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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Effect of helium on spatial plasma parameters in low pressure argon-helium plasma

Spatial distributions of the electron energy probability function were measured using a Langmuir probe in side-type argon-helium inductively coupled plasma. Collisional dominated electron heating and a concave shape of plasma density profile were observed at 10 mTorr pure argon. As the helium proportion increased, the electron heating and density profile changed to collisionless dominated heating and a convex shape respectively, and the same tendency was shown when the pressure decreased in the pure argon plasma. These changes were due to the decrease in the e-n collision frequency and the expansion of the electron power dissipation region.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Simultaneous direct measurements of concentration and velocity in the Richtmyer–Meshkov instability

The Richtmyer–Meshkov instability (RMI) is experimentally investigated in a vertical shock tube using a broadband initial condition imposed on an interface between a helium–acetone mixture and argon (Atwood number $A\approx 0.7$ ). In the present work, a shear layer is introduced at the interface to serve as a statistically repeatable, broadband initial condition to the RMI, and the density interface is accelerated by either an $M=1.6$ or $M=2.2$ planar shock wave. The development of the ensuing mixing layer is investigated using simultaneous planar laser-induced fluorescence (PLIF) and particle image velocimetry (PIV). PLIF images are processed to reveal the light-gas mole fraction, while PIV particle image pairs yield corresponding two-component planar velocity results. Field structure and distribution are explored through probability density functions (PDFs), and a decomposition is performed on concentration and velocity results to obtain a mean flow field and define fluctuations. Simultaneous concentration and velocity field measurements allow – for the first time in this regime – experimentally determined turbulence quantities such as Reynolds stresses, turbulent mass-flux velocities and turbulent kinetic energy to be obtained. We show that by the latest times the mixing layer has passed the turbulent threshold, and there is evidence of turbulent mixing occurring sooner for the higher Mach number case. Interface measurements show nonlinear growth with a power-law fit to the thickness data, and that integral measurements of mixing layer thickness are proportional to threshold measurements. Spectral analysis demonstrates the emergence of an inertial range with a slope ${\sim}k^{-5/3}$ when considering both density and velocity effects in planar turbulent kinetic energy (TKE) measurements.

Mechanics↗
DOE OSTI2026

Continuum radiated power density $(P^{\textrm{C}}_{\textrm{rad}})$ and effective charge $(Z_{\textrm{eff}})$ estimates from multi-energy photon-counting measurements

Multi-energy soft x-ray pinhole cameras have been designed, built, calibrated, and operated at Madison symmetric Torus, Alcator C-Mod, and more recently at Tokamak a configuration variable and Tungsten Environment in Steady-State Tokamak (WEST), to measure plasma emission across multiple energy ranges. Here we describe a new methodology to estimate the local continuum radiated power density and the plasma effective charge (Z eff ) directly from photon-counting measurements of the line-free continuum emission (Bremsstrahlung and Radiative Recombination) in several energy bands between 11 and 18 keV. This capability is particularly valuable for confinement systems using metal plasma-facing components, where x-ray losses from interactions with the sputtered wall can represent a significant fraction of the total radiated power (P rad ). The approach leverages a well-characterized detector responsivity, modeled by a complementary error function, and interprets the differential multi-energy measurements between adjacent energy levels through the probability density function of a Gaussian distribution. The implementation of this diagnostic technique is currently under development on the WEST tokamak, aiming at the goal of providing real-time P rad and Z eff measurements during long-pulse operation (up to 1000 s) in the 2026 campaign.

WEST↗
DOE OSTI2017

Self-learning Monte Carlo method: Continuous-time algorithm

The recently introduced self-learning Monte Carlo method is a general-purpose numerical method that speeds up Monte Carlo simulations by training an effective model to propose uncorrelated configurations in the Markov chain. We implement this method in the framework of a continuous-time Monte Carlo method with an auxiliary field in quantum impurity models. We introduce and train a diagram generating function (DGF) to model the probability distribution of auxiliary field configurations in continuous imaginary time, at all orders of diagrammatic expansion. Furthermore, by using DGF to propose global moves in configuration space, we show that the self-learning continuous-time Monte Carlo method can significantly reduce the computational complexity of the simulation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

