovative strategies in building thermal management. Passive thermal regulation, particularly through bio-inspired designs, offers a promising avenue by mimicking nature's efficient control of optical properties. This research introduces a novel, climate-responsive coating that integrates optimized bio-inspired surface features with a solid-solid phase change material (SS-PCM) to dynamically manage solar absorptivity without adding additional thickness, enabling both heating and cooling as needed. Drawing on the photonic architectures of the Saharan silver ant and Morpho Didius butterfly, we employed a modeling and multi-objective optimization framework to tailor these surface features. Simulations reveal that surface texture, rather than the intrinsic phase transition of the SS PCM, dominates optical control. Relative to a flat SS PCM coating, optimized isotropic random roughness and broader range features yielded the highest passive heating power increase of about 144 % and 319 % respectively suitable for cold climates. Saharan ant-inspired features enhanced passive cooling for hot climates, achieving a 21.8 % improvement. For moderate climates, Butterfly-wing-inspired surface features provided a balanced enhancement of 19 % for heating and 7 % for cooling. Across all cases, the optimized surface features reduced combined heating and cooling energy demand more effectively than the baseline coating, while preserving material thickness. These findings demonstrate that climate-adaptive, optimized bio-inspired surface features can unlock the full potential of SS PCM coatings, providing a versatile pathway to significant energy savings in buildings and other applications. The methodology establishes a framework for designing next-generation adaptive envelopes that leverage natural photonic principles for high-impact, low-cost thermal regulation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Basin-Scale Structural Features Database

The Basin-Scale Structural Features database provides spatial datasets of faults, fractures, folds, and earthquakes compiled from public, authoritative sources (e.g., U.S. Geological Survey and State Geological Surveys) and aggregated into derivative forms to support subsurface assessments. Recognizing that characterizing basin-scale structural features requires interpreting data that are often ambiguous or lack key information, the source data were evaluated using a knowledge-data framework and geospatial fuzzy logic method (Justman et al., 2020) to represent both measured (observed) and predicted (inferred or potential) structural features as derivative datasets. This workflow employs conceptual models for known structural features and predicted structural features, incorporating geospatial data to estimate potential, even with limited data. The aim is to aid and support an understanding of basin-scale features and identify potential gaps in data and knowledge. As of 4/30/2025, the database includes resources for nine sedimentary basins: Appalachian, Denver, U.S. Gulf Coast, Illinois, Michigan, Permian, Sacramento, San Joquin and Williston. The database is organized by basin and then data category: 1) Faults, fractures, folds, 2) Earthquakes, 3) Topographic, 4) Structural contours and isopachs, 5) Geophysical, and 6) Structural feature density assessment maps.

basin scale↗
NASA NTRS2010

Testing of Haar-Like Feature in Region of Interest Detection for Automated Target Recognition (ATR) System

The objectives of this project were to develop a ROI (Region of Interest) detector using Haar-like feature similar to the face detection in Intel's OpenCV library, implement it in Matlab code, and test the performance of the new ROI detector against the existing ROI detector that uses Optimal Trade-off Maximum Average Correlation Height filter (OTMACH). The ROI detector included 3 parts: 1, Automated Haar-like feature selection in finding a small set of the most relevant Haar-like features for detecting ROIs that contained a target. 2, Having the small set of Haar-like features from the last step, a neural network needed to be trained to recognize ROIs with targets by taking the Haar-like features as inputs. 3, using the trained neural network from the last step, a filtering method needed to be developed to process the neural network responses into a small set of regions of interests. This needed to be coded in Matlab. All the 3 parts needed to be coded in Matlab. The parameters in the detector needed to be trained by machine learning and tested with specific datasets. Since OpenCV library and Haar-like feature were not available in Matlab, the Haar-like feature calculation needed to be implemented in Matlab. The codes for Adaptive Boosting and max/min filters in Matlab could to be found from the Internet but needed to be integrated to serve the purpose of this project. The performance of the new detector was tested by comparing the accuracy and the speed of the new detector against the existing OTMACH detector. The speed was referred as the average speed to find the regions of interests in an image. The accuracy was measured by the number of false positives (false alarms) at the same detection rate between the two detectors.

