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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Working with Bézier Curves as bases for Functional Expansion Tallies

Functional expansion tallies (FETs) are powerful tools for getting more information per history from Monte Carlo simulations, but in the past they have been constrained to orthogonal bases. Bézier curves are used widely in computer aided design (CAD) geometry kernels and could be well-suited for FETs due to their ability to assume many arbitrary shapes, but they use nonorthogonal bases. Recent developments in 2021 have made nonorthogonal FETs possible. The convergence of Bézier curve FETs in both polynomial order and number of samples is explored in this work. It is shown that these bases are well-suited for representing normal distributions and this opens the door to the possibility of other CAD-derived FET bases.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Study of the normalized transverse momentum distribution of W bosons produced in $p\bar{p}$ collisions at $\sqrt{s}$ = 1.96 TeV

We present a study of the normalized transverse momentum distribution of W bosons produced in $p\bar{p}$ collisions, using data corresponding to an integrated luminosity of 4.35 fb -1 collected with the D0 detector at the Fermilab Tevatron collider at $\sqrt{s}$ = 1.96 TeV. The measurement focuses on the transverse momentum region below 15 GeV, which is of special interest for electroweak precision measurements; it relies on the same detector calibration methods which were used for the precision measurement of the W boson mass. The measured distribution is compared to different QCD predictions and a procedure is given to allow the comparison of any further theoretical models to the D0 data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

XPCA - R Implementation

Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. We consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multivariate distribution and, therefore, normal marginal distributions for each column. If some marginals are continuous but not normal, the semiparametric copula-based principal component analysis (COCA) method is an alternative to PCA that combines a Gaussian copula with nonparametric marginals. If some marginals are discrete or semi-continuous, we present a new extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric marginals and accounts for discrete variables in the likelihood calculation by integrating over appropriate intervals. Like PCA, the factors produced by XPCA can be used to find latent structure in data, build predictive models, and perform dimensionality reduction. We implement the new model, XPCA, and its ancestors PCA and COCA in both R and Python. This software provides implementations of XPCA, COCA, and probabilistic PCA in both R and Python. XPCA is written in arXiv report https://arxiv.org/abs/1808.07510. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. SAND2019-10591 M

Kincher-Winoto, Kina↗
DOE OSTI2019

XPCA -Python Implementation

Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. We consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multivariate distribution and, therefore, normal marginal distributions for each column. If some marginals are continuous but not normal, the semiparametric copula-based principal component analysis (COCA) method is an alternative to PCA that combines a Gaussian copula with nonparametric marginals. If some marginals are discrete or semi-continuous, we present a new extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric marginals and accounts for discrete variables in the likelihood calculation by integrating over appropriate intervals. Like PCA, the factors produced by XPCA can be used to find latent structure in data, build predictive models, and perform dimensionality reduction. We implement the new model, XPCA, and its ancestors PCA and COCA in both R and Python. This software provides implementations of XPCA, COCA, and probabilistic PCA in both R and Python. XPCA is written in arXiv report https://arxiv.org/abs/1808.07510. SAND2019-10591 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Kincher-Winoto, Kina↗
DOE OSTI2015

MO-G-304-02: Knowledge Based DVH Prediction Using a Geometric Dose Transform

Purpose: To demonstrate a novel method for predicting patient dose-volume histograms (DVHs) using a prior database of optimized radiotherapy treatment plans. Such predicted DVHs could be useful for automating treatment planning. Methods: Our initial demonstration utilized a database of 100 prostate intensity-modulated radiotherapy (IMRT) data-sets. Each data-set contained a CT image with contours of the planning target volume (PTV), rectum, and bladder, the parameters of a clinically approved IMRT plan, and a corresponding simulated dose distribution. We applied a novel geometric transformation to remove the influence of the PTV size, shape, and location on the dose distribution. We termed the transformed distribution the geometrically normalized dose distribution (GNDD). This normalization transform was applied to 80 data-sets randomly selected from the database, and a population GNDD was computed as the average. Next, the population GNDD was mapped onto each of the remaining 20 patient datasets using the reverse of the geometric normalization transform, and predicted DVHs were calculated from the reverse transformed dose distributions (GNDD-DVHs). In addition, a state of the art machine learning based method from the literature was tested for comparison. Results: DVH prediction accuracy was quantified by calculating the relative root mean squared error (rRMSE) on predicted DVHs for the 20 test patients using their known DVHs. For bladder, rectum, and PTV average rRMSEs for the GNDD method were 9.7 ± 4.2%, 13.9 ± 6.0%, and 2.3 ± 0.5% respectively. Prediction results using GNDD were roughly equivalent to that from the machine learning method. Conclusion: We developed a new method for predicting DVH curves from a database of prior patient plans. We demonstrated that our simple approach achieves accuracy comparable to a method using a complicated machine learning based approach.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

