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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Understanding Asteroid 16 Psyche’s composition through 3D impact crater modeling

Asteroid 16 Psyche is the largest M-type (metallic) Main Belt Asteroid (MBA). Radar albedo data indicate Psyche’s surface is rich in metallic content, but estimates for Psyche’s bulk structure vary widely, with bulk density estimates ranging from 1.4 ± 0.3 g/cm 3 to 4.5 ± 1.4 g/cm 3 and porosity estimates ranging from 30% to 70%. A forthcoming NASA discovery mission will visit Psyche. Narrowing the range of estimated compositions of Psyche prior to mission launch can help to determine the necessary tools for asteroid analysis. Psyche has two large impact structures in its Southern hemisphere, with estimated diameters from 50 km to 70 km and estimated depths up to 6.4 km. In this work, we present results from 2D and 3D hydrocode simulations of the largest of these impact structures. Through quantitative comparison of the simulated crater dimensions with measured values, our models suggest that Psyche is largely composed of porous, metallic material. In addition, to match the crater dimensions, our simulations indicate that the angle of impact was significantly altered from vertical by 45–60 degrees.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Multi-head physics-informed neural networks for learning functional priors and uncertainty quantification

In numerous applications, the integration of prior knowledge and historical information is essential, particularly for tasks requiring the solution of ordinary or partial differential equations (ODEs/PDEs) in data-sparse or noisy environments. For instance, achieving accurate solutions to time-dependent PDEs with limited initial condition measurements necessitates an effective strategy for embedding prior knowledge. Hard-parameter sharing architectures in neural networks (NNs) have demonstrated success in both traditional and scientific machine learning domains, facilitating the learning of informative representations. Here, in this study, we introduce a novel, yet efficient, method to enhance physics-informed neural networks (PINNs) by incorporating a multi-head structure that enables the learning of functional priors from both empirical data and governing physical laws. This prior information can then be used to address data sparsity and high-level noise in solving ODE/PDE problems with uncertainty quantification (UQ). The approach, termed Multi-Head PINN (MH-PINN), consists of a shared body NN and multiple head NNs, each corresponding to an individual PINN instance. Our framework for functional prior learning is carried out in two stages: (1) training the MH-PINNs to develop a shared body NN alongside multiple head NNs, and (2) employing these trained head NNs to estimate a prior distribution through a normalizing flow-based density estimator. The learned functional prior can then be applied as a regularization mechanism in deterministic contexts or as an informative prior within a Bayesian inference framework, aiding in the resolution of subsequent ODE/PDE tasks. We evaluate the efficacy of MH-PINNs across five benchmark problems, including a high-dimensional parametric PDE, all characterized by data sparsity or substantial noise levels. Our findings reveal that MH-PINNs deliver accurate solutions and robust UQ, demonstrating adaptability across a range of complex and challenging scenarios.

Bayesian inference↗
DOE OSTI2022

Non-Parametric Statistical Analysis of Current Waveforms through Power System Sensors

The protection, control, and monitoring of the power grid is not possible without accurate measurement devices. As the percentage of renewable energy sources penetrating the existing grid infrastructure increases, so do uncertainties surrounding their effects on the everyday operation of the power system. Many of these devices are sources of high-frequency transients. These transients may be useful for identifying certain events or behaviors otherwise not seen in traditional analysis techniques. Therefore, the ability of sensors to accurately capture these phenomena is paramount. In this work, two commercial-grade power system distribution sensors are investigated in terms of their ability to replicate high-frequency phenomena by studying their responses to three events: a current inrush, a microgrid “close-in”, and a fault on the terminals of a wind turbine. Kernel density estimation is used to derive the non-parametric probability density functions of these error distributions and their adequateness is quantified utilizing the commonly used root mean square error (RMSE) metric. It is demonstrated that both sensors exhibit characteristics in the high harmonic range that go against the assumption that measurement error is normally distributed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Assessment of Molten Eutectic LiF-NaF-KF Density through Experimental Determination and Semiempirical Modeling

