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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Point Cloud Based Approach to Stem Width Extraction of Sorghum

A revolution in the field of genomics has produced vast amounts of data and furthered our understanding of the genotypephenotype map, but is currently constrained by manually intensive or limited phenotype data collection. We propose an algorithm to estimate stem width, a key characteristic used for biomass potential evaluation, from 3D point cloud data collected by a robot equipped with a depth sensor in a single pass in a standard field. The algorithm applies a two step alignment to register point clouds in different frames, a Frangi filter to identify stemlike objects in the point cloud and an orientation based filter to segment out and refine individual stems for width estimation. Individually, detected stems which are split due to occlusions are merged and then registered with previously found stems in previous camera frames in order to track temporally. We then refine the estimates to produce an accurate histogram of width estimates per plot. Since the plants in each plot are genetically identical, distributions of the stem width per plot can be useful in identifying genetically superior sorghum for biofuels.

09 BIOMASS FUELS↗
DOE OSTI2023

Extrapolation of the Rainflow-Counted Load Ranges for Fatigue Assessment of the Wind Turbine's Blades

Wind turbine design standards recommend the use of statistical modeling coupled with extrapolation of the short-term load data to long-term periods for fatigue reliability assessment. However, statistical error and computational expense can limit the accuracy of such approaches. In the case of wind turbine blades, the errors are more significant because of the high material fatigue exponent that makes the damage estimations more sensitive to variations. In addition, due to different excitation sources, the flapwise load range histogram is not unimodal, and thus its statistical modeling is complex. In the present work, we provide three methods for statistical modeling of the flapwise bending moment ranges including a novel approach based on frequency-based separation of the modes. The first two methods are simplified approaches for modeling the most crucial load ranges using unimodal distributions and the third method involves multimodal distribution fitting. The research is based on 3600 10-minute aeroelastic simulations of DTU 10MW case study wind turbine from which a benchmark damage equivalent load (DEL) is calculated. The DEL calculated by each of the three proposed methods is compared to this reference. The results show that the conventional approach based on using 6 seeds as well as using mixture models fitted on the limited data lead to under-conservative results with errors up to 23%. On the other hand, the simplified unimodal approaches provided in this work can provide conservative estimations of the fatigue damage with mean values 5% and 12% higher than the benchmark. However, the variability of the DEL estimates is higher when using unimodal extrapolation of the load ranges, and the data can be conservative by 17.5%. The proposed unimodal fits suggested for modeling and extrapolation of the blade's load ranges provide less errors relatively and most importantly conservative DEL estimations while maintaining computational efficiency.

blade fatigue↗
DOE OSTI2024

Machine-learning based approach to examine ecological processes influencing the diversity of riverine dissolved organic matter composition

Dissolved organic matter (DOM) assemblages in freshwater rivers are formed from mixtures of simple to complex compounds that are highly variable across time and space. These mixtures largely form due to the environmental heterogeneity of river networks and the contribution of diverse allochthonous and autochthonous DOM sources. Most studies are, however, confined to local and regional scales, which precludes an understanding of how these mixtures arise at large, e.g., continental, spatial scales. The processes contributing to these mixtures are also difficult to study because of the complex interactions between various environmental factors and DOM. Here we propose the use of machine learning (ML) approaches to identify ecological processes contributing toward mixtures of DOM at a continental-scale. We related a dataset that characterized the molecular composition of DOM from river water and sediment with Fourier-transform ion cyclotron resonance mass spectrometry to explanatory physicochemical variables such as nutrient concentrations and stable water isotopes ( 2 H and 18 O). Using unsupervised ML, distinctive clusters for sediment and water samples were identified, with unique molecular compositions influenced by environmental factors like terrestrial input and microbial activity. Sediment clusters showed a higher proportion of protein-like and unclassified compounds than water clusters, while water clusters exhibited a more diversified chemical composition. We then applied a supervised ML approach, involving a two-stage use of SHapley Additive exPlanations (SHAP) values. In the first stage, SHAP values were obtained and used to identify key physicochemical variables. These parameters were employed to train models using both the default and subsequently tuned hyperparameters of the Histogram-based Gradient Boosting (HGB) algorithm. The supervised ML approach, using HGB and SHAP values, highlighted complex relationships between environmental factors and DOM diversity, in particular the existence of dams upstream, precipitation events, and other watershed characteristics were important in predicting higher chemical diversity in DOM. Our data-driven approach can now be used more generally to reveal the interplay between physical, chemical, and biological factors in determining the diversity of DOM in other ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Convolutional Neural Network for Segmenting Micro-X-ray Computed Tomography Images of Wood Cellular Structures