Enhancing Privacy in Participatory Sensing Applications with Multidimensional Data

Participatory sensing applications rely on individuals to share local and personal data with others to produce aggregated models and knowledge. In this setting, privacy is an important consideration, and lack of privacy could discourage widespread adoption of many exciting applications. We present a privacy-preserving participatory sensing scheme for multidimensional data which uses negative surveys. Multidimensional data, such as vectors of attributes that include location and environment fields, pose a particular challenge for privacy protection and are common in participatory sensing applications. When reporting data in a negative survey, an individual participant randomly selects a value from the set complement of the sensed data value, once for each dimension, and returns the negative values to a central collection server. Using algorithms described in this paper, the server can reconstruct the probability density functions of the original distributions of sensed values, without knowing the participants actual data. As a consequence, complicated encryption and key management schemes are avoided, conserving energy. We study trade-offs between accuracy and privacy, and their relationships to the number of dimensions, categories, and participants. We introduce dimensional adjustment, a method that reduces the magnification of error associated with earlier work. Two simulation scenarios illustrate how the approach can protect the privacy of a participant's multidimensional data while allowing useful population information to be aggregated.

Groat, Michael↗
DOE OSTI2022

Modeling Urban Acoustic Noise in the Las Vegas, NV Region

Ambient infrasound noise in quiet, rural environments has been extensively studied and well-characterized through noise models for several decades. More recently, creating noise models for high-noise rural environments has also become an area of active research. However, far less work has been done to create generalized low-frequency noise models for urban areas. The high ambient noise levels expected in cities and other highly populated areas means that these environments are regarded as poor locations for acoustic sensors, and historically, sensor deployment in urban areas were avoided for this reason. However, there are several advantages to placing sensors in urban environments, including convenience of deployment and maintenance, and increasingly, necessity, as more previously rural areas become populated. This study seeks to characterize trends in low-frequency urban noise by creating a background noise model for Las Vegas, NV, using the Las Vegas Infrasound Array (LVIA): a network of eleven infrasound sensors deployed throughout the city. Data included in this study spans from 2019 to 2021 and provides a largely uninterrupted record of noise levels in the city from 0.1–500 Hz, with only minor discontinuities on individual stations. We organize raw data from the LVIA sensors into hourly power spectral density (PSD) averages for each station and select from these PSDs to create frequency distributions for time periods of interest . These frequency distributions are converted into probability density functions (PDFs), which are then used to evaluate variations in frequency and amplitude over daily to seasonal timescale s. In addition to PDFs, the median, 5 th percentile, and 95 th percentile amplitude values are calculated across the entire frequency range. This methodology follows a well-established process for noise model creation.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2016

Detection of anomalous events

A system is described for receiving a stream of events and scoring the events based on anomalousness and maliciousness (or other classification). The system can include a plurality of anomaly detectors that together implement an algorithm to identify low-probability events and detect atypical traffic patterns. The anomaly detector provides for comparability of disparate sources of data (e.g., network flow data and firewall logs.) Additionally, the anomaly detector allows for regulatability, meaning that the algorithm can be user configurable to adjust a number of false alerts. The anomaly detector can be used for a variety of probability density functions, including normal Gaussian distributions, irregular distributions, as well as functions associated with continuous or discrete variables.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Statistics of Green's functions on a disordered Cayley tree and the validity of forward scattering approximation

The accuracy of the forward scattering approximation for two-point Green's functions of the Anderson localization model on the Cayley tree is studied. A relationship between the moments of the Green's function and the largest eigenvalue of the linearized transfer-matrix equation is proved in the framework of the supersymmetric functional-integral method. The new large-disorder approximation for this eigenvalue is derived and its accuracy is established. Using this approximation the probability distribution of the two-point Green's function is found and compared with that in the forward scattering approximation (FSA). It is shown that FSA overestimates the role of resonances and thus the probability for the Green's function to be significantly larger than its typical value. The error of FSA increases with increasing the distance between points in a two-point Green's function.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Exact Bayesian computation using H-functions

