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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

On-the-fly Doppler Broadening with Probability Table Interpolation for Unresolved Resonance Region in RMC

With the rapid development of computational power and parallel algorithms, Monte Carlo method has been widely investigated and used in neutron transport simulations due to its advantages in geometry modelling and usage of continuous energy point-wise cross sections. Nowadays the road has been paved to couple Monte Carlo codes with thermal-hydraulics codes to make multi-physics calculations. One of the most important aspects of thermal-hydraulics feedback is the detailed temperature distribution, which demands temperature dependent cross sections in neutron transport simulations. The conventional pre-generated cross section libraries at each temperature point are no longer feasible for detailed temperature distributions because of tremendous memory footprint. Hence, different on-the-fly Doppler broadening methods have been proposed for resolved resonance energy region and thermal energy region, such as the interpolation, regress model fitting, multipole representation (MPR), target motion sampling (TMS) and different integration algorithms based on Doppler broadening equation. Compared to resolved resonance energy region and thermal energy region, the representation of cross sections in unresolved resonance region is slightly different. As a result of the limitation of experiment measurements, the overlapped resonances cannot be distinguished from each other and the statistic average resonance parameters are given in the evaluated nuclear data file. In early applications, infinite-dilute pointwise cross sections were obtained from average parameters and were directly used in neutron simulations. However, it was proven that these infinite-dilute pointwise cross sections cannot capture the energy self-shielding effect, thus leading to an overestimate of reaction rates and usually an underestimate of k{sub eff} owing to the increased absorption rates and increased scattering probability to energy below unresolved resonance region where the capture-to-fission ratio is typically higher. This phenomenon is of great importance especially for problems where there is a considerable flux in unresolved resonance region. Then the probability table method was put forward by Levitt to take energy self-shielding effect into account, which provides probability distribution function of cross sections in unresolved resonance region instead of specific cross section values. The probability distribution function is presented in the form of tables and the mean values are equal to the infinite dilution cross sections. To obtain the temperature dependent cross sections in unresolved energy region, there are two different methods. One is to calculate cross sections directly from average resonance parameters and their statistical distributions , which is effective and precise but time-consuming. The other is to interpolate between pre-generated probability table files, which is much more efficient. In this paper, on-the-fly Doppler broadening of cross sections in unresolved resonance regions based on probability tables interpolation was implemented in RMC codes as a supplement to on-the-fly Doppler broadening for resolved resonance energy region and thermal region, which is based on the integration of Doppler broadening equation. The results of criticality calculation and depletion calculation show that on-the-fly Doppler broadening for the whole energy region is accurate and efficient. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2011

BAYESIAN MAGNETOHYDRODYNAMIC SEISMOLOGY OF CORONAL LOOPS

We perform a Bayesian parameter inference in the context of resonantly damped transverse coronal loop oscillations. The forward problem is solved in terms of parametric results for kink waves in one-dimensional flux tubes in the thin tube and thin boundary approximations. For the inverse problem, we adopt a Bayesian approach to infer the most probable values of the relevant parameters, for given observed periods and damping times, and to extract their confidence levels. The posterior probability distribution functions are obtained by means of Markov Chain Monte Carlo simulations, incorporating observed uncertainties in a consistent manner. We find well-localized solutions in the posterior probability distribution functions for two of the three parameters of interest, namely the Alfven travel time and the transverse inhomogeneity length scale. The obtained estimates for the Alfven travel time are consistent with previous inversion results, but the method enables us to additionally constrain the transverse inhomogeneity length scale and to estimate real error bars for each parameter. When observational estimates for the density contrast are used, the method enables us to fully constrain the three parameters of interest. These results can serve to improve our current estimates of unknown physical parameters in coronal loops and to test the assumed theoretical model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

Plasma diffusion in self-consistent fluctuations

The problem of particle diffusion in position space, as a consequence of electromagnetic fluctuations is addressed. Numerical results obtained with a self-consistent hybrid code are presented, and a method to calculate diffusion coefficient in the direction perpendicular to the mean magnetic field is proposed. The diffusion is estimated for two different types of fluctuations. The first type (resulting from an agyrotropic initial setting) is stationary, wide band white noise, and associated to Gaussian probability distribution function for the magnetic fluctuations. The second type (resulting from a Kelvin-Helmholtz instability) is non-stationary, with a power-law spectrum, and a non-Gaussian probability distribution function. The results of the study allow revisiting the question of loading particles of solar wind origin in the Earth magnetosphere.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