To further enhance the performance of wood products, improved tools are needed to study in situ cellular scale phenomena like mechanical deformations and moisture swelling. Micro-X-ray computed tomography (μXCT) using brilliant synchrotron light sources now has the spatial and temporal resolution for real-time visualization of phenomena in three-dimensional cellular structures. However, the tradeoff for speed includes the loss of intensity contrast between different types of materials within the imaged structure, such as cell wall and air in wood. This loss of contrast prevents traditional histogram-based segmentation methods from being used effectively. A new convolutional neural network (CNN) approach was therefore developed to segment fast μXCT images of wood into cell wall and air volumes. The fast μXCT and segmentation were demonstrated in the study of moisture swelling in loblolly pine (Pinus taeda) earlywood and latewood cellular structures conditioned at 0%, 33%, 75%, and 95% relative humidity (RH). The CNN segmentation results had a mean intersection over union (IoU) metric accuracy of 96%. Initial analysis of the swelling in the latewood revealed cell walls swelled about 25% when conditioned from 0% to 95% RH. Additionally, the widths of ray cell lumina in the transverse plane of latewood could be observed to increase at higher RH. The segmentation method presented here will facilitate future quantitative analyses in in situ μXCT studies of wood and other similar cellular materials.

Arzola-Villegas, Xavier (ORCID:0000000305369766)↗
DOE OSTI2024

Addressing Low-Cost Methane Sensor Calibration Shortcomings with Machine Learning

Quantifying methane emissions is essential for meeting near-term climate goals and is typically carried out using methane concentrations measured downwind of the source. One major source of methane that is important to observe and promptly remediate is fugitive emissions from oil and gas production sites but installing methane sensors at the thousands of sites within a production basin is expensive. In recent years, relatively inexpensive metal oxide sensors have been used to measure methane concentrations at production sites. Current methods used to calibrate metal oxide sensors have been shown to have significant shortcomings, resulting in limited confidence in methane concentrations generated by these sensors. To address this, we investigate using machine learning (ML) to generate a model that converts metal oxide sensor output to methane mixing ratios. To generate test data, two metal oxide sensors, TGS2600 and TGS2611, were collocated with a trace methane analyzer downwind of controlled methane releases. Over the duration of the measurements, the trace gas analyzer’s average methane mixing ratio was 2.40 ppm with a maximum of 147.6 ppm. The average calculated methane mixing ratios for the TGS2600 and TGS2611 using the ML algorithm were 2.42 ppm and 2.40 ppm, with maximum values of 117.5 ppm and 106.3 ppm, respectively. A comparison of histograms generated using the analyzer and metal oxide sensors mixing ratios shows overlap coefficients of 0.95 and 0.94 for the TGS2600 and TGS2611, respectively. Overall, our results showed there was a good agreement between the ML-derived metal oxide sensors’ mixing ratios and those generated using the more accurate trace gas analyzer. This suggests that the response of lower-cost sensors calibrated using ML could be used to generate mixing ratios with precision and accuracy comparable to higher priced trace methane analyzers. This would improve confidence in low-cost sensors’ response, reduce the cost of sensor deployment, and allow for timely and accurate tracking of methane emissions.

