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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 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↗
NASA NTRS1976

Test particle propagation in magnetostatic turbulence. 1. Failure of the diffusion approximation

The equation which governs the quasi-linear approximation to the ensemble and gyro-phase averaged one-body probability distribution function is constructed from first principles. This derived equation is subjected to a thorough investigation in order to calculate the possible limitations of the quasi-linear approximation. It is shown that the reduction of this equation to a standard diffusion equation in the Markovian limit can be accomplished through the application of the adiabatic approximation. A numerical solution of the standard diffusion equation in the Markovian limit is obtained for the narrow parallel beam injection. Comparison of the diabatic and adiabatic results explicitly demonstrates the failure of the Markovian description of the probability distribution function. Through the use of a linear time-scale extension the failure of the adiabatic approximation, which leads to the Markovian limit, is shown to be due to mixing of the relaxation and interaction time scales in the presence of the strong mean field.

Klimas, A. J.↗
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↗
DOE OSTI2023

Bayesian Monte Carlo Evaluation of Imperfect (n, 233 U) Data and Model

Conventional nuclear data evaluation methods using generalized linear least squares make the following assumptions: prior and posterior probability distribution functions (PDFs) of all model parameters and data are normal (Gaussian); the linear approximation is sufficiently accurate to minimize the cost function (even for nonlinear models); the model (e.g., of neutron cross section) and experimental data (including covariance data) are without defect and prior PDFs of parameters and measured data are known perfectly. Neglect of covariance between model parameters and measured data in conventional evaluations contributes to imperfections. These assumptions are inherent to the generalized linear least squares minimization method commonly used for resolved resonance region neutron cross section evaluations but are often not justified due to the presence of non-normal PDFs, nonlinear models (e.g., R-matrix formalism), and inherent imperfections in data and models (e.g., imperfect covariance data). Here, these assumptions are removed in a mathematical framework of Bayes’ theorem, which is implemented using the Metropolis-Hastings Monte Carlo method. Most importantly, new parameters are introduced to parameterize discrepancies between the theoretical model and measured data to quantify judgement about discrepancies or imperfections in a reproducible manner. An evaluation of 233U in the eV region using the ENDF-B/VIII.0 library and transmission data (Guber et al.) is presented, and posterior parameters are compared to those obtained by conventional evaluation methods. This example illustrates the effects of removing the most harmful assumption: that of model-data perfection.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

The azimuthal dependence of outflows and accretion detected using O vi absorption

We report a bimodality in the azimuthal angle (Φ) distribution of gas around galaxies traced by O vi absorption. We present the mean Φ probability distribution function of 29 Hubble Space Telescope-imaged O vi absorbing (EW > 0.1 Å) and 24 non-absorbing (EW < 0.1 Å) isolated galaxies (0.08 < z < 0.67) within ∼200 kpc of background quasars. We show that equivalent width (EW) is anti-correlated with impact parameter and O vi covering fraction decreases from 80% within 50 kpc to 33% at 200 kpc. The presence of O vi absorption is azimuthally dependent and occurs between ±10°–20° of the galaxy projected major axis and within ±30° of the projected minor axis. We find higher EWs along the projected minor axis with weaker EWs along the project major axis. Highly inclined galaxies have the lowest covering fractions due to minimized outflow/inflow cross-section geometry. Absorbing galaxies also have bluer colors while non-absorbers have redder colors, suggesting that star formation is a key driver in the O vi detection rate. O vi surrounding blue galaxies exists primarily along the projected minor axis with wide opening angles while O vi surrounding red galaxies exists primarily along the projected major axis with smaller opening angles, which may explain why absorption around red galaxies is less frequently detected. Our results are consistent with a circumgalactic medium (CGM) originating from major axis-fed inflows/recycled gas and from minor axis-driven outflows. Non-detected O vi occurs between Φ = 20°–60°, suggesting that O vi is not mixed throughout the CGM and remains confined within the outflows and the disk-plane. We find low O vi covering fractions within ±10{sup ∘} of the projected major axis, suggesting that cool dense gas resides in a narrow planer geometry surrounded by diffuse O vi gas.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Ubiquity of Benford's law and emergence of the reciprocal distribution

