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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 721 records · Page 40

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NASA NTRS1991

Probing shock models of CH(+) formation

High-resolution, high SNR observations of CH and CH(+) toward Zeta Oph, Xi Per, and P Cyg are used to test several predictions of shock models of molecule formation. The predicted differences between the CH and HCH(+) heliocentric velocities of the magnitude produced by the shock theories are not found in any of the three stars. toward Zeta Oph and Xi Per, no difference is found, based on high-quality data, between the observed CH(+) lie profiles and a Gaussian function which would be expected as a result of shock structure. Gaussian line width parameters of CH(+) lines toward Zeta Oph and Xi Per are broad as predicted by shock models are consistent with previous measurements. These observations yield only weak evidence in favor of the predictions of shock models of molecule formation.

Hawkins, Isabel↗

NASA NTRS1995

Atomic Oxygen Durability Evaluation of Protected Polymers Using Thermal Energy Plasma Systems

The durability evaluation of protected polymers intended for use in low Earth orbit (LEO) has necessitated the use of large-area, high-fluence, atomic oxygen exposure systems. Two thermal energy atomic oxygen exposure systems which are frequently used for such evaluations are radio frequency (RF) plasma ashers and electron cyclotron resonance plasma sources. Plasma source testing practices such as ample preparation, effective fluence prediction, atomic oxygen flux determination, erosion measurement, operational considerations, and erosion yield measurements are presented. Issues which influence the prediction of in-space durability based on ground laboratory thermal energy plasma system testing are also addressed.

Banks, Bruce A.↗

DOE OSTI2022

Predicting solid state material platforms for quantum technologies

Semiconductor materials provide a compelling platform for quantum technologies (QT). However, identifying promising material hosts among the plethora of candidates is a major challenge. Therefore, we have developed a framework for the automated discovery of semiconductor platforms for QT using material informatics and machine learning methods. Different approaches were implemented to label data for training the supervised machine learning (ML) algorithms logistic regression, decision trees, random forests and gradient boosting. We find that an empirical approach relying exclusively on findings from the literature yields a clear separation between predicted suitable and unsuitable candidates. In contrast to expectations from the literature focusing on band gap and ionic character as important properties for QT compatibility, the ML methods highlight features related to symmetry and crystal structure, including bond length, orientation and radial distribution, as influential when predicting a material as suitable for QT.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Uncertainty-aware particle segmentation for electron microscopy at varied length scales

Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale. To study the powder samples that are widely used in materials development, scanning electron microscopes (SEMs) are increasingly used at the laboratory scale to generate large datasets with hundreds of images. Parsing these images to identify distinct particles and determine their morphology requires careful analysis, and automating this process remains challenging. In this work, we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM images. We address several challenges inherent to measurements, such as image blur and particle agglomeration. Moreover, our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a particle. Recognizing that disparate length scales are often present in large datasets, we use this framework to create two models that are separately trained to handle images obtained at low or high magnification. By testing these models on a variety of inorganic samples, our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts, revealing comparable accuracy while requiring a fraction of the time. These findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization.

36 MATERIALS SCIENCE↗

NASA NTRS2013

Buckling Testing and Analysis of Honeycomb Sandwich Panel Arc Segments of a Full-Scale Fairing Barrel: 6-Ply In-Autoclave Facesheets - Part 2

Four honeycomb sandwich panel types, representing 1/16th arc segments of a 10-m diameter barrel section of the Heavy Lift Launch Vehicle (HLLV), were manufactured and tested under the NASA Composites for Exploration program and the NASA Constellation Ares V program. Two configurations were chosen for the panels: 6-ply facesheets with 1.125 in. honeycomb core and 8-ply facesheets with 1.000 in. honeycomb core. Additionally, two separate carbon fiber/epoxy material systems were chosen for the facesheets: in-autoclave IM7/977-3 and out-of-autoclave T40-800b/5320-1. Smaller 3- by 5-ft panels were cut from the 1/16th barrel sections. These panels were tested under compressive loading at the NASA Langley Research Center (LaRC). Furthermore, linear eigenvalue and geometrically nonlinear finite element analyses were performed to predict the compressive response of each 3- by 5-ft panel. This manuscript summarizes the experimental and analytical modeling efforts pertaining to the panels composed of 6-ply, IM7/977-3 facesheets (referred to as Panels B-1 and B-2). To improve the robustness of the geometrically nonlinear finite element model, measured surface imperfections were included in the geometry of the model. Both the linear and nonlinear models yield good qualitative and quantitative predictions. Additionally, it was correctly predicted that the panel would fail in buckling prior to failing in strength. Furthermore, several imperfection studies were performed to investigate the influence of geometric imperfections, fiber angle misalignments, and three-dimensional (3-D) effects on the compressive response of the panel.