03 NATURAL GAS↗
DOE OSTI2016

LAMOST OBSERVATIONS IN THE KEPLER FIELD: SPECTRAL CLASSIFICATION WITH THE MKCLASS CODE

The LAMOST-Kepler project was designed to obtain high-quality, low-resolution spectra of many of the stars in the Kepler field with the Large Sky Area Multi Object Fiber Spectroscopic Telescope (LAMOST) spectroscopic telescope. To date 101,086 spectra of 80,447 objects over the entire Kepler field have been acquired. Physical parameters, radial velocities, and rotational velocities of these stars will be reported in other papers. In this paper we present MK spectral classifications for these spectra determined with the automatic classification code MKCLASS. We discuss the quality and reliability of the spectral types and present histograms showing the frequency of the spectral types in the main table organized according to luminosity class. Finally, as examples of the use of this spectral database, we compute the proportion of A-type stars that are Am stars, and identify 32 new barium dwarf candidates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

GLOBAL MODELING OF NEBULAE WITH PARTICLE GROWTH, DRIFT, AND EVAPORATION FRONTS. I. METHODOLOGY AND TYPICAL RESULTS

We model particle growth in a turbulent, viscously evolving protoplanetary nebula, incorporating sticking, bouncing, fragmentation, and mass transfer at high speeds. We treat small particles using a moments method and large particles using a traditional histogram binning, including a probability distribution function of collisional velocities. The fragmentation strength of the particles depends on their composition (icy aggregates are stronger than silicate aggregates). The particle opacity, which controls the nebula thermal structure, evolves as particles grow and mass redistributes. While growing, particles drift radially due to nebula headwind drag. Particles of different compositions evaporate at “evaporation fronts” (EFs) where the midplane temperature exceeds their respective evaporation temperatures. We track the vapor and solid phases of each component, accounting for advection and radial and vertical diffusion. We present characteristic results in evolutions lasting 2 × 10{sup 5} years. In general, (1) mass is transferred from the outer to the inner nebula in significant amounts, creating radial concentrations of solids at EFs; (2) particle sizes are limited by a combination of fragmentation, bouncing, and drift; (3) “lucky” large particles never represent a significant amount of mass; and (4) restricted radial zones just outside each EF become compositionally enriched in the associated volatiles. We point out implications for millimeter to submillimeter SEDs and the inference of nebula mass, radial banding, the role of opacity on new mechanisms for generating turbulence, the enrichment of meteorites in heavy oxygen isotopes, variable and nonsolar redox conditions, the primary accretion of silicate and icy planetesimals, and the makeup of Jupiter’s core.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Deep mixing in stellar variability: improved method, statistics, and applications

The preliminary results on deep-mixing manifestations in stellar variability are tested using our improved method and extended data set. We measure the timescales τ {sub m} of the stochastic change in the spectral power of rotational harmonics with numbers m ≤ 3 in the light curves of 1361 main-sequence stars from the Kepler mission archive. We find that the gradient [log(τ{sub 2})−log(τ{sub 1})]/[log(2)−log(1)] has a histogram maximum at −2/3, demonstrating agreement with Kolmogorov’s theory of turbulence and therefore confirming the manifestation of deep mixing. The squared amplitudes of the first and second rotational harmonics, corrected for integral photometry distortion, also show a quasi-Kolmogorov character with spectral index ≈−5/3. Moreover, the reduction of τ {sub 1} and τ {sub 2} to the timescales τ {sub lam1} and τ {sub lam2} of laminar convection in the deep stellar layers reveals the proximity of both τ {sub lam1} and τ {sub lam2} to the turnover time τ {sub MLT} of standard mixing length theory. Considering this result, we use the obtained stellar variability timescales instead of τ {sub MLT} in our analysis of the relation between stellar activity and the Rossby number P/τ {sub MLT}. Comparison of our diagrams with previous results and theoretical expectations shows that best-fit correspondence is achieved for τ {sub lam1}, which can therefore be used as an analog of τ {sub MLT}. This means that the laminar component (giant cells) of stellar turbulent convection indeed plays an important role in the physics of stars. Additionally, we estimate the diffusivity of magnetic elements in stellar photospheres.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

CHANGE OF MAGNETIC FIELD-GAS ALIGNMENT AT THE GRAVITY-DRIVEN ALFVÉNIC TRANSITION IN MOLECULAR CLOUDS: IMPLICATIONS FOR DUST POLARIZATION OBSERVATIONS

