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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Entropy-Bayesian Inversion of Time-Lapse Tomographic GPR data for Monitoring Dielectric Permittivity and Soil Moisture Variations

In this study, we evaluate the possibility of monitoring soil moisture variation using tomographic ground penetrating radar travel time data through Bayesian inversion, which is integrated with entropy memory function and pilot point concepts, as well as efficient sampling approaches. It is critical to accurately estimate soil moisture content and variations in vadose zone studies. Many studies have illustrated the promise and value of GPR tomographic data for estimating soil moisture and associated changes, however, challenges still exist in the inversion of GPR tomographic data in a manner that quantifies input and predictive uncertainty, incorporates multiple data types, handles non-uniqueness and nonlinearity, and honors time-lapse tomograms collected in a series. To address these challenges, we develop a minimum relative entropy (MRE)-Bayesian based inverse modeling framework that non-subjectively defines prior probabilities, incorporates information from multiple sources, and quantifies uncertainty. The framework enables us to estimate dielectric permittivity at pilot point locations distributed within the tomogram, as well as the spatial correlation range. In the inversion framework, MRE is first used to derive prior probability density functions (pdfs) of dielectric permittivity based on prior information obtained from a straight-ray GPR inversion. The probability distributions are then sampled using a Quasi-Monte Carlo (QMC) approach, and the sample sets provide inputs to a sequential Gaussian simulation (SGSIM) algorithm that constructs a highly resolved permittivity/velocity field for evaluation with a curved-ray GPR forward model. The likelihood functions are computed as a function of misfits, and posterior pdfs are constructed using a Gaussian kernel. Inversion of subsequent time-lapse datasets combines the Bayesian estimates from the previous inversion (as a memory function) with new data. The memory function and pilot point design takes advantage of the spatial-temporal correlation of the state variables. We first apply the inversion framework to a static synthetic example and then to a time-lapse GPR tomographic dataset collected during a dynamic experiment conducted at the Hanford Site in Richland, WA. We demonstrate that the MRE-Bayesian inversion enables us to merge various data types, quantify uncertainty, evaluate nonlinear models, and produce more detailed and better resolved estimates than straight-ray based inversion; therefore, it has the potential to improve estimates of inter-wellbore dielectric permittivity and soil moisture content and to monitor their temporal dynamics more accurately.

Bayesian Inversion↗
DOE OSTI2016

Charge transport properties of poly(dA)-poly(dT) DNA in variation of backbone disorder and amplitude of base-pair twisting motion

By using tight binding Hamiltonian model, charge transport properties of poly(dA)-poly(dT) DNA in variation of backbone disorder and amplitude of base-pair twisting motion is studied. The DNA chain used is 32 base pairs long poly(dA)-poly(dT) molecule. The molecule is contacted to electrode at both ends. The influence of environment on charge transport in DNA is modeled as variation of backbone disorder. The twisting motion amplitude is taking into account by assuming that the twisting angle distributes following Gaussian distribution function with zero average and standard deviation proportional to square root of temperature and inversely proportional to the twisting motion frequency. The base-pair twisting motion influences both the onsite energy of the bases and electron hopping constant between bases. The charge transport properties are studied by calculating current using Landauer-Buttiker formula from transmission probabilities which is calculated by transfer matrix methods. The result shows that as the backbone disorder increases, the maximum current decreases. By decreasing the twisting motion frequency, the current increases rapidly at low voltage, but the current increases slower at higher voltage. The threshold voltage can increase or decrease with increasing backbone disorder and increasing twisting frequency.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Measuring distances and reddenings for a billion stars: Toward a 3D dust map from Pan-STARRS 1