In this paper, we apply the Law of Total Probability to the construction of scale-invariant probability distribution functions (pdf's), and require that probability measures be dimensionless and unitless under a continuous change of scales. If the scale-change distribution function is scale invariant then the constructed distribution will also be scale invariant. Repeated application of this construction on an arbitrary set of (normalizable) pdf's results again in scale-invariant distributions. The invariant function of this procedure is given uniquely by the reciprocal distribution, suggesting a kind of universality. Finally, we separately demonstrate that the reciprocal distribution results uniquely from requiring maximum entropy for size-class distributions with uniform bin sizes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

SU-F-T-129: Impact of Radial Fluctuations in RBE for Therapeutic Proton Beams

Purpose: To evaluate the off axis relative biological effectiveness (RBE) for actively scanned proton beams and determine if a constant radial RBE can be assumed. Methods: The PHITS Monte Carlo code paired with a microscopic analytical function was used to determine probability distribution functions of the lineal energy in 0.3µm diameter spheres throughout a water phantom. Twenty million primary protons were simulated for a 0.6cm diameter pencil beam. Beam energies corresponding to Bragg Peak depths of 50, 100, 150, 200, 250, and 300mm were used and evaluated transversely every millimeter and radially for annuli of 1.0, 2.0, 3.0, 3.2, 3.4, 3.6, 4.0, 5.0, 10.0, 15.0, 20.0 and 25.0mm outer radius. The acquired probability distributions were reduced to dose-mean lineal energies and applied to the modified microdosimetric kinetic model, for human submandibular gland (HSG) cells, to calculate relative biological effectiveness (RBE) compared to 60Co beams at the 10% survival threshold. Results: RBE was generally seen to increase as distance from the central axis (CAX) increased. However, this increase was only seen in low dose regions and its overall effects on the transverse biological dose remains low. In the entrance region of the phantom (10mm depth), minimum and maximum calculated RBEs varied between 15.22 and 18.88% for different energies. At the Bragg peak, this difference ranged from 3.15 to 26.77%. Despite these rather large variations the dose-weighted RBE and the CAX RBE varied by less than 0.14% at 10mm depth and less than 0.16% at the Bragg peak. Similarly small variations were found at all depths proximal of the Bragg peak. Conclusion: Although proton RBE does vary radially, its overall effect on biological dose is minimal and the use of a radially constant RBE in treatment planning for scanned proton beams would not produce large errors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-H-BRA-09: Application of a Modified Microdosimetric-Kinetic Model to Analyze Relative Biological Effectiveness of Ions Relevant to Light Ion Therapy Using the Particle Heavy Ion Transport System

Purpose: To evaluate the dose and biological effectiveness of various ions that could potentially be used for actively scanned particle therapy. Methods: The PHITS Monte Carlo code paired with a microscopic analytical function was used to determine probability distribution functions of the lineal energy in 0.3µm diameter spheres throughout a water phantom. Twenty million primary particles for 1H beams and ten million particles for 4He, 7Li, 10B, 12C, 14N, 16O, and 20Ne were simulated for 0.6cm diameter pencil beams. Beam energies corresponding to Bragg peak depths of 50, 100, 150, 200, 250, and 300mm were used and evaluated transversely every millimeter and radially in annuli with outer radius of 1.0, 2.0, 3.0, 3.2, 3.4, 3.6, 4.0, 5.0, 10.0, 15.0, 20.0 and 25.0mm. The acquired probability distributions were reduced to dose-mean lineal energies and applied to the modified microdosimetric kinetic model for five different cell types to calculate relative biological effectiveness (RBE) compared to 60Co beams at the 10% survival threshold. The product of the calculated RBEs and the simulated physical dose was taken to create biological dose and comparisons were then made between the various ions. Results: Transversely, the 10B beam was seen to minimize relative biological dose in both the constant and accelerated dose change regions, proximal to the Bragg Peak, for all beams traveling greater than 50mm. For the 50mm beam, 7Li was seen to provide the most optimal biological dose profile. Radially small fluctuations (<4.2%) were seen in RBE while physical dose was greater than 1% for all beams. Conclusion: Even with the growing usage of 12C, it may not be the most optimal ion in all clinical situations. Boron was calculated to have slightly enhanced RBE characteristics, leading to lower relative biological doses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A functional global sensitivity measure and efficient reliability sensitivity analysis with respect to statistical parameters