Diffuse striations in molecular clouds are preferentially aligned with local magnetic fields, whereas dense filaments tend to be perpendicular to them. When and why this transition occurs remain uncertain. To explore the physics behind this transition, we compute the histogram of relative orientation (HRO) between the density gradient and the magnetic field in three-dimensional magnetohydrodynamic (MHD) simulations of prestellar core formation in shock-compressed regions within giant molecular clouds. We find that, in the magnetically dominated (sub-Alfvénic) post-shock region, the gas structure is preferentially aligned with the local magnetic field. For overdense sub-regions with super-Alfvénic gas, their elongation becomes preferentially perpendicular to the local magnetic field. The transition occurs when self-gravitating gas gains enough kinetic energy from the gravitational acceleration to overcome the magnetic support against the cross-field contraction, which results in a power-law increase of the field strength with density. Similar results can be drawn from HROs in projected two-dimensional maps with integrated column densities and synthetic polarized dust emission. We quantitatively analyze our simulated polarization properties, and interpret the reduced polarization fraction at high column densities as the result of increased distortion of magnetic field directions in trans- or super-Alfvénic gas. Furthermore, we introduce measures of the inclination and tangledness of the magnetic field along the line of sight as the controlling factors of the polarization fraction. Observations of the polarization fraction and angle dispersion can therefore be utilized in studying local magnetic field morphology in star-forming regions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Size of the accretion disk in the graviationally lensed quasar SDSS J1004+4112 from the statistics of microlensing magnifications

We present eight monitoring seasons of the four brightest images of the gravitational lens SDSS J1004+4112 observed between 2003 December and 2010 October. Using measured time delays for the images A, B, and C and the model predicted time delay for image D we have removed the intrinsic quasar variability, finding microlensing events of about 0.5 and 0.7 mag of amplitude in the images C and D. From the statistics of microlensing amplitudes in images A, C, and D, we have inferred the half-light radius (at λ{sub rest}=2407 A-ring ) for the accretion disk using two different methods, R{sub 1/2}=8.7{sub −5.5}{sup +18.5}√(M/0.3M{sub ⊙}) (histogram product) and R{sub 1/2}=4.2{sub −2.2}{sup +3.2}√(M/0.3M{sub ⊙}) lt-days (χ{sup 2}). The results are in agreement within uncertainties with the size predicted from the black hole mass in SDSS J1004+4112 using thin disk theory.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

FLARES ON A-TYPE STARS: EVIDENCE FOR HEATING OF SOLAR CORONA BY NANOFLARES?

We analyzed the occurrence rates of flares on stars of spectral types K, G, F, and A, observed by Kepler . We found that the histogram of occurrence frequencies of stellar flares is systematically shifted toward a high-energy tail for A-type stars compared to stars of cooler spectral types. We extrapolated the fitted power laws toward flares with smaller energies (nanoflares) and made estimates for total energy flux to stellar atmospheres by flares. We found that, for A-type stars, the total energy flux density was at least four-times smaller than for G stars. We speculate that this deficit in energy supply may explain the lack of hot coronae on A-type stars. Our results indicate the importance of nanoflares for heating and formation of the solar corona.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

SUPERPOSITION OF POLYTROPES IN THE INNER HELIOSHEATH

This paper presents a possible generalization of the equation of state and Bernoulli's integral when a superposition of polytropic processes applies in space and astrophysical plasmas. The theory of polytropic thermodynamic processes for a fixed polytropic index is extended for a superposition of polytropic indices. In general, the superposition may be described by any distribution of polytropic indices, but emphasis is placed on a Gaussian distribution. The polytropic density–temperature relation has been used in numerous analyses of space plasma data. This linear relation on a log–log scale is now generalized to a concave-downward parabola that is able to describe the observations better. The model of the Gaussian superposition of polytropes is successfully applied in the proton plasma of the inner heliosheath. The estimated mean polytropic index is near zero, indicating the dominance of isobaric thermodynamic processes in the sheath, similar to other previously published analyses. By computing Bernoulli's integral and applying its conservation along the equator of the inner heliosheath, the magnetic field in the inner heliosheath is estimated, B ∼ 2.29 ± 0.16 μG. The constructed normalized histogram of the values of the magnetic field is similar to that derived from a different method that uses the concept of large-scale quantization, bringing incredible insights to this novel theory.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A Census of the 32 Ori Association with Gaia*