The dynamics of the multi-planet system orbiting Kepler-56

Kepler-56 is a multi-planet system containing two coplanar inner planets that are in orbits misaligned with respect to the spin axis of the host star, and an outer planet. Various mechanisms have been proposed to explain the broad distribution of spin-orbit angles among exoplanets, and these theories fall under two broad categories. The first is based on dynamical interactions in a multi-body system, while the other assumes that disk migration is the driving mechanism in planetary configuration and that the star (or disk) is titled with respect to the planetary plane. Here we show that the large observed obliquity of Kepler 56 system is consistent with a dynamical origin. In addition, we use observations by Huber et al. to derive the obliquity's probability distribution function, thus improving the constrained lower limit. The outer planet may be the cause of the inner planets' large obliquities, and we give the probability distribution function of its inclination, which depends on the initial orbital configuration of the planetary system. We show that even in the presence of precise measurement of the true obliquity, one cannot distinguish the initial configurations. Finally we consider the fate of the system as the star continues to evolve beyond the main sequence, and we find that the obliquity of the system will not undergo major variations as the star climbs the red giant branch. We follow the evolution of the system and find that the innermost planet will be engulfed in ∼129 Myr. Furthermore we put an upper limit of ∼155 Myr for the engulfment of the second planet. This corresponds to ∼3% of the current age of the star.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Statistical measurement of the gamma-ray source-count distribution as a function of energy

Statistical properties of photon count maps have recently been proven as a new tool to study the composition of the gamma-ray sky with high precision. We employ the 1-point probability distribution function of six years of Fermi-LAT data to measure the source-count distribution dN/dS and the diffuse components of the high-latitude gamma-ray sky as a function of energy. To that aim, we analyze the gamma-ray emission in five adjacent energy bands between 1 and 171 GeV. It is demonstrated that the source-count distribution as a function of flux is compatible with a broken power law up to energies of ∼50 GeV. The index below the break is between 1.95 and 2.0. For higher energies, a simple power-law fits the data, with an index of 2.2{sub −0.3}{sup +0.7} in the energy band between 50 and 171 GeV. Upper limits on further possible breaks as well as the angular power of unresolved sources are derived. We find that point-source populations probed by this method can explain 83{sub −13}{sup +7}% (81{sub −19}{sup +52}%) of the extragalactic gamma-ray background between 1.04 and 1.99 GeV (50 and 171 GeV). The method has excellent capabilities for constraining the gamma-ray luminosity function and the spectra of unresolved blazars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

UNCERTAINTIES IN GALACTIC CHEMICAL EVOLUTION MODELS

We use a simple one-zone galactic chemical evolution model to quantify the uncertainties generated by the input parameters in numerical predictions for a galaxy with properties similar to those of the Milky Way. We compiled several studies from the literature to gather the current constraints for our simulations regarding the typical value and uncertainty of the following seven basic parameters: the lower and upper mass limits of the stellar initial mass function (IMF), the slope of the high-mass end of the stellar IMF, the slope of the delay-time distribution function of Type Ia supernovae (SNe Ia), the number of SNe Ia per M {sub ⊙} formed, the total stellar mass formed, and the final mass of gas. We derived a probability distribution function to express the range of likely values for every parameter, which were then included in a Monte Carlo code to run several hundred simulations with randomly selected input parameters. This approach enables us to analyze the predicted chemical evolution of 16 elements in a statistical manner by identifying the most probable solutions, along with their 68% and 95% confidence levels. Our results show that the overall uncertainties are shaped by several input parameters that individually contribute at different metallicities, and thus at different galactic ages. The level of uncertainty then depends on the metallicity and is different from one element to another. Among the seven input parameters considered in this work, the slope of the IMF and the number of SNe Ia are currently the two main sources of uncertainty. The thicknesses of the uncertainty bands bounded by the 68% and 95% confidence levels are generally within 0.3 and 0.6 dex, respectively. When looking at the evolution of individual elements as a function of galactic age instead of metallicity, those same thicknesses range from 0.1 to 0.6 dex for the 68% confidence levels and from 0.3 to 1.0 dex for the 95% confidence levels. The uncertainty in our chemical evolution model does not include uncertainties relating to stellar yields, star formation and merger histories, and modeling assumptions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

The rates and time-delay distribution of multiply imaged supernovae behind lensing clusters