We present a method to infer reddenings and distances to stars based only on their broad-band photometry, and show how this method can be used to produce a three-dimensional (3D) dust map of the Galaxy. Our method samples from the full probability density function of distance, reddening, and stellar type for individual stars, as well as the full uncertainty in reddening as a function of distance in the 3D dust map. We incorporate prior knowledge of the distribution of stars in the Galaxy and the detection limits of the survey. For stars in the Pan-STARRS 1 (PS1) 3π survey, we demonstrate that our reddening estimates are unbiased and accurate to ∼0.13 mag in E(B – V) for the typical star. Based on comparisons with mock catalogs, we expect distances for main-sequence stars to be constrained to within ∼20%-60%, although this range can vary, depending on the reddening of the star, the precise stellar type, and its position on the sky. A later paper will present a 3D map of dust over the three quarters of the sky surveyed by PS1. Both the individual stellar inferences and the 3D dust map will enable a wealth of Galactic science in the plane. The method we present is not limited to the passbands of the PS1 survey but may be extended to incorporate photometry from other surveys, such as the Two Micron All Sky Survey, the Sloan Digital Sky Survey (where available), and in the future, LSST and Gaia.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Quantum temporal probabilities in tunneling systems

We study the temporal aspects of quantum tunneling as manifested in time-of-arrival experiments in which the detected particle tunnels through a potential barrier. In particular, we present a general method for constructing temporal probabilities in tunneling systems that (i) defines ‘classical’ time observables for quantum systems and (ii) applies to relativistic particles interacting through quantum fields. We show that the relevant probabilities are defined in terms of specific correlation functions of the quantum field associated with tunneling particles. We construct a probability distribution with respect to the time of particle detection that contains all information about the temporal aspects of the tunneling process. In specific cases, this probability distribution leads to the definition of a delay time that, for parity-symmetric potentials, reduces to the phase time of Bohm and Wigner. We apply our results to piecewise constant potentials, by deriving the appropriate junction conditions on the points of discontinuity. For the double square potential, in particular, we demonstrate the existence of (at least) two physically relevant time parameters, the delay time and a decay rate that describes the escape of particles trapped in the inter-barrier region. Finally, we propose a resolution to the paradox of apparent superluminal velocities for tunneling particles. We demonstrate that the idea of faster-than-light speeds in tunneling follows from an inadmissible use of classical reasoning in the description of quantum systems. -- Highlights: •Present a general methodology for deriving temporal probabilities in tunneling systems. •Treatment applies to relativistic particles interacting through quantum fields. •Derive a new expression for tunneling time. •Identify new time parameters relevant to tunneling. •Propose a resolution of the superluminality paradox in tunneling.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Bayesian reconstruction of anisotropic flow fluctuations at fixed impact parameter

The cumulants of the distribution of anisotropic flow are measured accurately in Pb+Pb collisions at the LHC as a function of centrality classifiers (charged multiplicity and/or transverse energy). Using Bayesian inference, we reconstruct from these measurements the probability distribution of anisotropic flow in the ``theorists' frame'' where the impact parameter has a fixed magnitude and orientation, up to ∼70% centrality. The variation of flow fluctuations with impact parameter displays direct evidence of viscous damping, which is larger for higher Fourier harmonics, in line with expectations from hydrodynamics. We use intensive measures of non-Gaussian flow fluctuations, which have reduced dependence on centrality. Here, we infer from ATLAS data the magnitude of these intensive non-Gaussianities in each Fourier harmonic. They provide data-driven estimates of response coefficients to initial anisotropies, without resorting to any specific microscopic model of initial conditions. These estimates agree with viscous hydrodynamic calculations.

Bayesian methods↗
DOE OSTI2011

Statistical Models of Power-law Distributions in Homogeneous Plasmas

A variety of in-situ measurements in space plasmas point out to an intermittent formation of distribution functions with elongated tails and power-law at high energies. Power-laws form ubiquitous signature of many complex systems, plasma being a good example of a non-Boltzmann behavior for distribution functions of energetic particles. Particles, which either undergo mutual collisions or are scattered in phase space by electromagnetic fluctuations, exhibit statistical properties, which are determined by the transition probability density function of a single interaction, while their non-asymptotic evolution may determine the observed high-energy populations. It is shown that relaxation of the Brownian motion assumptions leads to non-analytical characteristic functions and to generalization of the Fokker-Planck equation with fractional derivatives that result in power law solutions parameterized by the probability density function.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

Combined effects of gas pressure and exciting frequency on electron energy distribution functions in hydrogen capacitively coupled plasmas