Molten salts have favorable material properties for use in high-temperature energy systems, including thermal energy storage systems, concentrating solar power plants, nuclear reactors, and various industrial manufacturing processes. Knowledge of chemical and thermophysical property data is essential for the design and optimization of these systems, yet data are often limited or uncertain for many candidate salts due to the difficulty of thermophysical property measurements at relevant temperatures (e.g., 500–900 °C). Here, the density of molten LiF-NaF-KF eutectic is reassessed through review of previous experimental data, new density measurements from 470 to 800 °C, and semiempirical modeling. Additionally, the density was measured using the displacement technique. Compositional and temperature-dependent density estimates were calculated with a multidimensional Redlich–Kister model. The results of the new experimental measurements agree within 2% of the modeled density of molten eutectic LiF-NaF-KF. The Redlich–Kister model’s prediction shows a near-ideal density behavior for the LiF-NaF-KF system and is promising for the estimation of off-eutectic LiF-NaK-KF densities. Finally, through review of the existing literature and comparison to new measurements, recommendations are made for the density of LiF-NaF-KF.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Outlier analyses of the Protein Data Bank archive using a probability-density-ranking approach

Outlier analyses are central to scientific data assessments. Conventional outlier identification methods do not work effectively for Protein Data Bank (PDB) data, which are characterized by heavy skewness and the presence of bounds and/or long tails. We have developed a data-driven nonparametric method to identify outliers in PDB data based on kernel probability density estimation. Unlike conventional outlier analyses based on location and scale, Probability Density Ranking can be used for robust assessments of distance from other observations. Analyzing PDB data from the vantage points of probability and frequency enables proper outlier identification, which is important for quality control during deposition-validation-biocuration of new three-dimensional structure data. Ranking of Probability Density also permits use of Most Probable Range as a robust measure of data dispersion that is more compact than Interquartile Range. The Probability-Density-Ranking approach can be employed to analyze outliers and data-spread on any large data set with continuous distribution.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1977

Emission measures, electron densities, and nonthermal velocities from optically thin UV lines near a quiet solar limb

Calibrated intensities and line widths are presented for 45 optically thin chromospheric and transition-zone UV lines observed in a quiet area near the solar limb. The observations were made over the wavelength range from 1175 to 1915 A with the slit spectrograph on the Skylab Apollo Telescope Mount. Three nonthermal-velocity components are derived from the transition-zone line widths: a main component of 23 km/s and two high-velocity components of 45 and 75 km/s, respectively, which contribute 5% to 10% of the averaged intensity. Absolute intensities are determined as a function of height, emission measures are obtained as functions of height as well as temperature, and electron densities are estimated from density-sensitive line ratios. The densities estimated from different sets of lines are found not to agree with each other, but the values obtained from the Si III multiplet between 1297 and 1301 A are considered to be the most reliable. An atmospheric model with inhomogeneous structures extending high above the solar surface is suggested.

Moe, O. K.↗
DOE OSTI2014

An actuarial approach to retrofit savings in buildings

An actuarial method has been developed for determining energy savings from retrofits from energy use data for a number of buildings. This method should be contrasted with the traditional method of using pre- and post-retrofit data on the same building. This method supports the U.S. Department of Energy Building Performance Database of real building performance data and related tools that enable engineering and financial practitioners to evaluate retrofits. The actuarial approach derives, from the database, probability density functions (PDFs) for energy savings from retrofits by creating peer groups for the user’s pre post buildings. From the energy use distribution of the two groups, the savings PDF is derived. This provides the basis for engineering analysis as well as financial risk analysis leading to investment decisions. Several technical issues are addressed: The savings PDF is obtained from the pre- and post-PDF through a convolution. Smoothing using kernel density estimation is applied to make the PDF more realistic. The low data density problem can be mitigated through a neighborhood methodology. Correlations between pre and post buildings are addressed to improve the savings PDF. Sample size effects are addressed through the Kolmogorov--Smirnov tests and quantile-quantile plots.