neural network↗
DOE OSTI2025

Mining Product Reviews for Important Product Features of Refurbished iPhones

Problem: Remanufacturers want to increase consumer interest in refurbished products, which motivates the need to understand which product features are important to buyers of refurbished products such as mobile phones. Research Questions: This study addresses two questions. First, which product features are most important for buyers of refurbished iPhones? Second, how do those preferences differ from the preferences of buyers of new iPhones? Methods: Online reviews of iPhones are obtained and converted into a document–term matrix. Using this text model, three subsets of features are identified using statistical analysis of frequency of mention: most frequent, average, and least frequent. A logistic regression (LR) model is then used to identify which features are most predictive of whether a review is for a new or refurbished phone. Results: Buyers of refurbished phones mention battery health, screen/display, shell condition, and brand significantly more often than other features. Directly contrasting reviews of refurbished versus new phones shows that shell condition, brand, speaker, and charger are found to be the most predictive product features indicated in reviews for refurbished phones. Of those, the shell condition is significantly more predictive than the others. Implications: The results identify product features that remanufacturers of iPhones can emphasize to increase customer demand.

Anisi, Atefeh↗
NASA NTRS1972

Vegetation-terrain feature relationships in southeast Arizona

There are no author-identified significant results in this report. Studies of relationships of vegetation distribution to geomorphic characteristics of the landscape and of plant phenological patterns to vegetation identification of satellite imagery indicate that there exists positive relationships between certain plant species and certain terrain features. Not all species were found to exhibit positive relationships with all terrain feature variables, but enough positive relationships seem to exist to indicate that terrain feature variable-vegetation relationship studies have a definite place in plant ecological investigations. Even more importantly, the vegetation groups examined appeared to be successfully discriminated by the terrain feature variables. This would seem to indicate that spatial interpretations of vegetation groups may be possible. While vegetational distributions aren't determined by terrain feature differences, terrain features do mirror factors which directly influence vegetational response and hence distribution. As a result, those environmental features which can be readily and rapidly ascertained on relatively small-scale imagery may prove to be valuable indicators of vegetation distribution.

Schrumpf, B. J.↗
NASA NTRS1979

Efficient feature subset selection with probabilistic distance criteria

Recursive expressions are derived for efficiently computing the commonly used probabilistic distance measures as a change in the criteria both when a feature is added to and when a feature is deleted from the current feature subset. A combinatorial algorithm for generating all possible r feature combinations from a given set of s features in (s/r) steps with a change of a single feature at each step is presented. These expressions can also be used for both forward and backward sequential feature selection.

Chittineni, C. B.↗
NASA NTRS1984

Io's sodium directional features - Evidence for a magnetospheric-wind-driven gas escape mechanism

Elongated features in Io's sodium cloud, directed away from Jupiter and inclined both to the north and to the south of the satellite's orbital plane, have been observed. The north/south directions of the features are correlated with Io's magnetic longitude, suggesting a formation mechanism involving the oscillating plasma torus. It is shown by means of a model analysis that the features can result from a source of high-velocity (about 20 km/s) sodium combined with the oscillating neutral sodium sink provided by the plasma. The phase relationship between the features' directions and Io's magnetic longitude can be understood if escaping sodium is initially directed at near right angles to Io's orbital motion. The directionality of the features requires that the sodium flux from equatorial regions be higher than that from the poles. The initial directions and speeds of sodium atoms escaping Io to form the directional features can be understood in terms of a magnetospheric-wind-driven escape mechanism. The one sequence of directional feature observations that has been analyzed in detail implies a high-speed sodium source rate of about 10 to the 26th atoms/s.

Pilcher, C. B.↗
NASA NTRS1985

Spectroscopy of the 3 micron emission features

High-spectral-resolution observations of the 3.3 and 3.4 microns features in the three planetary nebulae NGC 7027, IC 418, and BD +30 deg 3639, in the H II region S106, and in the 'red rectangle' HD 44179 are presented. The profile of the unidentified 3.3 microns emission feature is similar in all five sources. The unidentified feature previously referred to as the 3.4 microns feature actually consists of two components, a low-level emission from 3.35 to 3.60 microns and a narrow emission peak at 3.40 microns. The strength of the latter feature relative to that of the 3.3 microns feature varies by a a factor of three from source to source. The origin and properties of these features may be explained by further development of the small-grain models of Sellgren (1984) and Leger and Puget (1984).