I have used high-precision photometry and astrometry from the third data release of Gaia (DR3) to identify candidate members of the 32 Ori association. Spectral types and radial velocities have been measured for subsets of the candidates using new and archival spectra. For the candidates that have radial velocity measurements, I have used UVW velocities to further constrain their membership, arriving at a final catalog of 169 candidates. I estimate that the completeness of the survey is ~90% for spectral types of ≲M7 (≳0.06 M ⊙ ). The histogram of spectral types for the 32 Ori candidates exhibits a maximum at M5 (~0.15 M ⊙ ), resembling the distributions measured for other young clusters and associations in the solar neighborhood. The available UVW velocities indicate that the association is expanding, but they do not produce a well-defined kinematic age. Based on their sequences of low-mass stars in color–magnitude diagrams, the 32 Ori association and Upper Centaurus-Lupus/Lower Centaurus-Crux (UCL/LCC) are coeval to within ±1.2 Myr, and they are younger than the β Pic moving group by ~3 Myr, which agrees with results from previous analysis based on the second data release of Gaia. Finally, I have used mid-IR photometry from the Wide-field Infrared Survey Explorer to check for excess emission from circumstellar disks among the 32 Ori candidates. Disks are detected for 18 candidates, half of which are reported for the first time in this work. The fraction of candidates at ≤M6 that have full, transitional, or evolved disks is 10/149 = 0.07$^{+0.03}_{-0.02'}$ which is consistent with the value for UCL/LCC.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A Census of the Taurus Star-forming Region and Neighboring Associations with Gaia*

I have used high-precision photometry and astrometry from the third data release of Gaia to perform a survey for members of the Taurus star-forming region and young associations in its vicinity. This work has produced a new catalog of 532 adopted members of Taurus, which has only minor changes relative to the previous catalog from Esplin & Luhman. I have used the Gaia astrometry to divide the Taurus members into 13 groups that have distinct kinematics. Meanwhile, I have identified 1378 candidate members of seven associations near Taurus. All of these associations have histograms of spectral types that peak near M5 (~0.15 M ⊙ ), resembling other young populations in the solar neighborhood. For the Taurus groups and neighboring associations, I have estimated ages from their sequences of low-mass stars in Hertzsprung–Russell diagrams. Most of the Taurus groups have median ages of ~1–3 Myr, while the associations have ages ranging from 13 to 56 Myr. I have used mid-infrared photometry from the Wide-field Infrared Survey Explorer to search for excess emission from circumstellar disks among the candidate members of the associations. Disks are detected for 51 stars, 20 of which are reported for the first time in this work. Some recent studies have proposed that samples of older stars (≳10 Myr) found in the vicinity of Taurus represent a distributed population that is associated with the Taurus cloud complex. However, I find that most of those stars have kinematics that are inconsistent with any relationship with Taurus.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

ON THE NON-KOLMOGOROV NATURE OF FLARE-PRODUCTIVE SOLAR ACTIVE REGIONS

A magnetic power spectral analysis is performed on 53 solar active regions, observed from 2011 August to 2012 July. Magnetic field data obtained from the Helioseismic and Magnetic Imager, inverted as Active Region Patches, are used to study the evolution of the magnetic power index as each region rotates across the solar disk. Active regions are classified based on the numbers and sizes of solar flares they produce in order to study the relationship between flare productivity and the magnetic power index. The choice of window size and inertial range plays a key role in determining the correct magnetic power index. The overall distribution of magnetic power indices has a range of 1.0–2.5. Flare-quiet regions peak at a value of 1.6. However, flare-productive regions peak at a value of 2.2. Overall, the histogram of the distribution of power indices of flare-productive active regions is well separated from flare-quiet active regions. Only 12% of flare-quiet regions exhibit an index greater than 2, whereas 90% of flare-productive regions exhibit an index greater than 2. Flare-quiet regions exhibit a high temporal variance (i.e., the index fluctuates between high and low values), whereas flare-productive regions maintain an index greater than 2 for several days. This shows the importance of including the temporal evolution of active regions in flare prediction studies, and highlights the potential of a 2–3 day prediction window for space weather applications.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Interplay of electron and proton instabilities in expanding solar wind