Pineda, Evan J.↗

NASA NTRS2019

Buckling Testing and Analysis of Honeycomb Sandwich Panel Arc Segments of a Full-Scale Fairing Barrel Part 4: Six-ply Out-of-Autoclave Facesheets

Four honeycomb sandwich panel types, representing 1/16th arc segments of a 10-m diameter barrel section of the Heavy Lift Launch Vehicle (HLLV), were manufactured and tested under the NASA Composites for Exploration program and the NASA Constellation Ares V program. Two configurations were chosen for the panels: 6-ply facesheets with 1.125 in. honeycomb core and 8-ply facesheets with 1.000 in. honeycomb core. Additionally, two separate carbon fiber/epoxy material systems were chosen for the facesheets: in-autoclave IM7/977-3 and out-of-autoclave T40-800b/5320-1. Smaller 3 ft. by 5 ft. panels were cut from the 1/16th barrel sections. These panels were tested under compressive loading at the NASA Langley Research Center (LaRC). Furthermore, linear eigenvalue and geometrically nonlinear finite element analyses were performed to predict the compressive response of each 3 ft. by 5 ft. panel. This manuscript summarizes the experimental and analytical modeling efforts pertaining to the panels composed of 6-ply, T40-800b/5320-1 facesheets (referred to as Panels D). To improve the robustness of the geometrically nonlinear finite element model, measured surface imperfections were included in the geometry of the model. Both the linear and nonlinear models yield good qualitative and quantitative predictions. Additionally, it was correctly predicted that the panel would fail in buckling prior to failing in strength. Furthermore, three-dimensional (3D) effects on the compressive response of the panel were studied.

David E Myers↗

NASA NTRS1985

Measurements and predictions of the structure of evaporating sprays

In the present experimental and theoretical study of turbulent, evaporating sprays, round, Freon-11 sprays produced by an air-atomizing injector that is directed vertically downward in still air were subjected to structure measurements for mean and fluctuating gas velocities, total Freon-11 concentration, drop size and velocity contributions, mean gas temperature, and liquid flux distributions. An evaluation was then conducted of three spray models: (1) locally homogeneous flow, (2) deterministic separated flow, and (3) stochastic separated flow. The first two of these were found to perform poorly; the stochastic model yielded the best agreement between predictions and measurements.

Solomon, A. S. P.↗

DOE OSTI2025

Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes

Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys (HEAs), especially when integrating computational predictions with sparse experimental observations. This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes (cGP), Deep Gaussian processes (DGP), encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting (XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V. We specifically assess their capabilities in predicting correlated material properties, including yield strength, hardness, modulus, ultimate tensile strength, elongation, and average hardness under dynamic/quasi-static conditions, alongside auxiliary computational properties. The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic, heterotopic, and incomplete data commonly encountered in materials science. Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperform other surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge. This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and data-efficient materials design.

36 MATERIALS SCIENCE↗

NASA NTRS1978

Effect of the reaction HO2 + O3 yields OH + 2O2 on stratospheric ozone

It is noted that recent measurements of large values of the rate coefficient for the reaction NO + HO2 yields NO2 + OH lead to model predictions of excessive amounts of stratospheric ozone. This letter shows that a recent measurement of the rate coefficient (k2) for the reaction HO2 + O3 yields OH + 2O2 largely resolves these problems of excessive stratospheric ozone in models. A two-dimensional model of stratospheric trace constituents is used to calculate the concentrations of 35 constituents; the results are compared with experimental measurements of ozone column densities. It is found that the model predictions of excess ozone abundances diminish significantly when the measured value of k2 is employed.