Geballe, T. R.↗
NASA NTRS1988

Spectral feature design in high dimensional multispectral data

The High resolution Imaging Spectrometer (HIRIS) is designed to acquire images simultaneously in 192 spectral bands in the 0.4 to 2.5 micrometers wavelength region. It will make possible the collection of essentially continuous reflectance spectra at a spectral resolution sufficient to extract significantly enhanced amounts of information from return signals as compared to existing systems. The advantages of such high dimensional data come at a cost of increased system and data complexity. For example, since the finer the spectral resolution, the higher the data rate, it becomes impractical to design the sensor to be operated continuously. It is essential to find new ways to preprocess the data which reduce the data rate while at the same time maintaining the information content of the high dimensional signal produced. Four spectral feature design techniques are developed from the Weighted Karhunen-Loeve Transforms: (1) non-overlapping band feature selection algorithm; (2) overlapping band feature selection algorithm; (3) Walsh function approach; and (4) infinite clipped optimal function approach. The infinite clipped optimal function approach is chosen since the features are easiest to find and their classification performance is the best. After the preprocessed data has been received at the ground station, canonical analysis is further used to find the best set of features under the criterion that maximal class separability is achieved. Both 100 dimensional vegetation data and 200 dimensional soil data were used to test the spectral feature design system. It was shown that the infinite clipped versions of the first 16 optimal features had excellent classification performance. The overall probability of correct classification is over 90 percent while providing for a reduced downlink data rate by a factor of 10.

Chen, Chih-Chien Thomas↗
NASA NTRS1989

Cloud field classification based on textural features

An essential component in global climate research is accurate cloud cover and type determination. Of the two approaches to texture-based classification (statistical and textural), only the former is effective in the classification of natural scenes such as land, ocean, and atmosphere. In the statistical approach that was adopted, parameters characterizing the stochastic properties of the spatial distribution of grey levels in an image are estimated and then used as features for cloud classification. Two types of textural measures were used. One is based on the distribution of the grey level difference vector (GLDV), and the other on a set of textural features derived from the MaxMin cooccurrence matrix (MMCM). The GLDV method looks at the difference D of grey levels at pixels separated by a horizontal distance d and computes several statistics based on this distribution. These are then used as features in subsequent classification. The MaxMin tectural features on the other hand are based on the MMCM, a matrix whose (I,J)th entry give the relative frequency of occurrences of the grey level pair (I,J) that are consecutive and thresholded local extremes separated by a given pixel distance d. Textural measures are then computed based on this matrix in much the same manner as is done in texture computation using the grey level cooccurrence matrix. The database consists of 37 cloud field scenes from LANDSAT imagery using a near IR visible channel. The classification algorithm used is the well known Stepwise Discriminant Analysis. The overall accuracy was estimated by the percentage or correct classifications in each case. It turns out that both types of classifiers, at their best combination of features, and at any given spatial resolution give approximately the same classification accuracy. A neural network based classifier with a feed forward architecture and a back propagation training algorithm is used to increase the classification accuracy, using these two classes of features. Preliminary results based on the GLDV textural features alone look promising.

Sengupta, Sailes Kumar↗
NASA NTRS1989

Spatial variations of the 3 micron emission features within nebulae

The 3 micron spectra is presented for the Orion bar region and the Red Rectangle. In both objects spectra were obtained at more than one location, corresponding to different distances from the excitation source. The well known 3.3 and 3.4 micron emission bands are seen in both objects as well as the recently discovered features at 3.46, 3.51, and 3.57 microns in the Orion bar spectra. The spectra show that the relative strengths of the 3 micron emission features vary within the Orion bar. As distance from the exciting star increases, the 3.4 and 3.51 micron features increase, and the 3.46 micron feature decreases in strength, relative to the strong 3.3 micron feature. These are two possible interpretations which are postulated, each of which involves the breaking of bonds by UV radiation, which removes the modes responsible for the 3.4 micron emission near the star. The two possible bond ruptures are the CH bond in small polycyclic aromatic hydrocarbons (PAHs), or the bond to an aliphatic subgroup. It has to be pointed out that neither interpretation appears entirely satisfactory. The vibrational overtone interpretation cannot explain the presence or behavior of the 3.46 micron feature, whereas the laboratory spectra of aliphatic sidegroups contain many more features in the 3 micron region than are observed in the astronomical sources.