The combined effects of the variation of hydrogen pressure (40-400 mTorr) and exciting frequency (13.56-50 MHz) on the electron energy probability function (EEPF) and other plasma parameters in capacitively coupled hydrogen H{sub 2} discharge at fixed discharge voltage were investigated using rf-compensated Langmuir probe. At a fixed exciting frequency of 13.56 MHz, the EEPF evolved from Maxwellian-like distribution to a bi-Maxwellian distribution when the H{sub 2} pressure increased, possibly due to efficient vibrational excitation. The electron density largely increased to a peak value and then decreased with the increase of H{sub 2} pressure. Meanwhile, the electron temperature and plasma potential significantly decrease and reaching a minimum at 120 mTorr beyond, which saturated or slightly increases. On the other hand, the dissipated power and electron density markedly increased with increasing the exciting frequency at fixed H{sub 2} pressure and voltage. The electron temperatures negligibly dependent on the driving frequency. The EEPFs at low pressure 60 mTorr resemble Maxwellian-like distribution and evolve into a bi-Maxwellian type as frequency increased, due to a collisonless (stochastic) sheath-heating in the very high frequency regime, while the EEPF at hydrogen pressure {>=}120 mTorr retained a bi-Maxwellian-type distribution irrespective of the driving frequency. Such evolution of the EEPFs shape with the driving frequency and hydrogen pressure has been discussed on the basis of electron diffusion processes and low threshold-energy inelastic collision processes taking place in the discharge. The ratio of stochastic power to bulk power heating ratio is dependent on the hydrogen pressure while it is independent on the driving frequency.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Impact of large-scale dynamics on the microphysical properties of midlatitude cirrus

In situ microphysical observations 3 of mid-latitude cirrus collected during the Department of Energy Small Particles in Cirrus (SPAR-TICUS) field campaign are combined with an atmospheric state classification for the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site to understand statistical relationships between cirrus microphysics and the large-scale meteorology. The atmospheric state classification is informed about the large-scale meteorology and state of cloudiness at the ARM SGP site by combining ECMWF ERA-Interim reanalysis data with 14 years of continuous observations from the millimeter-wavelength cloud radar. Almost half of the cirrus cloud occurrences in the vicinity of the ARM SGP site during SPARTICUS can be explained by three distinct synoptic condi- tions, namely upper-level ridges, mid-latitude cyclones with frontal systems and subtropical flows. Probability density functions (PDFs) of cirrus micro- physical properties such as particle size distributions (PSDs), ice number con- centrations and ice water content (IWC) are examined and exhibit striking differences among the different synoptic regimes. Generally, narrower PSDs with lower IWC but higher ice number concentrations are found in cirrus sam- pled in upper-level ridges whereas cirrus sampled in subtropical flows, fronts and aged anvils show broader PSDs with considerably lower ice number con- centrations but higher IWC. Despite striking contrasts in the cirrus micro- physics for different large-scale environments, the PDFs of vertical velocity are not different, suggesting that vertical velocity PDFs are a poor predic-tor for explaining the microphysical variability in cirrus. Instead, cirrus mi- crophysical contrasts may be driven by differences in ice supersaturations or aerosols.

Muhlbauer, Andreas↗
DOE OSTI2019

Bayesian strategies for uncertainty quantification of the thermodynamic properties of materials

Reliable models of the thermodynamic properties of materials are critical for industrially relevant applications that require a good understanding of equilibrium phase diagrams, thermal and chemical transport, and microstructure evolution. The goal of thermodynamic models is to capture data from both experimental and computational studies and then make reliable predictions when extrapolating to new regions of parameter space. These predictions will be impacted by artifacts present in real data sets such as outliers, systematic errors, and unreliable or missing uncertainty bounds. Such issues increase the probability of the thermodynamic model producing erroneous predictions. We present a Bayesian framework for the selection, calibration, and quantification of uncertainty of thermodynamic property models. The modular framework addresses numerous concerns regarding thermodynamic models including thermodynamic consistency, robustness to outliers, and systematic errors by the use of hyperparameter weightings and robust Likelihood and Prior distribution choices. Furthermore, the framework’s inherent transparency (e.g. our choice of probability functions and associated parameters) enables insights into the complex process of thermodynamic assessment. We introduce these concepts through examples where the true property model is known. Additionally, we demonstrate the utility of the framework through the creation of a property model from a large set of experimental specific heat and enthalpy measurements of Hafnium metal from 0 to 4900K.