Time delays of gravitationally lensed sources can be used to constrain the mass model of a deflector and determine cosmological parameters. We here present an analysis of the time-delay distribution of multiply imaged sources behind 17 strong lensing galaxy clusters with well-calibrated mass models. We find that for time delays less than 1000 days, at z = 3.0, their logarithmic probability distribution functions are well represented by P(log Δt) = 5.3 × 10{sup −4}Δt{sup β-tilde}/M{sub 250}{sup 2β-tilde}, with β-tilde = 0.77, where M{sub 250} is the projected cluster mass inside 250 kpc (in 10{sup 14}M{sub ☉}), and β-tilde is the power-law slope of the distribution. The resultant probability distribution function enables us to estimate the time-delay distribution in a lensing cluster of known mass. For a cluster with M{sub 250} = 2 × 10{sup 14}M{sub ☉}, the fraction of time delays less than 1000 days is approximately 3%. Taking Abell 1689 as an example, its dark halo and brightest galaxies, with central velocity dispersions σ≥500kms{sup −1}, mainly produce large time delays, while galaxy-scale mass clumps are responsible for generating smaller time delays. We estimate the probability of observing multiple images of a supernova in the known images of Abell 1689. A two-component model of estimating the supernova rate is applied in this work. For a magnitude threshold of m{sub AB} = 26.5, the yearly rate of Type Ia (core-collapse) supernovae with time delays less than 1000 days is 0.004±0.002 (0.029±0.001). If the magnitude threshold is lowered to m{sub AB} ∼ 27.0, the rate of core-collapse supernovae suitable for time delay observation is 0.044±0.015 per year.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Role of Arctic Sea Ice in the 2014–2015 Eurasian Warm Winter

Abstract Because of large internal variabilities, the role of Arctic sea ice concentration (SIC) on regional climate cannot be easily separated. This study uses two groups of large ensemble atmospheric model simulations under climatological and observed SIC boundary conditions to investigate the role of SIC in the 2014–2015 December to February Eurasian warm winter (DJF15). It is shown that the SIC has large impact on the probability distribution function of the DJF15 temperature and pressure fields. The anomalous high Barents Sea ice during the 2014–2015 autumn and winter leads to significant shift in the probability distribution function skewness of the DJF15 surface temperature (from −0.13 to −0.48) and the related sea level pressure (from −0.18 to 0.32) that favor more occurrences of warm temperature anomaly and positive North Atlantic Oscillation‐like pattern. This asymmetry is consistent with anomalous forcing in phase with the anomalies of the sea level pressure field.

Xie, Jinbo↗
DOE OSTI2014

Oscillations in probability distributions for stochastic gene expression

The phenomenon of oscillations in probability distribution functions of number of components is found for a model of stochastic gene expression. It takes place in cases of low levels of molecules or strong intracellular noise. The oscillations distinguish between more probable even and less probable odd number of particles. The even-odd symmetry restores as the number of molecules increases with the probability distribution function tending to Poisson distribution. We discuss the possibility of observation of the phenomenon in gene, protein, and mRNA expression experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Statistical analysis and modeling of intermittent transport events in the tokamak scrape-off layer

The turbulence observed in the scrape-off-layer of a tokamak is often characterized by intermittent events of bursty nature, a feature which raises concerns about the prediction of heat loads on the physical boundaries of the device. It appears thus necessary to delve into the statistical properties of turbulent physical fields such as density, electrostatic potential, and temperature, focusing on the mathematical expression of tails of the probability distribution functions. The method followed here is to generate statistical information from time-traces of the plasma density stemming from Braginskii-type fluid simulations and check this against a first-principles theoretical model. The analysis of the numerical simulations indicates that the probability distribution function of the intermittent process contains strong exponential tails, as predicted by the analytical theory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Complex statistics and diffusion in nonlinear disordered particle chains

We investigate dynamically and statistically diffusive motion in a Klein-Gordon particle chain in the presence of disorder. In particular, we examine a low energy (subdiffusive) and a higher energy (self-trapping) case and verify that subdiffusive spreading is always observed. We then carry out a statistical analysis of the motion, in both cases, in the sense of the Central Limit Theorem and present evidence of different chaos behaviors, for various groups of particles. Integrating the equations of motion for times as long as 10{sup 9}, our probability distribution functions always tend to Gaussians and show that the dynamics does not relax onto a quasi-periodic Kolmogorov-Arnold-Moser torus and that diffusion continues to spread chaotically for arbitrarily long times.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A data science approach for analysis and reconstruction of spinodal-like composition fields in irradiated FeCrAl alloys