Moorhouse, Alan↗
NASA NTRS1991

IRAS low-resolution spectral observations of the 10 and 18 micron silicate emission feature

The IRAS LRS Atlas contains 8-23 micron spectra of 1816 stars with silicate emission features. In order to study the shapes of the silicate dust features, the 117 stars whose emission features appear to be optically thin and which have the best signal-to-noise ratio at the longest wavelengths were analyzed. Simple spherical dust shell models were calculated in both the optically thin and the slightly optical thick approximations. From the comparison of the predicted spectra of the dust and stellar continuum, the emissivity function Kappa(lambda) was derived. In the different stars, the shape of the 10-micron feature is either narrow or broad and it is peaked either at about 9.7 microns or at 10 microns. Either particle size effects (particles above 0.75 microns in radius) or optical depth effects (central optical depth about 1) could broaden the 10-micron feature. Chemical composition differences no doubt are also important, particularly as regards the position of the peak of the 10-micron feature. The stars with the peak at 10 microns are more closely confined to the Galactic plane than the stars with the peak at 9.7 microns. The shape of the 18-micron feature is essentially the same in all stars, and can be used to extend the interstellar extinction curve past 13 to 22 microns.

Simpson, Janet P.↗
NASA NTRS1993

Feature extraction and classification algorithms for high dimensional data

Feature extraction and classification algorithms for high dimensional data are investigated. Developments with regard to sensors for Earth observation are moving in the direction of providing much higher dimensional multispectral imagery than is now possible. In analyzing such high dimensional data, processing time becomes an important factor. With large increases in dimensionality and the number of classes, processing time will increase significantly. To address this problem, a multistage classification scheme is proposed which reduces the processing time substantially by eliminating unlikely classes from further consideration at each stage. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Next an approach to feature extraction for classification is proposed based directly on the decision boundaries. It is shown that all the features needed for classification can be extracted from decision boundaries. A characteristic of the proposed method arises by noting that only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is introduced. The proposed feature extraction algorithm has several desirable properties: it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal means or equal covariances as some previous algorithms do. In addition, the decision boundary feature extraction algorithm can be used both for parametric and non-parametric classifiers. Finally, some problems encountered in analyzing high dimensional data are studied and possible solutions are proposed. First, the increased importance of the second order statistics in analyzing high dimensional data is recognized. By investigating the characteristics of high dimensional data, the reason why the second order statistics must be taken into account in high dimensional data is suggested. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics. A method to visualize statistics using a color code is proposed. By representing statistics using color coding, one can easily extract and compare the first and the second statistics.

Lee, Chulhee↗
NASA NTRS2002

Discovery of Cyclotron Resonance Features in the Soft Gamma Repeater SGR 1806-20

We report evidence for cyclotron resonance features from the Soft Gamma Repeater SCR 1806-20 in outburst, detected with the Rossi X-ray Timing Explorer in the spectrum of a long, complex precursor that preceded a strong burst. The features consist of a narrow 5.0 keV absorption line with modulation near its second and third harmonics (at 11.2 keV and 17.5 keV respectively). The line features are transient and are detected in the harder part of the precursor. The 5.0 keV feature is strong, with an equivalent width of approx. 500 eV, and a narrow width of < 0.4 keV. Interpreting the features as electron cyclotron lines in the context of accretion models leads to a large mass-radius ratio (M/R > 0.3 Solar Mass/km) that is inconsistent with neutron stars, or requires a low (5 - 7) x 10(exp 11) G magnetic field that is unlikely for SGRs. The line widths are also narrow compared to those of electron cyclotron resonances observed so far in X-ray pulsars. In the magnetar picture, the features are plausibly explained as ion cyclotron resonances in an ultra-strong magnetic field, which have recently been predicted from magnetar candidates. In this view, the 5.0 keV feature is consistent with a proton cyclotron fundamental whose energy and width are close to model predictions. The line energy would correspond to a surface magnetic field of 1.0 x 10(exp 15) G for SGR 1806-20, in good agreement with that inferred from the spin-down measure in the source.