36 MATERIALS SCIENCE↗
DOE OSTI2015

External control of electron energy distributions in a dual tandem inductively coupled plasma

The control of electron energy probability functions (EEPFs) in low pressure partially ionized plasmas is typically accomplished through the format of the applied power. For example, through the use of pulse power, the EEPF can be modulated to produce shapes not possible under continuous wave excitation. This technique uses internal control. In this paper, we discuss a method for external control of EEPFs by transport of electrons between separately powered inductively coupled plasmas (ICPs). The reactor incorporates dual ICP sources (main and auxiliary) in a tandem geometry whose plasma volumes are separated by a grid. The auxiliary ICP is continuously powered while the main ICP is pulsed. Langmuir probe measurements of the EEPFs during the afterglow of the main ICP suggests that transport of hot electrons from the auxiliary plasma provided what is effectively an external source of energetic electrons. The tail of the EEPF and bulk electron temperature were then elevated in the afterglow of the main ICP by this external source of power. Results from a computer simulation for the evolution of the EEPFs concur with measured trends.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Scattering using real-time path integrals

Background: Path integrals are a powerful tool for solving problems in quantum theory that are not amenable to a treatment by perturbation theory. Most path integral computations require an analytic continuation to imaginary time. While imaginary time treatments of scattering are possible, imaginary time is not a natural framework for treating scattering problems. More importantly, quantum algorithms for calculating path integrals require real-time evolution. Purpose: Here, we test a recently introduced method for performing direct calculations of scattering observables using real-time path integrals in order to understand the challenges facing real-time path integral calculations of scattering observables. Method: The computations are based on a new interpretation of the path integral as the expectation value of a potential functional on cylinder sets of continuous paths with respect to a complex probability distribution. The method can in principle be applied to arbitrary short-range potentials. Results: The method is applied to compute matrix elements of Møller wave operators applied to narrow wave packets. These are used to calculate half-shell sharp-momentum transition matrix elements for one-dimensional potential scattering. The calculations for half-shell transition operator matrix elements converge to the numerical solution of the Lippmann-Schwinger equation. Conclusions: This work presents a proof in principle that scattering observables can be computed using real-time Feynman path integrals. While the computational method is not efficient, it can be improved. It provides a laboratory for studying quantum computational algorithms that are applicable to scattering problems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

PULSAR SIGNAL DENOISING METHOD BASED ON LAPLACE DISTRIBUTION IN NO-SUBSAMPLING WAVELET PACKET DOMAIN

In order to improve the denoising effect of the pulsar signal, a new denoising method is proposed in the no-subsampling wavelet packet domain based on the local Laplace prior model. First, we count the true noise-free pulsar signal’s wavelet packet coefficient distribution characteristics and construct the true signal wavelet packet coefficients’ Laplace probability density function model. Then, we estimate the denosied wavelet packet coefficients by using the noisy pulsar wavelet coefficients based on maximum a posteriori criteria. Finally, we obtain the denoisied pulsar signal through no-subsampling wavelet packet reconstruction of the estimated coefficients. The experimental results show that the proposed method performs better when calculating the pulsar time of arrival than the translation-invariant wavelet denoising method.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Defense strategies for infrastructures with multiple systems of components

In several critical infrastructures correlations between the constituent systems represent certain vulnerabilities: disruptions to one may propagate to others and possibly to the entire infrastructure. The correlations between the systems are characterized in two ways in this paper: (i) the aggregate failure correlation function specifies the conditional failure probability of the infrastructure given the failure of an individual system, and (ii) the pairwise correlation function between two systems specifies the failure probability of one system given the failure of the other. The survival probabilities of individual systems satisfy firstorder differential conditions that generalize the contest success functions and statistical independence conditions. We formulate a problem of ensuring the resilience of an infrastructure as a game between the provider and attacker; their utility functions are sums of infrastructure survival probability terms and cost terms, both expressed in terms of the numbers of system components attacked and reinforced. We derive Nash Equilibrium conditions and sensitivity functions that highlight the dependence of infrastructure resilience on the cost terms, correlation functions, and individual system survival probabilities. We apply these results to models of distributed cloud computing and energy grid infrastructures.