Whitten, R. C.↗

NASA NTRS2015

A Climate Generator for Agricultural Planning in Southeastern South America

A method is described for the generation of climate scenarios in a form suitable for driving agricultural models. The scenarios are tailored to the region in southeastern South America bounded by 25-40 deg S, 45-65 deg W, denoted here as SESA. SESA has been characterized by increasing summer precipitation, particularly during the late 20th century, which, in the context of favorable market conditions, has enabled increases in agricultural production. Since about year 2000, however, the upward tendency appears to have slowed or possibly stopped, raising questions about future climate inputs to regional agricultural yields. The method is not predictive in the deterministic sense, but rather attempts to characterize uncertainty in near-term future climate, taking into account both forced trends and unforced, natural climate fluctuations. It differs from typical downscaling methods in that GCM information is utilized only at the regional scale, subregional variability being modeled based on the observational record. Output, generated on the monthly time scale, is disaggregated to daily values with a weather generator and used to drive soybean yields in the crop model DSSAT-CSM, for which preliminary results are discussed. The simulations produced permit assessment of the interplay between long-range trends and near-term climate variability in terms of agricultural production.

climate simulation↗

NASA NTRS2014

Buckling Testing and Analysis of Honeycomb Sandwich Panel Arc Segments of a Full-Scale Fairing Barrel: 8-ply Out-of-Autoclave Facesheets - Part 3

Four honeycomb sandwich panels, representing 1/16th arc segments of a 10 m diameter barrel section of the heavy lift launch vehicle, were manufactured under the NASA Composites for Exploration program and the NASA Constellation Ares V program. Two configurations were chosen for the panels: 6-ply facesheets with 1.125 in. honeycomb core and 8-ply facesheets with 1.000 in. honeycomb core. Additionally, two separate carbon fiber/epoxy material systems were chosen for the facesheets: inautoclave IM7/977-3 and out-of-autoclave T40-800B/5320-1. Smaller 3- by 5-ft panels were cut from the 1/16th barrel sections. These panels were tested under compressive loading at the NASA Langley Research Center. Furthermore, linear eigenvalue and geometrically nonlinear finite element analyses were performed to predict the compressive response of the 3- by 5-ft panels. This manuscript summarizes the experimental and analytical modeling efforts pertaining to the panel composed of 8-ply, T40-800B/5320-1 facesheets (referred to as Panel C). To improve the robustness of the geometrically nonlinear finite element model, measured surface imperfections were included in the geometry of the model. Both the linear and nonlinear, two-dimensional (2-D) and three-dimensional (3-D), models yield good qualitative and quantitative predictions. Additionally, it was predicted correctly that the panel would fail in buckling prior to failing in strength.

heavy lift launch vehicles↗

NASA NTRS2020

Data Science for Weather Impacts on Crop Yield

Private businesses in sectors, such as food, energy, and retail, as well as public sector and federal agencies are interested in the predictive understanding of weather impacts on crop yield, which is an important aspect of food security. Scientific literature has mainly examined how crop yield is impacted by growing season-averaged weather indices. Although a few studies did consider weather extremes in their analysis, their scope was either restricted to measuring their conditional relationship with yield or the extreme event types considered were limited. Selection of regression models, whether the more commonly used linear approaches or nonlinear methods, have not been appropriately justified in this context. Here, we develop data-driven methods to examine two inter-related hypotheses for improved scientific understanding and enhanced predictive modeling. The first hypothesis, that extreme weather indices have a statistically significant information content in them is found to be valid based on linear and nonlinear methods for pairwise dependence. The second hypothesis, examines the value addition of nonlinear regression methods, and suggests that linear approaches may not alone be adequate. The results of this study can inform scientific understanding, generation and relevance of indices and end-to-end risk assessment systems in the context of climate impacts on crop yield. An immediate application may be in the context of NASA Earth Exchange (NEX) which facilitates the generation and dissemination of impacts relevant weather data and indices using a multitude of satellite-derived data sets and model outputs.