Ibrahim, A. I.↗
NASA NTRS2002

Discovery of Cyclotron Resonance Features in the Soft Gamma Repeater SGR 1806-20

We report evidence of cyclotron resonance features from the Soft Gamma Repeater SGR 1806-20 in outburst, detected with the Rossi X-ray Timing Explorer in the spectrum of a long, complex precursor that preceded a strong burst. The features consist of a narrow 5.0 keV absorption line with modulation near its second and third harmonics (at 11.2 keV and 17.5 keV respectively). The line features are transient and are detected in the harder part of the precursor. The 5.0 keV feature is strong, with an equivalent width of approx. 500 eV and a narrow width of less than 0.4 keV. Interpreting the features as electron cyclotron lines in the context of accretion models leads to a large mass-radius ratio (M/R greater than 0.3 solar mass/km) that is inconsistent with neutron stars or that requires a low (5-7) x 10(exp 11) G magnetic field that is unlikely for SGRs. The line widths are also narrow compared with those of electron cyclotron resonances observed so far in X-ray pulsars. In the magnetar picture, the features are plausibly explained as ion cyclotron resonances in an ultra-strong magnetic field that have recently been predicted from magnetar candidates. In this view, the 5.0 keV feature is consistent with a proton cyclotron fundamental whose energy and width are close to model predictions. The line energy would correspond to a surface magnetic field of 1.0 x 10(exp 15) G for SGR 1806-20, in good agreement with that inferred from the spin-down measure in the source.

Ibrahim, Alaa I.↗
NASA NTRS2004

Three Years of TRMM Precipitation Features: Radar, Radiometric, and Lightning Characteristics - Part 1

During its first three years, the Tropical Rainfall Measuring Mission (TRMM) satellite observed nearly six million precipitation features. The population of precipitation features is sorted by lightning flash rate, minimum brightness temperature, maximum radar reflectivity, areal extent, and volumetric rainfall. For each of these characteristics, essentially describing the convective intensity or the size of the features, the population is broken into categories consisting of the top 0.001%, top 0.01%, top 0.1%, top 1%, top 2.4%, and remaining 97.6%. The set of 'weakest / smallest' features comprises 97.6% of the population because that fraction does not have detected lightning, with a minimum detectable flash rate 0.7 fl/min. The greatest observed flash rate is 1351 fl/min; the lowest brightness temperatures are 42 K (85-GHz) and 69 K (37- GHz). The largest precipitation feature covers 335,000 sq km and the greatest rainfall from an individual precipitation feature exceeds 2 x 10(exp 12) kg of water. There is considerable overlap between the greatest storms according to different measures of convective intensity. The largest storms are mostly independent of the most intense storms. The set of storms producing the most rainfall is a convolution of the largest and the most intense storms. This analysis is a composite of the global tropics and subtropics. Significant variability is known to exist between locations, seasons, and meteorological regimes. Such variability will be examined in Part II. In Part I, only a crude land / Ocean separation is made. The known differences in bulk lightning flash rates over land and Ocean result from at least two differences in the precipitation feature population: the frequency of occurrence of intense storms, and the magnitude of those intense storms that do occur. Even when restricted to storms with the same brightness temperature, same size, or same radar reflectivity aloft, the storms over water are considerably less likely to produce lightning than are comparable storms over land.

Cecil, Daniel J.↗
NASA NTRS2005

Three Years of TRMM Precipitation Features: Radar, Radiometric, and Lightning Characteristics - Part 1

During its first three years, the Tropical Rainfall Measuring Mission (TRMM) satellite observed nearly six million precipitation features. The population of precipitation features is sorted by lightning flash rate, minimum brightness temperature, maximum radar reflectivity. areal extent, and volumetric rainfall. For each of these characteristics, essentially describing the convective intensity or the size of the features, the population is broken into categories consisting of the top 0.001%, top 0.01%, top 0.1%, top 1%, top 2.4%. and remaining 97.6%. The set of weakest/smallest features composes 97.6% of the population because that fraction does not have detected lightning, with a minimum detectable flash rate of 0.7 flashes (fl) per minute. The greatest observed flash rate is 1351 fl per minute; the lowest brightness temperatures are 42 K (85 GHz) and 69 K (37 GHz). The largest precipitation feature covers 335 000 square kilometers and the greatest rainfall from an individual precipitation feature exceeds 2 x 10 kg per hour of water. There is considerable overlap between the greatest storms according to different measures of convective intensity. The largest storms are mostly independent of the most intense storms. The set of storms producing the most rainfall is a convolution of the largest and the most intense storms. This analysis is a composite of the global Tropics and subtropics. Significant variability is known to exist between locations. seasons, and meteorological regimes. Such variability will be examined in Part II. In Part I, only a crude land-ocean separation is made. The known differences in bulk lightning flash rates over land and ocean result from at least two differences in the precipitation feature population: the frequency of occurrence of intense storms and the magnitude of those intense storms that do occur. Even when restricted to storms with the same brightness temperature, same size, or same radar reflectivity aloft, the storms over water are considerably less likely to produce lightning than are comparable storms over land.

Cecil, Daniel J.↗