Subbarao, Krishnappa↗
DOE OSTI2016

Capabilities of laser technology for manufacturing diagnostic peptide matrices with maximal density

The process of manufacturing a matrix-gel biochip is modelled by means of laser fused deposition of a layer of polymer microparticles, containing a sensitive peptide element, onto a glass substrate. The limits of acceptable ranges and the optimal values of laser parameters, at which the melting of the polymer coating occurs without damaging the sensitive elements of the biochip, are theoretically determined. The results of the experiments on laser fused deposition of a layer of microparticles having the size 3 – 4 μm confirm the conservation of the functions of the biological complexes at optimal irradiation regimes. The parameters of the laser impact affecting the possible minimal separation between the zones of laser fused deposition are investigated. The essential role of heat conductivity and thermoplasticity of the polymer in increasing the size of the melted droplet is demonstrated. Using the laser radiation with the wavelength 532 nm focused into a spot with the diameter 6 μm (the laser pulse duration being 10 ms) the laser fused deposition density of 110000 spots per 1 cm{sup 2} is achieved. The maximal estimated density of laser fused deposition for the studied system amounts to 250000 spots per 1 cm{sup 2}. (biophotonics)

36 MATERIALS SCIENCE↗
DOE OSTI2021

Measuring Young Stars in Space and Time. II. The Pre-main-sequence Stellar Content of N44

The Hubble Space Telescope survey Measuring Young Stars in Space and Time (MYSST) entails some of the deepest photometric observations of extragalactic star formation, capturing even the lowest-mass stars of the active star-forming complex N44 in the Large Magellanic Cloud. We employ the new MYSST stellar catalog to identify and characterize the content of young pre-main-sequence (PMS) stars across N44 and analyze the PMS clustering structure. To distinguish PMS stars from more evolved line of sight contaminants, a non-trivial task due to several effects that alter photometry, we utilize a machine-learning classification approach. This consists of training a support vector machine (SVM) and a random forest (RF) on a carefully selected subset of the MYSST data and categorize all observed stars as PMS or non-PMS. Combining SVM and RF predictions to retrieve the most robust set of PMS sources, we find ∼26,700 candidates with a PMS probability above 95% across N44. Employing a clustering approach based on a nearest neighbor surface density estimate, we identify 16 prominent PMS structures at 1σ significance above the mean density with sub-clusters persisting up to and beyond 3σ significance. The most active star-forming center, located at the western edge of N44's bubble, is a subcluster with an effective radius of ∼5.6 pc entailing more than 1100 PMS candidates. Furthermore, we confirm that almost all identified clusters coincide with known H ii regions and are close to or harbor massive young O stars or YSOs previously discovered by MUSE and Spitzer observations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1981

Observed dynamics of superclusters

Radial velocity measurements of two large, rich superclusters show evidence of a gravitational slowing of the Hubble flow within the superclusters. Models assuming spherically symmetric, noncrossing mass shells yield mass density estimates for the clusters ranging from less than one to several times the critical density, and preliminary counts of galaxies and Abell clusters indicate a density enhancement within the superclusters of 10-71 times the field mass density. The combination of these two results is found to favor an open universe, assuming a standard Friedmann cosmology. It is noted that all forms of mass that are clumped on the scale of superclusters, or smaller, will be detected by these measurements, including neutrinos sufficiently massive to significantly contribute to the cosmological density parameter.

Harms, R. J.↗
DOE OSTI2011

PRECISE ESTIMATES OF THE PHYSICAL PARAMETERS FOR THE EXOPLANET SYSTEM HD 17156 ENABLED BY HUBBLE SPACE TELESCOPE FINE GUIDANCE SENSOR TRANSIT AND ASTEROSEISMIC OBSERVATIONS