A General N -Moment Normalization Method for Deriving Raindrop Size Distribution Scaling Relationships

A general drop size distribution (DSD) normalization method is formulated in terms of generalized power series relating any DSD moment to any number and combination of reference moments. This provides a consistent framework for comparing the variability of normalized DSD moments using different sets of reference moments, with no explicit assumptions about the DSD functional form (e.g., gamma). It also provides a method to derive any unknown moment plus an estimate of its uncertainty from one or more known moments, which is relevant to remote sensing retrievals and bulk microphysics schemes in weather and climate models. Herein, the approach is applied to a large dataset of disdrometer-observed and bin microphysics-modeled DSDs. As expected, the spread of normalized moments decreases as the number of reference moments is increased, quantified by the logarithmic standard deviation of the normalized moments, σ. Averaging σ for all combinations of reference moments and normalized moments of integer order 0–10, 42.9%, 81.3%, 93.7%, and 96.9% of spread are accounted for applying one-, two-, three-, and four-moment normalizations, respectively. Thus, DSDs can be well characterized overall using three reference moments, whereas adding a fourth reference moment contributes little independent information. The spread of disdrometer-observed DSD moments from uncertainty associated with drop count statistics generally lies between values of σ using two- and three-moment normalizations. However, this uncertainty has little impact on the derived DSD scaling relationships or σ when considered.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Rapid flattening of butterfly pitch angle distributions of radiation belt electrons by whistler-mode chorus

Van Allen radiation belt electrons exhibit complex dynamics during geomagnetically active periods. Investigation of electron pitch angle distributions (PADs) can provide important information on the dominant physical mechanisms controlling radiation belt behaviors. In this paper, we report a storm time radiation belt event where energetic electron PADs changed from butterfly distributions to normal or flattop distributions within several hours. Van Allen Probes observations showed that the flattening of butterfly PADs was closely related to the occurrence of whistler-mode chorus waves. Two-dimensional quasi-linear STEERB simulations demonstrate that the observed chorus can resonantly accelerate the near-equatorially trapped electrons and rapidly flatten the corresponding electron butterfly PADs. Finally, these results provide a new insight on how chorus waves affect the dynamic evolution of radiation belt electrons.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Gauges, loops, and polynomials for partition functions of graphical models

Graphical models represent multivariate and generally not normalized probability distributions. Computing the normalization factor, called the partition function, is the main inference challenge relevant to multiple statistical and optimization applications. The problem is #P-hard that is of an exponential complexity with respect to the number of variables. Here, aimed at approximating the partition function, we consider multi-graph models where binary variables and multivariable factors are associated with edges and nodes, respectively, of an undirected multi-graph. We suggest a new methodology for analysis and computations that combines the Gauge function technique from Chertkov and Chernyak with the technique developed in Anari and Oveis Gharan 2017 arXiv:1702.02937; Gurvits 2011 arXiv:1106.2844; Straszak and Vishnoi 2017 55th Annual Allerton Conf. on Communication, Control, and Computing, based on the recent progress in the field of real stable polynomials. We show that the Gauge function, representing a single-out term in a finite sum expression for the partition function which achieves extremum at the so-called belief-propagation gauge, has a natural polynomial representation in terms of gauges/variables associated with edges of the multi-graph. Moreover, Gauge function can be used to recover the partition function through a sequence of transformations allowing appealing algebraic and graphical interpretations. Algebraically, one step in the sequence consists of the application of a differential operator over gauges associated with an edge. Graphically, the sequence is interpreted as a repetitive elimination/contraction of edges resulting in multi-graph models on decreasing in size (number of edges) graphs with the same partition function as in the original multi-graph model. Even though the complexity of computing factors in the sequence of the derived multi-graph models and respective Gauge functions grow exponentially with the number of eliminated edges, polynomials associated with the new factors remain bi-stable if the original factors have this property. Moreover, we show that BP estimations in the sequence do not decrease, each low-bounding the partition function.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Measurement of double-differential cross sections for top quark pair production in pp collisions at $\sqrt{s} = 8$ $\,\text {TeV}$ and impact on parton distribution functions