Sensitivity analysis and reliability assessment are two important aspects of structural and system safety. Epistemic uncertainty with respect to probabilistic model of input parameters due to lack of knowledge is present in many scarce-data applications and complicates the characterization of uncertainty in model response. In this article, we present two importance measures to evaluate the impact of distribution parameters on the probability distribution function (PDF) of the output and the failure probability. The epistemic uncertainty associated with the distribution parameters is modeled as random variables. Additionally, a modified extended polynomial chaos expansion (MEPCE) approach is introduced in which aleatory and epistemic random variables are modeled and propagated simultaneously while allowing the separate assessment for any single epistemic variable. A MEPCE-based kernel density estimation (KDE) construction provides a composite map from each epistemic variable to the response PDF. The functional global sensitivity index of the PDF with respect to the distribution parameters is thus derived, as a function of output, which is both more informative and more efficient than standard scalar sensitivity measures. Reliability sensitivity indices can be readily evaluated by integrating the global sensitivity index function over the failure zone. Three illustrative examples are used to demonstrate the proposed methodology.

42 ENGINEERING↗
NASA NTRS1994

A 'skewed' lognormal approximation to the probablility distribution function of the large-scale density field

I propose a method to fit the probability distribution function (PDF) of the large-scale density field rho, motivated by a Lagrangian version of the continuity equation. It consists in applying the Edgeworth expansion to the quantity Phi identical with log rho - mean value of log rho. The method is tested on the matter particle distribution in two cold dark matter N-body simulations of different physical sizes to cover a large dynamic range. It is seen to be very efficient, even in the nonlinear regime, and may thus be used as an analytical tool to study the effect on the PDF of the transition between the weakly nonlinear regime and the highly nonlinear regime.

Colombi, S.↗
DOE OSTI2022

Fragility Functions Resource Report: Documented Sources for Electricity and Water Resilience Valuation

Fragility curves provide the vulnerability between hazard intensity and an asset. Federal installations may include many different electricity and water infrastructure types (or assets) such as generators, wind turbines, solar PV, switch yards, substations and power lines as well as water distribution systems that could be affected by different hazards. The vulnerability of each asset is a function of its age, type of materials and maintenance. In addition, the vulnerability changes with the hazards intensity and has a probability distribution function associated with it. The fragility functions are used in conjunction with hazard probability and consequence valuations to determine the values at risk for examination of investment grade analyses of alternative mitigation strategies. This document provides examples of fragility functions and links to their sources for different electricity and water infrastructure assets by hazard type.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