Data mining, food security, weather impacts↗

DOE OSTI2024

Automated Calibration for Rapid Optical Spectroscopy Sensor Development for Online Monitoring

An automated platform has been developed to assist researchers in the rapid development of optical spectroscopy sensors to quantify species from spectral data. This platform performs calibration and validation measurements simultaneously. Real-time, in situ monitoring of complex systems through optical spectroscopy has been shown to be a useful tool; however, building calibration models requires development time, which can be a limiting factor in the case of radiological or otherwise hazardous systems. While calibration time can be reduced through optimized design of experiments, this study approached the challenge differently through automation. The ATLAS (Automated Transient Learning for Applied Sensors) platform used pneumatic control of stock solutions to cycle flow profiles through desired calibration concentrations for multivariate model construction. Additionally, the transients between desired concentrations based on flow calculations were used as validation measurements to understand model predictive capabilities. This automated approach yielded an incredible 76% reduction in model development time and a 60% reduction in sample volume versus estimated manual sample preparation and static measurements. The ATLAS system was demonstrated on two systems: a three-lanthanide system with Pr/Nd/Ho representing a use case with significant overlap or interference between analyte signatures and an alternate system containing Pr/Nd/Ni to demonstrate a use case in which broad-band corrosion species signatures interfered with more distinct lanthanide absorbance profiles. Both systems resulted in strong model prediction performance (RMSEP < 9%). Lastly, ATLAS was demonstrated as a tool to simulate process monitoring scenarios (e.g., column separation) in which models can be further optimized to account for day-to-day changes as necessary (e.g., baseline correction). Ultimately, ATLAS offers a vital tool to rapidly screen monitoring methods, investigate sensor fusion, and explore more complex systems (i.e., larger numbers of species).

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Genomic prediction of regional-scale performance in switchgrass ( Panicum virgatum ) by accounting for genotype-by-environment variation and yield surrogate traits

Switchgrass is a potential crop for bioenergy or carbon capture schemes, but further yield improvements through selective breeding are needed to encourage commercialization. To identify promising switchgrass germplasm for future breeding efforts, we conducted multisite and multitrait genomic prediction with a diversity panel of 630 genotypes from 4 switchgrass subpopulations (Gulf, Midwest, Coastal, and Texas), which were measured for spaced plant biomass yield across 10 sites. Our study focused on the use of genomic prediction to share information among traits and environments. Specifically, we evaluated the predictive ability of cross-validation (CV) schemes using only genetic data and the training set (cross-validation 1: CV1), a subset of the sites (cross-validation 2: CV2), and/or with 2 yield surrogates (flowering time and fall plant height). We found that genotype-by-environment interactions were largely due to the north–south distribution of sites. The genetic correlations between the yield surrogates and the biomass yield were generally positive (mean height r = 0.85; mean flowering time r = 0.45) and did not vary due to subpopulation or growing region (North, Middle, or South). Genomic prediction models had CV predictive abilities of –0.02 for individuals using only genetic data (CV1), but 0.55, 0.69, 0.76, 0.81, and 0.84 for individuals with biomass performance data from 1, 2, 3, 4, and 5 sites included in the training data (CV2), respectively. To simulate a resource-limited breeding program, we determined the predictive ability of models provided with the following: 1 site observation of flowering time (0.39); 1 site observation of flowering time and fall height (0.51); 1 site observation of fall height (0.52); 1 site observation of biomass (0.55); and 5 site observations of biomass yield (0.84). The ability to share information at a regional scale is very encouraging, but further research is required to accurately translate spaced plant biomass to commercial-scale sward biomass performance.

09 BIOMASS FUELS↗

NASA NTRS1981

Predictability of quasi-geostrophic planetary waves in global and hemispheric domains

Factors which affect the long term predictability, and also the feasibility of defining an ultimate level of weather predictability, are discussed. It is noted that current models yield a one week forecast at the best. Long range predictability, however, is a restriction on the knowledge of evolutive processes of the atmosphere, and does not characterize the prediction of specific atmospheric quantities. The influences of local phenomena such as cyclones and other nonlinear processes can cause planetary waves which affect the predictability of weather everywhere. It is suggested that any successful prediction model will necessarily account for the evolution of ultralong waves. Although significant differences in predictions have been found between hemispheric and global predictions, the location of the errors in extrapolating the hemispheric to the global scale have been identified.