Rao, Nageswara S↗
DOE OSTI2022

Estimating Cosmological Constraints from Galaxy Cluster Abundance using Simulation-Based Inference

Inferring the values and uncertainties of cosmological parameters in a cosmology model is of paramount importance for modern cosmic observations. In this paper, we use the simulation-based inference (SBI) approach to estimate cosmological constraints from a simplified galaxy cluster observation analysis. Using data generated from the Quijote simulation suite and analytical models, we train a machine learning algorithm to learn the probability function between cosmological parameters and the possible galaxy cluster observables. The posterior distribution of the cosmological parameters at a given observation is then obtained by sampling the predictions from the trained algorithm. Our results show that the SBI method can successfully recover the truth values of the cosmological parameters within the 2σ limit for this simplified galaxy cluster analysis, and acquires similar posterior constraints obtained with a likelihood-based Markov Chain Monte Carlo method, the current state-of the-art method used in similar cosmological studies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Bayesian Integration of multiscale environmental data

The software is designed for efficiently integrating large-size of multi-scale environmental data using the Bayesian framework. Suppose we need to estimate the spatial distribution of variable X with high spatial resolution. The available data include (1) direct measurements Z of the unknowns with high resolution in a subset of the spatial domain (small spatial coverage), (2) measurements C of the unknowns at the median scale, and (3) measurements A of the unknowns at the coarsest scale but with large spatial coverage. The goal is to estimate the unknowns at the fine grids by conditioning to all the available data. We first consider all the unknowns as random variables and estimate conditional probability distribution of those variables by conditioning to the limited high-resolution observations (Z). We then treat the estimated probability distribution as the prior distribution. Within the Bayesian framework, we combine the median and large-scale measurements (C and A) through likelihood functions. Since we assume that all the relevant multivariate distributions are Gaussian, the resulting posterior distribution is a multivariate Gaussian distribution. The developed software provides numerical solutions of the posterior probability distribution. The software can be extended in several different ways to solve more general multi-scale data integration problems.

CHEN, JINSONG↗
DOE OSTI2016

Bayesian Integration of multiscale environmental data

The software is designed for efficiently integrating large-size of multi-scale environmental data using the Bayesian framework. Suppose we need to estimate the spatial distribution of variable X with high spatial resolution. The available data include (1) direct measurements Z of the unknowns with high resolution in a subset of the spatial domain (small spatial coverage), (2) measurements C of the unknowns at the median scale, and (3) measurements A of the unknowns at the coarsest scale but with large spatial coverage. The goal is to estimate the unknowns at the fine grids by conditioning to all the available data. We first consider all the unknowns as random variables and estimate conditional probability distribution of those variables by conditioning to the limited high-resolution observations (Z). We then treat the estimated probability distribution as the prior distribution. Within the Bayesian framework, we combine the median and large-scale measurements (C and A) through likelihood functions. Since we assume that all the relevant multivariate distributions are Gaussian, the resulting posterior distribution is a multivariate Gaussian distribution. The developed software provides numerical solutions of the posterior probability distribution. The software can be extended in several different ways to solve more general multi-scale data integration problems.

CHEN, JINSONG↗
DOE OSTI2012

Orbital-selective magnetism in the spin-ladder iron selenides Ba[subscript 1;#8722;x]K[subscript x]Fe[subscript 2]Se[subscript 3]

Here we show that the 2.80(8){mu}{sub B} Fe{sup -1} block antiferromagnetic order of BaFe{sub 2}Se{sub 3} transforms into stripe antiferromagnetic order in KFe{sub 2}Se{sub 3} with a decrease in moment to 2.1(1){mu}{sub B} Fe{sup -1}. This reduction is larger than expected from the change in electron count from Ba{sup 2+} to K{sup +}, and occurs with the loss of the displacements of Fe atoms from ideal positions in the ladders, as found by neutron pair distribution function analysis. Intermediate compositions remain insulating, and magnetic susceptibility measurements show a suppression of magnetic order and probable formation of a spin glass. Together, these results imply an orbital-dependent selection of magnetic versus bonded behavior, driven by relative bandwidths and fillings.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