A statistical method for the analysis of continuously distributed data representative of composition fluctuations in irradiated FeCrAl alloys acquired using Energy Dispersive X-ray Spectroscopy (EDS) method is presented. Using probability distribution functions, direct and cross-covariances between the elemental compositions, the effects of alloy composition and irradiation dose were investigated on the spatial distribution and length scale of composition fluctuations at the nanoscale. We have observed that, for neutron-irradiated FeCrAl alloys, the distribution of Fe and Cr followed a left-skewed and right-skewed distribution, respectively for all (average) alloy compositions and irradiation doses. The analysis also revealed enhanced spatial gradients in the elemental compositions at higher irradiation dose. Direct and cross-covariance estimates of the experimental data were also utilized for reconstruction of composition data through fitting it to a parametric form of the covariance functions. Linear Model of Coregionalization was used to determine the parameters of the covariance functions. Subsequently, a spectral method was utilized for simulating a realization of the alloy compositions. Close correspondence was observed between the experimental and the reconstructed data which was analyzed using probability distribution functions and covariance functions. Composition space of the experimental and reconstructed data and dislocation velocities as a function of applied stress and line directions over the entire composition maps were also examined.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Power Outage Data Informed Resilience Assessment Framework

Catastrophic impacts to power systems due to disruptive events have increased significantly during the last decade. These events highlight the need to develop approaches to assess the resilience of power systems against extreme events. However, the availability of data that capture power system performance during and after disruptive events is scarce. This paper proposes an assessment framework to evaluate the performance aspects of the grid system during extreme outage events using the Environment for Analysis of Geo-Located Energy Information (EAGLE-I) data. EAGLE-I includes information related to the number of impacted customers, duration, and location of power outages in the United States. Statistical analyses were conducted to extract resilient-based outage data and derive probability distribution functions of their impact and recovery characteristics. A list of extreme events is identified based on few predetermined threshold values. Metrics from other power outage assessments were used to measure the characteristics of each event, including impact rate and duration, recovery rate and duration, and impact level. A probability distribution function is obtained for each metric. The obtained results provide a representation of national grid performance during extreme events, which can be applied as a framework to evaluate various resilience enhancement techniques.

EAGLE-I↗
DOE OSTI2012

Propagation of radiation in fluctuating multiscale plasmas. I. Kinetic theory

A theory for propagation of radiation in a large scale plasma with small scale fluctuations is developed using a kinetic description in terms of the probability distribution function of the radiation in space, time, and wavevector space. Large scale effects associated with spatial variations in the plasma density and refractive index of the plasma wave modes and small scale effects such as scattering of radiation by density clumps in fluctuating plasma, spontaneous emission, damping, and mode conversion are included in a multiscale kinetic description of the radiation. Expressions for the Stokes parameters in terms of the probability distribution function of the radiation are used to enable radiation properties such as intensity and polarization to be calculated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Non-Gaussian structure of B-mode polarization after delensing

The B-mode polarization of the cosmic microwave background on large scales has been considered as a probe of gravitational waves from the cosmic inflation. Ongoing and future experiments will, however, suffer from contamination due to the B-modes of non-primordial origins, one of which is the lensing induced B-mode polarization. Subtraction of the lensing B-modes, usually referred to as delensing, will be required for further improvement of detection sensitivity of the gravitational waves. In such experiments, knowledge of statistical properties of the B-modes after delensing is indispensable to likelihood analysis particularly because the lensing B-modes are known to be non-Gaussian. In this paper, we study non-Gaussian structure of the delensed B-modes on large scales, comparing it with that of the lensing B-modes. In particular, we investigate the power spectrum correlation matrix and the probability distribution function (PDF) of the power spectrum amplitude. Assuming an experiment in which the quadratic delensing is an almost optimal method, we find that delensing reduces correlations of the lensing B-mode power spectra between different multipoles, and that the PDF of the power spectrum amplitude is well described as a normal distribution function with a variance larger than that in the case of a Gaussian field. These features are well captured by an analytic model based on the 4th order Edgeworth expansion. As a consequence of the non-Gaussianity, the constraint on the tensor-to-scalar ratio after delensing is degraded within approximately a few percent, which depends on the multipole range included in the analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