Normalized double-differential cross sections for top quark pair ( $\mathrm{t}\overline{\mathrm{t}}$ ) production are measured in pp collisions at a centre-of-mass energy of 8 $\,\text {TeV}$ with the CMS experiment at the LHC. The analyzed data correspond to an integrated luminosity of 19.7 $\,\text {fb}^{-1}$ . The measurement is performed in the dilepton $\mathrm {e}^{\pm }\mu ^{\mp }$ final state. The $\mathrm{t}\overline{\mathrm{t}}$ cross section is determined as a function of various pairs of observables characterizing the kinematics of the top quark and $\mathrm{t}\overline{\mathrm{t}}$ system. The data are compared to calculations using perturbative quantum chromodynamics at next-to-leading and approximate next-to-next-to-leading orders. They are also compared to predictions of Monte Carlo event generators that complement fixed-order computations with parton showers, hadronization, and multiple-parton interactions. Overall agreement is observed with the predictions, which is improved when the latest global sets of proton parton distribution functions are used. The inclusion of the measured $\mathrm{t}\overline{\mathrm{t}}$ cross sections in a fit of parametrized parton distribution functions is shown to have significant impact on the gluon distribution.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Correlated rattling-ion origins of dielectric properties in reentrant dipole glasses BaTiO{sub 3}-BiScO{sub 3}

The local structure of the pseudo-cubic solid solution 0.6BaTiO{sub 3}-0.4BiScO{sub 3}, which exhibits reentrant dipole-glass behavior, has been determined using the Reverse Monte Carlo method to simultaneously fit (1) neutron and X-ray total scattering data (including the corresponding real-space pair-distribution functions), (2) Bi and Sc extended X-ray absorption fine structure, and (3) patterns of diffuse scattering in electron diffraction. These structural refinements revealed the multi-site probability density distributions for both Bi (14-sites) and Ti (8 sites), whereas Ba and Sc featured normal unimodal distributions. Bi atoms are displaced along both the 〈111〉 and 〈100〉 directions, while Ti atoms are shifted along 〈111〉. Correlated dynamic hopping of Bi and Ti over their corresponding split sites combined with chemical disorder is proposed as the origin of the strong frequency dispersion observed in dielectric measurements. The existence of split sites also explains the reentrant dipole-glass behavior reported for this system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Direct assignment of molecular vibrations via normal mode analysis of the neutron dynamic pair distribution function technique

For over a century, vibrational spectroscopy has enhanced the study of materials. Yet, assignment of particular molecular motions to vibrational excitations has relied on indirect methods. Here, we demonstrate that applying group theoretical methods to the dynamic pair distribution function analysis of neutron scattering data provides direct access to the individual atomic displacements responsible for these excitations. Applied to the molecule-based frustrated magnet with a potential magnetic valence-bond state, LiZn{sub 2}Mo{sub 3}O{sub 8}, this approach allows direct assignment of the constrained rotational mode of Mo{sub 3}O{sub 13} clusters and internal modes of MoO{sub 6} polyhedra. We anticipate that coupling this well known data analysis technique with dynamic pair distribution function analysis will have broad application in connecting structural dynamics to physical properties in a wide range of molecular and solid state systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Peculiarities of high-overtone transition probabilities in carbon monoxide revealed by high-precision calculation

In the recent work devoted to the calculation of the rovibrational line list of the CO molecule [G. Li et al., Astrophys. J., Suppl. Ser. 216, 15 (2015)], rigorous validation of the calculated parameters including intensities was carried out. In particular, the Normal Intensity Distribution Law (NIDL) [E. S. Medvedev, J. Chem. Phys. 137, 174307 (2012)] was employed for the validation purposes, and it was found that, in the original CO line list calculated for large changes of the vibrational quantum number up to Δn = 41, intensities with Δn > 11 were unphysical. Therefore, very high overtone transitions were removed from the published list in Li et al. Here, we show how this type of validation is carried out and prove that the quadruple precision is indispensably required to predict the reliable intensities using the conventional 32-bit computers. Based on these calculations, the NIDL is shown to hold up for the 0 → n transitions till the dissociation limit around n = 83, covering 45 orders of magnitude in the intensity. The low-intensity 0 → n transition predicted in the work of Medvedev [Determination of a new molecular constant for diatomic systems. Normal intensity distribution law for overtone spectra of diatomic and polyatomic molecules and anomalies in overtone absorption spectra of diatomic molecules, Institute of Chemical Physics, Russian Academy of Sciences, Chernogolovka, 1984] at n = 5 is confirmed, and two additional “abnormal” intensities are found at n = 14 and 23. Criteria for the appearance of such “anomalies” are formulated. The results could be useful to revise the high-overtone molecular transition probabilities provided in spectroscopic databases.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2016

Effect of Ni content on the morphological evolution of Ni-YSZ solid oxide fuel cell electrodes