Roads, J. O.↗

NASA NTRS1990

Efficient finite element modeling of laminated composite plates based on higher-order theory

A simple and efficient three-node plate bending element for the analysis of composite laminates is developed from a variational principle. The deformations due to transverse shear and transverse normal effects are accounted for, allowing accurate predictions in the range of thin to thick plates. The methodology incorporates C(exp 0) and C(exp 1) continuous displacement approximations and yields accurate ply-by-ply predictions of all displacement, strain, and stress variables.

Tessler, Alexander↗

DOE OSTI2020

Charge radii of exotic neon and magnesium isotopes

We compute the charge radii and ground-state energies of even-mass neon and magnesium isotopes from neutron number N = 8 to the dripline. Our calculations are based on nucleon-nucleon and three-nucleon potentials from chiral effective field theory that include Δ isobars. These potentials yield an accurate saturation point and symmetry energy of nuclear matter. We use the coupled-cluster method and start from an axially symmetric reference state. Binding energies and two-neutron separation energies largely agree with data, and the dripline in neon is accurate. The computed charge radii are accurate for many isotopes where data exist. Finer details, such as isotope shifts, however, are not accurately reproduced. These chiral potentials indicate a subshell closure at N = 14 for the radii (but not for two-neutron separation energies) and a decrease in charge radii at N = 8 (observed in neon and predicted for magnesium). Furthermore, they yield a continued increase of charge radii as neutrons are added beyond N = 14 yet underestimate the large increase at N = 20 in magnesium.

20 ≤ A ≤ 38↗

NASA NTRS1995

Ion Acoustic Wave Frequencies and Onset Times During Type 3 Solar Radio Bursts

Conflicting interpretations exist for the low-frequency ion acoustic (S) waves often observed by ISEE 3 in association with intense Langmuir (L) waves in the source regions of type III solar radio bursts near 1 AU. Two indirect lines of observational evidence, as well as plasma theory, suggest they are produced by the electrostatic (ES) decay L yields L(PRIME) + S. However, contrary to theoretical predictions, an existing analysis of the wave frequencies instead favors the electromagnetic (EM) decays L yields T + S, where T denotes an EM wave near the plasma frequency. This conflict is addressed here by comparing the observed wave frequencies and onset times with theoretical predictions for the ES and EM decays, calculated using the time-variable electron beam and magnetic field orientation data, rather than the nominal values used previously. Field orientation effects and beam speed variations are shown analytically to produce factor-of-three effects, greater than the difference in wave frequencies predicted for the ES and EM decays; effects of similar magnitude occur in the events analyzed here. The S-wave signals are extracted by hand from a sawtooth noise background, greatly improving the association between S waves and intense L waves. Very good agreement exists between the time-varying predictions for the ES decay and the frequencies of most (but not all) wave bursts. The waves occur only after the ES decay becomes kinematically allowed, which is consistent with the ES decay proceeding and producing most of the observed signals. Good agreement exists between the EM decay's predictions and a significant fraction of the S-wave observations while the EM decay is kinematically allowed. The wave data are not consistent, however, with the EM decay being the dominant nonlinear process. Often the observed waves are sufficiently broadband to overlap simultaneously the frequency ranges predicted for the ES and EM decays. Coupling the dominance of the ES decay with this frequency overlap provides support for a previous suggestion that fundamental emission occurs when the EM decay is stimulated by the ES decay product waves. The periods in which the ES and EM decays produce observable S waves are consistent with the observed and (independently) predicted times of fundamental and harmonic radio emission. This supports interpretation of fundamental emission as stimulated EM decay and harmonic emission as the coalescence L + L(prime) yields T of beam-generated L waves and L(prime) waves produced by the ES decay, where T denotes an electromagnetic wave at twice the plasma frequency. Analysis of the electron beam data reveals that the time-varying beam speed is consistent with ballistic beam propagation with minimal energy loss, implying that the beam propagates in a state close to time- and volume-averaged marginal stability. This confirms a central tenet of the stochastic growth theory for type III bursts.

Cairns, Iver H.↗