MODELING THE Ly α FOREST IN COLLISIONLESS SIMULATIONS

Cosmological hydrodynamic simulations can accurately predict the properties of the intergalactic medium (IGM), but only under the condition of retaining the high spatial resolution necessary to resolve density fluctuations in the IGM. This resolution constraint prohibits simulating large volumes, such as those probed by BOSS and future surveys, like DESI and 4MOST. To overcome this limitation, we present “Iteratively Matched Statistics” (IMS), a novel method to accurately model the Ly α forest with collisionless N -body simulations, where the relevant density fluctuations are unresolved. We use a small-box, high-resolution hydrodynamic simulation to obtain the probability distribution function (PDF) and the power spectrum of the real-space Ly α forest flux. These two statistics are iteratively mapped onto a pseudo-flux field of an N -body simulation, which we construct from the matter density. We demonstrate that our method can reproduce the PDF, line of sight and 3D power spectra of the Ly α forest with good accuracy (7%, 4%, and 7% respectively). We quantify the performance of the commonly used Gaussian smoothing technique and show that it has significantly lower accuracy (20%–80%), especially for N -body simulations with achievable mean inter-particle separations in large-volume simulations. In addition, we show that IMS produces reasonable and smooth spectra, making it a powerful tool for modeling the IGM in large cosmological volumes and for producing realistic “mock” skies for Ly α forest surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Photometric redshifts for the S-PLUS Survey: Is machine learning up to the task?

The Southern Photometric Local Universe Survey (S-PLUS) is a novel project that aims to map the Southern Hemisphere using a twelve filter system, comprising five broad-band SDSS-like filters and seven narrow-band filters optimized for important stellar features in the local universe. In this paper we use the photometry and morphological information from the first S-PLUS data release (S-PLUS DR1) cross-matched to unWISE data and spectroscopic redshifts from Sloan Digital Sky Survey DR15. We explore three different machine learning methods (Gaussian Processes with GPz and two Deep Learning models made with TensorFlow) and compare them with the currently used template-fitting method in the S-PLUS DR1 to address whether machine learning methods can take advantage of the twelve filter system for photometric redshift prediction. Using tests for accuracy for both single-point estimates such as the calculation of the scatter, bias, and outlier fraction, and probability distribution functions (PDFs) such as the Probability Integral Transform (PIT), the Continuous Ranked Probability Score (CRPS) and the Odds distribution, we conclude that a deep-learning method using a combination of a Bayesian Neural Network and a Mixture Density Network offers the most accurate photometric redshifts for the current test sample. In conclusion, it achieves single-point photometric redshifts with scatter (σ NMAD ) of 0.023, normalized bias of -0.001, and outlier fraction of 0.64% for galaxies with r_auto magnitudes between 16 and 21.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

SU-E-T-293: Dosimetric Analysis of Microscopic Disease in SBRT for Lung Cancers

Purpose: To evaluate the dosimetry of microscopic disease (MD) region of lung cancer in stereotactic body radiation therapy (SBRT). Methods: For simplicity, we assume organ moves along one dimension. The probability distribution function of tumor position was calculated according to the breathing cycle. The dose to the MD region was obtained through accumulating the treatment planning system calculated doses at different positions in a breathing cycle. A phantom experiment was then conducted to validate the calculated results using a motion phantom (The CIRS ‘Dynamic’ Thorax Phantom). The simulated breathing pattern used a cos4(x) curve with an amplitude of 10mm. A 3-D conformal 7-field plan with 6X energy was created and the dose was calculated in the average intensity projection (AIP) simulation CT images. Both films (EBT2) and optically stimulated luminescence (OSL) detectors were inserted in the target of the phantom to measure the dose during radiation delivery (Varian Truebeam) and results were compared to planning dose parameters. Results: The Gamma analysis (3%/3mm) between measured dose using EBT2 film and calculated dose using AIP was 80.5%, indicating substantial dosimetric differences. While the Gamma analysis (3%/3mm) between measured dose using EBT2 and accumulated dose using 4D-CT was 98.9%, indicating the necessity of dose accumulation using 4D-CT. The measured doses using OSL and theoretically calculated doses using probability distribution function at the corresponding position were comparable. Conclusion: Use of static dose calculation in the treatment planning system could substantially underestimate the actually delivered dose in the MD region for a moving target. Funding Supported by NSFC, No.81372436.

60 APPLIED LIFE SCIENCES↗