We present observations of three distinct transits of HD 17156b obtained with the Fine Guidance Sensors on board the Hubble Space Telescope. We analyzed both the transit photometry and previously published radial velocities to find the planet-star radius ratio R{sub p} /R{sub *} = 0.07454 {+-} 0.00035, inclination i = 86.49{sup +0.24}{sub -0.20} deg, and scaled semimajor axis a/R{sub *} = 23.19{sup +0.32}{sub -0.27}. This last value translates directly to a mean stellar density determination {rho}{sub *} = 0.522{sup +0.021}{sub -0.018} g cm{sup -3}. Analysis of asteroseismology observations by the companion paper of Gilliland et al. provides a consistent but significantly refined measurement of {rho}{sub *} = 0.5308 {+-} 0.0040. We compare stellar isochrones to this density estimate and find M{sub *} = 1.275 {+-} 0.018 M{sub sun} and a stellar age of 3.37{sup +0.20}{sub -0.47} Gyr. Using this estimate of M{sub *} and incorporating the density constraint from asteroseismology, we model both the photometry and published radial velocities to estimate the planet radius R{sub p} = 1.0870 {+-} 0.0066 R{sub J} and the stellar radius R{sub *} = 1.5007 {+-} 0.0076 R{sub sun}. The planet radius is larger than that found in previous studies and consistent with theoretical models of a solar-composition gas giant of the same mass and equilibrium temperature. For the three transits, we determine the times of mid-transit to a precision of 6.2 s, 7.6 s, and 6.9 s, and the transit times for HD 17156 do not show any significant departures from a constant period. The joint analysis of transit photometry and asteroseismology presages similar studies that will be enabled by the NASA Kepler Mission.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Vision 20/20: Mammographic breast density and its clinical applications

Breast density is a strong predictor of the failure of mammography screening to detect breast cancer and is a strong predictor of the risk of developing breast cancer. The many imaging options that are now available for imaging dense breasts show great promise, but there is still the question of determining which women are “dense” and what imaging modality is suitable for individual women. To date, mammographic breast density has been classified according to the Breast Imaging-Reporting and Data System (BI-RADS) categories from visual assessment, but this is known to be very subjective. Despite many research reports, the authors believe there has been a lack of physics-led and evidence-based arguments about what breast density actually is, how it should be measured, and how it should be used. In this paper, the authors attempt to start correcting this situation by reviewing the history of breast density research and the debates generated by the advocacy movement. The authors review the development of breast density estimation from pattern analysis to area-based analysis, and the current automated volumetric breast density (VBD) analysis. This is followed by a discussion on seeking the ground truth of VBD and mapping volumetric methods to BI-RADS density categories. The authors expect great improvement in VBD measurements that will satisfy the needs of radiologists, epidemiologists, surgeons, and physicists. The authors believe that they are now witnessing a paradigm shift toward personalized breast screening, which is going to see many more cancers being detected early, with the use of automated density measurement tools as an important component.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Discovery of a Protocluster Associated with a Lyα Blob Pair at z = 2.3

Bright Lyα blobs (LABs)—extended nebulae with sizes of ∼100 kpc and Lyα luminosities of ∼10{sup 44} erg s{sup −1}—often reside in overdensities of compact Lyα emitters (LAEs) that may be galaxy protoclusters. The number density, variance, and internal kinematics of LABs suggest that they themselves trace group-like halos. Here, we test this hierarchical picture, presenting deep, wide-field Lyα narrowband imaging of a 1° × 0.°5 region around a LAB pair at z = 2.3 discovered previously by a blind survey. We find 183 Lyα emitters, including the original LAB pair and three new LABs with Lyα luminosities of (0.9--1.3)×10{sup 43} erg s{sup −1} and isophotal areas of 16–24 arcsec{sup 2}. Using the LAEs as tracers and a new kernel density estimation method, we discover a large-scale overdensity (Boötes J1430+3522) with a surface density contrast of δ{sub Σ}=2.7, a volume density contrast of δ ∼10.4, and a projected diameter of ≈20 comoving Mpc. Comparing with cosmological simulations, we conclude that this LAE overdensity will evolve into a present-day Coma-like cluster with log(M/M{sub ⊙}) ∼ 15.1 ± 0.2. In this and three other wide-field LAE surveys re-analyzed here, the extents and peak amplitudes of the largest LAE overdensities are similar, not increasing with survey size, and implying that they were indeed the largest structures then and today evolve into rich clusters. Intriguingly, LABs favor the outskirts of the densest LAE concentrations, i.e., intermediate LAE overdensities of δ{sub Σ}=1--2. We speculate that these LABs mark infalling protogroups being accreted by the more massive protocluster.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