CHARACTERIZATION OF THE DISTRIBUTION OF THE Ly{alpha} EMITTERS IN THE 53W002 FIELD AT z = 2.4

We present the results of our wide-field narrowband imaging of the field around the radio galaxy 53W002 at z = 2.390 with Subaru/Suprime-Cam. A custom-made filter, NB413, centered at 4140 A with a width of 83 A is used to observe the 31' Multiplication-Sign 24' area around the radio galaxy. We detected 204 Ly{alpha} emitters (LAEs) at z = 2.4 with a rest-frame equivalent width larger than 25 A to the depth of 26 AB mag (in NB413). The entire LAE population in the 53W002 field has an average number density and distributions of equivalent width and size that are similar to those of other fields at z {approx} 2. We identify a significant high-density region (53W002F-HDR) that spreads over Almost-Equal-To 5' Multiplication-Sign 4' near 53W002, where the LAE number density is nearly four times as large as the average of the entire field. Using the probability distribution function of density fluctuation, we evaluate the rareness probability of 53W002F-HDR to be 0.9{sup +2.4} {sub -0.62}%, which corresponds to a moderately rich structure. No notable environmental dependency at the comoving scale of 10 Mpc is found for the distributions of the Ly{alpha} equivalent width and luminosity in the field. We also detected four Ly{alpha} blobs, one of which is newly discovered. They are all located in the rims of high-density regions. The biased location and unique morphologies in Ly{alpha} suggest that galaxy interaction plays a key role in their formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

THRESHOLD PROBABILITY FUNCTIONS AND THERMAL INHOMOGENEITIES IN THE Ly{alpha} FOREST

We introduce to astrophysics the threshold probability functions S{sub 2}, C{sub 2}, and D{sub 2} first derived by Torquato et al., which effectively samples the flux probability distribution function (PDF) of the Ly{alpha} forest at different spatial scales. These statistics are tested on mock Ly{alpha} forest spectra based on various toy models for He II reionization, with homogeneous models with various temperature-density relations as well as models with temperature inhomogeneities. These mock samples have systematics and noise added to simulate the latest Sloan Digital Sky Survey Data Release 7 (SDSS DR7) data. We find that the flux PDF from SDSS DR7 can be used to constrain the temperature-density relation {gamma} (where T{proportional_to}(1 + {Delta}){sup {gamma}-1}) of the intergalactic medium (IGM) at z = 2.5 to a precision of {Delta}{gamma} = 0.2 at {approx}4{sigma} confidence. The flux PDF is degenerate to temperature inhomogeneities in the IGM arising from He II reionization, but we find S{sub 2} can detect these inhomogeneities at {approx}3{sigma}, with the assumption that the flux continuum of the Ly{alpha} forest can be determined to 9% accuracy, approximately the error from current fitting methods. If the flux continuum can be determined to 3% accuracy, then S{sub 2} is capable of constraining the characteristic scale of temperature inhomogeneities, with {approx}4{sigma} differentiation between toy models with hot bubble radii of 50 h{sup -1} Mpc and 25 h{sup -1} Mpc.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1985

Cosmological density fluctuations and large-scale structure From N-point correlation functions to the probability distribution

Knowledge of N-point correlation functions for all N allows one to invert and obtain the probability distribution of mass fluctuations in a fixed volume. The hierarchical sequence of higher order is applied to correlations with dimensionless amplitudes suggested by the BBGKY equations. The resulting distribution is significantly non-Gaussian, even for quite small mean square fluctuations. The qualitative and to some degree quantitative results are to a large degree independent of the exact sequence of amplitudes. An ensemble of such models compared with N-body simulations fails in detail to account for the low-density frequency distribution.

Fry, J. N.↗
DOE OSTI2012

Propagation of data error and parametric sensitivity in computable general equilibrium model.

While computable general equilibrium (CGE) models are a well-established tool in economic analyses, it is often difficult to disentangle the effects of policies of interest from that of the assumptions made regarding the underlying calibration data and model parameters. To characterize the behavior of a CGE model of carbon output with respect to two of these assumptions, we perform a large-scale Monte Carlo experiment to examine its sensitivity to base year calibration data and elasticity of substitution parameters in the absence of a policy change. By examining a variety of output variables at different levels of economic and geographic aggregation, we assess how these forms of uncertainty impact the conclusions that can be drawn from the model simulations. We find greater sensitivity to uncertainty in the elasticity of substitution parameters than to uncertainty in the base-year data as the projection period increases. While many model simulations were conducted to generate large output samples, we find that few are required to capture the mean model response of the variables tested. However, characterizing standard errors and empirical probability distribution functions is not possible without a large number of simulations.

99 GENERAL AND MISCELLANEOUS↗