The coarsening of Ni in Ni–yttria-stabilized zirconia (YSZ) anodes is a potential cause of long term solid oxide fuel cells (SOFC) performance degradation. The specifics of the Ni-YSZ structure—including Ni/YSZ ratio, porosity, and particle size distributions—are normally selected to minimize anode polarization resistance, but they also impact long-term stability. A better understanding of how these factors influence long-term stability is important for designing more durable anodes. The effect of structural details, e.g., Ni-YSZ ratio, on Ni coarsening has not been quantified. Furthermore, prior measurements have been done by comparing evolved structures with control samples, such that sample-to-sample variations introduce errors. Here in this paper, we report a four dimensional (three spatial dimensions and time) study of Ni coarsening in Ni-YSZ anode functional layers with different Ni/YSZ ratios, using synchrotron x-ray nano-tomography. The continuous structural evolution was observed and analyzed at sub-100 nm resolution. It is shown quantitatively that increasing the Ni/YSZ ratio increases the Ni coarsening rate. This is due to both increased pore volume and a decrease in the YSZ volume fraction, such that there is more free volume and a less obtrusive YSZ network, both of which allow greater Ni coarsening. The results are shown to be in good agreement with a power-law coarsening model. In conclusion, the finding is critical for informing the design of SOFC electrode microstructures that limit coarsening and performance degradation.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Properties of galactic dark matter: Constraints from astronomical observations

The distributions of normal matter and of dark matter in the Galaxy are coupled to each other as they both move in the common gravitational potential. In order to fully exploit this interplay and to derive the various properties of dark matter relevant to their direct and indirect detection, we have comprehensively reviewed the astronomical observations of the spatial and velocity distributions of the components of normal matter. We then postulate that the phase-space distribution of dark matter follows a lowered-isothermal form and self-consistently solve Poisson's equation to construct several models for the spatial and velocity distributions of dark matter. In this paper, we compute the total gravitational potential of the normal and dark matter components and investigate their consistency with current observations of the rotation curve of the Galaxy and of the spatial and velocity distributions of blue horizontal-branch and blue straggler stars. Even with this demand of consistency, a large number of models with a range of parameters characterizing the dark matter distribution remain. We find that the best choice of parameters, within the range of allowed values for the surface density of the disk 55 M {sub ☉} pc{sup –2}, are the following: the dark matter density at the Galactic center ρ{sub DM,} {sub c} ≈ 100-250 GeV cm{sup –3}, the local dark matter density ρ{sub DM}(R {sub 0}) ≈ 0.56-0.72 GeV cm{sup –3}, and the rms speed of dark matter particles 〈v{sub DM}{sup 2}(R{sub 0})〉{sup 1/2}≈490−−550 km s{sup –1}. We also discuss possible astronomical observations that may further limit the range of the allowed models. The predictions of the allowed models for direct and indirect detection will be discussed separately in a companion paper.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

The effects of burial diagenesis on multiscale porosity in the St. Peter Sandstone: An imaging, small-angle, and ultra-small-angle neutron scattering analysis

To examine the effects of burial diagenesis on heirarchical pore structures in sandstone and compare those with the effects of overgrowth formation, we obtained samples of St. Peter Sandstone from drill cores obtained in the Illinois and Michigan Basins. The multiscale pore structure of rocks in sedimentary reservoirs and the mineralogy associated with those pores are critical factors for estimating reservoir properties, including fluid mass in place, permeability, and capillary pressures, as well as geochemical interactions between the rock and the fluid. The combination of small- and ultra-small-angle neutron scattering with backscattered electron or X ray-computed tomographic imaging, or both, provided a means by which pore structures were quantified at scales ranging from aproximately 1 nm to 1 cm—seven orders of magnitude. Larger scale (>10 µm) porosity showed the expected logarithmic decrease in porosity with depth, although there was significant variation in each sample group. However, small- and ultra-small-angle neutron scattering data showed that the proportion of small-scale porosity increased with depth. Porosity distributions were not continuous, but consisted of a series of log normal-like distributions at several distinct scales within these rocks. Fractal dimensions at larger scales decreased (surfaces smoothed) with increasing depth, and those at smaller scales increased (surfaces roughened) and pores become more isolated (higher lacunarity). Furthermore, data suggest that changes in pore-size distributions are controlled by both physical (compaction) and chemical effects (precipitation, cementation, dissolution).

58 GEOSCIENCES↗
DOE OSTI2017

Comparing Methods for Assessing Reliability Uncertainty Based on Pass/Fail Data Collected Over Time

In this paper, we compare statistical methods for analyzing pass/fail data collected over time; some methods are traditional and one (the RADAR or Rationale for Assessing Degradation Arriving at Random) was recently developed. These methods are used to provide uncertainty bounds on reliability. We make observations about the methods' assumptions and properties. Finally, we illustrate the differences between two traditional methods, logistic regression and Weibull failure time analysis, and the RADAR method using a numerical example.

97 MATHEMATICS AND COMPUTING↗