Protons and electrons observed in the solar wind possess temperature anisotropies for which upper and lower bounds appear to be partially regulated by marginal conditions associated with various kinetic plasma instabilities. Such features are most clearly seen when a collection of measurements is plotted as a two-dimensional histogram in (β{sub ∥},T{sub ⊥}/T{sub ∥}) phase space. While the partial outer boundaries of such data distribution may well be explained by various instability threshold conditions, an outstanding issue is that the majority of data points are actually located sufficiently away from the boundaries and reside in near isotropic conditions. This implies that certain processes are operative that counteract the adiabatic effect in the radially expanding solar wind, without which solar wind plasma will inexorably be forced to proceed toward the marginal firehose condition. A number of physical processes have been proposed in the literature to explain such a feature. The present paper suggests yet another mechanism. It considers dynamic electrons and protons in the quasilinear evolution of anisotropy-driven instabilities, which is in contrast to previous studies where either protons or electrons are assumed to be stationary when considering the dynamics of the other particle species. It is shown that the dynamical interplay between the two species during the quasilinear development of parallel electron firehose and proton–cyclotron instabilities leads to a counter-balancing effect, which prevents the uniform progression of the solar wind protons toward the marginal firehose state.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Kinematic clues to bar evolution for galaxies in the local universe: why the fastest rotating bars are rotating most slowly

We have used Spitzer images of a sample of 68 barred spiral galaxies in the local universe to make systematic measurements of bar length and bar strength. We combine these with precise determinations of the corotation radii associated with the bars, taken from our previous study, which used the phase change from radial inflow to radial outflow of gas at corotation, based on high-resolution two-dimensional velocity fields in Hα taken with a Fabry–Pérot spectrometer. After presenting the histograms of the derived bar parameters, we study their dependence on the galaxy morphological type and on the total stellar mass of the host galaxy, and then produce a set of parametric plots. These include the bar pattern speed versus bar length, the pattern speed normalized with the characteristic pattern speed of the outer disk versus the bar strength, and the normalized pattern speed versus R, the ratio of corotation radius to bar length. To provide guidelines for our interpretation, we used recently published simulations, including disk and dark matter halo components. Our most striking conclusion is that bars with values of R < 1.4, previously considered dynamically fast rotators, can be among the slowest rotators both in absolute terms and when their pattern speeds are normalized. The simulations confirm that this is because as the bars are braked, they can grow longer more quickly than the outward drift of the corotation radius. We conclude that dark matter halos have indeed slowed down the rotation of bars on Gyr timescales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Solar Energetic Particle Transport Near a Heliospheric Current Sheet

Solar energetic particles (SEPs), a major component of space weather, propagate through the interplanetary medium strongly guided by the interplanetary magnetic field (IMF). In this work, we analyze the implications that a flat Heliospheric Current Sheet (HCS) has on proton propagation from SEP release sites to the Earth. We simulate proton propagation by integrating fully 3D trajectories near an analytically defined flat current sheet, collecting comprehensive statistics into histograms, fluence maps, and virtual observer time profiles within an energy range of 1–800 MeV. We show that protons experience significant current sheet drift to distant longitudes, causing time profiles to exhibit multiple components, which are a potential source of confusing interpretations of observations. We find that variation of the current sheet thickness within a realistic parameter range has little effect on particle propagation. We show that the IMF configuration strongly affects the deceleration of protons. We show that in our model, the presence of a flat equatorial HCS in the inner heliosphere limits the crossing of protons into the opposite hemisphere.

79 ASTRONOMY AND ASTROPHYSICS↗