ProbabilisticRecalibration of Forecasts

In this work, we present a scheme by which a probabilistic forecasting system whose predictions have a poor probabilistic calibration may be recalibrated through the incorporation of past performance information in order to produce a new forecasting system that is demonstrably superior to the original, inasmuch as one may use it to win wagers consistently against someone who is using the original system. The scheme utilizes Gaussian process (GP) modeling to estimate a probability distribution over the probability integral transform (PIT) of a scalar predictand. The GP density estimate gives closed-form access to information entropy measures that are associated with the estimated distribution, which allows the prediction of winnings in wagers against the base forecasting system. A separate consequence of the procedure is that the recalibrated forecast has a uniform expected PIT distribution. One distinguishing feature of the procedure is that it is appropriate even if the PIT values are not i.i.d. The recalibration scheme is formulated in a framework that exploits the deep connections among information theory, forecasting, and betting. We demonstrate the effectiveness of the scheme in two case studies: a laboratory experiment with a nonlinear circuit and seasonal forecasts of the intensity of the El Nino-Southern Oscillation phenomenon.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Observational evidence of preferred flow regimes in the Northern Hemisphere winter stratosphere

Ten years of stratospheric geopotential height data are analyzed in an attempt to determine whether there are preferred flow regimes in the Northern Hemisphere winter stratosphere. The data are taken from Stratospheric Sounding Units on board NOAA satellites. The probability density estimate of the amplitude of the wavenumber 1 10-mb height is found to be bimodal. The density distribution is composed of a dominant large-amplitude mode and a less frequent low-amplitude mode. When the wavenumber 1 10-mb height data are projected onto the phase plane defined by the 10-mb zonal-mean winds and wavenumber 1 100-mb heights, three preferred regimes are evident. The small-amplitude mode separates into a strong zonal wind-weak wave regime and a weak zonal wind-weak wave regime. The large-amplitude mode is an intermediate zonal wind-strong wave regime. Transitions between the large-amplitude regime and the weak zonal wind-weak wave regime are found to be associated with major stratospheric warmings. The clustering of the stratospheric data into the preferred flow regimes is interpreted in light of the bifurcation properties of the Holton and Mass model. The interannual variability of the Northern Hemisphere winter stratosphere is interpreted in terms of the relative frequency of the observed preferred regimes.

Pierce, R. B.↗
DOE OSTI2012

MATTER DISTRIBUTION AROUND GALAXIES

We explore the mass distribution of material associated with galaxies from the observation of gravitational weak lensing for the galaxy mass correlation function with the aid of N-body simulations of dark matter. The latter is employed to unfold various contributions to the integrated line-of-sight mass density. We conclude that galaxies have no definite edges of the matter distribution, extending to the middle of neighboring galaxies with the density profile roughly r{sup -2.4} beyond the virial radius. The mass distributed beyond the virial radius (gravitationally bound radius) explains the gap seen in the mass density estimates, the global value {Omega}{sub m} {approx} 0.27 and typically {Omega}{sub gal} {approx} 0.15 from the luminosity density multiplied by the mass-to-light ratio. We suggest using a physical method of gravitational lensing to characterize galaxy samples rather than characterizing them by photometric means.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Simulation based inference with domain adaptation for strong gravitational lensing

Simulation based inference leverages machine learning to carry out Bayesian inference in systems with intractable likelihoods. However, transitioning a network trained on simulated data to real data runs the risk of encountering domain shift, leading to performance losses. We attempt to implement domain adaptation into the sbi neural posterior estimation framework using the Maximum Mean Discrepancy as an additional network loss, using masked autoregressive fow (MAF) as our density estimator. We test the network on a set of 400,000 simulated strong gravitational lensing images generated using deeplenstronomy. The source domain is defined as low noise whereas the target domain has a noise profile sampled from experimentally derived DES survey conditions. We find that SBI appears robust against small changes in the data with similar performance on source and target. Moreover, while DA does lead to performance improvements, they are marginal at 6% less inference error.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