In the Bayesian Statistics, the use of conjugate prior distributions allows to obtain exact posterior quantities, avoiding the use of approximate methods. However, there are only a few conjugate structures available, which limits the use in practical problems. In this work we propose a general procedure to obtain the posterior distribution in an exact form in non-conjugate location parameter models with known (possibly, heteroskedastic) variance. The theory is based on special functions, specifically the H-function, which embraces most of the probability distributions. We express explicitly in a computable form all the Bayesian tools for posterior inferences, that is the posterior and the posterior predictive distributions and their moments, as well as the cumulative posterior distribution. As an illustration we consider a problem in astronomy of estimating the distance to the Large Magellanic Cloud, the largest galaxy now orbiting the Milky Way Galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

Classical probabilities for Majorana and Weyl spinors

Highlights: > Map of classical statistical Ising model to fermionic quantum field theory. > Lattice-regularized real Grassmann functional integral for single Weyl spinor. > Emerging complex structure characteristic for quantum physics. > A classical statistical ensemble describes a quantum theory. - Abstract: We construct a map between the quantum field theory of free Weyl or Majorana fermions and the probability distribution of a classical statistical ensemble for Ising spins or discrete bits. More precisely, a Grassmann functional integral based on a real Grassmann algebra specifies the time evolution of the real wave function q{sub {tau}}(t) for the Ising states {tau}. The time dependent probability distribution of a generalized Ising model obtains as p{sub {tau}}(t)=q{sub {tau}}{sup 2}(t). The functional integral employs a lattice regularization for single Weyl or Majorana spinors. We further introduce the complex structure characteristic for quantum mechanics. Probability distributions of the Ising model which correspond to one or many propagating fermions are discussed explicitly. Expectation values of observables can be computed equivalently in the classical statistical Ising model or in the quantum field theory for fermions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Evaluating Space Object Conjunction Probabilities Using Characteristic Function Inversion

This report discusses an approach to computing the probability of a conjunction between two space objects in the short-term encounter scenario. A conjunction is defined here as an event where the miss distance between the objects is less than some specified value. The scenario assumptions are that the motion of the objects is linear, their positions are Gaussian distributed, and their velocities are known and constant. Under these assumptions, the squared-miss distance is shown to have the generalized chi-square distribution. An established statistical technique called characteristic function inversion is employed to evaluate the distribution and obtain conjunction probabilities. The method is closely related to a recent approach based on moment generating function inversion, and a qualitative comparison of the approaches is provided. Last, the method is tested on several benchmark test cases where it agrees with numerical integration on the cases with conjunction probabilities above 10 –12 . However, the exact probability in these cases is usually not needed and this probability can be bounded above using an independent Gaussian approximation. Overall, the report shows how to compute conjunction probabilities using a standard statistical method, though numerical integration seems to perform equally well.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Multivariate Analogs of Classical Univariate Discrete Distributions and Their Properties

Some discrete distributions such as Bernoulli, binomial, geometric, negative binomial, Poisson, Polya–Aeppli, and others play an important role in applied problems of probability theory and mathematical statistics. We propose a variant of a multivariate distribution whose components have a given univariate discrete distribution. In fact we consider some very general variant of the so-called reduction method. We find the explicit form of the mass function and generating function of such distribution and study their properties. We prove that our construction is unique in natural exponential families of distributions. Our results are the generalization and unification of many results of other authors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The Attraction Indian Buffet Distribution

We propose the attraction Indian buffet distribution (AIBD), a distribution for binary feature matrices influenced by pairwise similarity information. Binary feature matrices are used in Bayesian models to uncover latent variables (i.e., features) that explain observed data. The Indian buffet process (IBP) is a popular exchangeable prior distribution for latent feature matrices. In the presence of additional information, however, the exchangeability assumption is not reasonable or desirable. The AIBD can incorporate pairwise similarity information, yet it preserves many properties of the IBP, including the distribution of the total number of features. Thus, much of the interpretation and intuition that one has for the IBP directly carries over to the AIBD. A temperature parameter controls the degree to which the similarity information affects feature-sharing between observations. Unlike other nonexchangeable distributions for feature allocations, the probability mass function of the AIBD has a tractable normalizing constant, making posterior inference on hyperparameters straight-forward using standard MCMC methods. A novel posterior sampling algorithm is proposed for the IBP and the AIBD. We demonstrate the feasibility of the AIBD as a prior distribution in feature allocation models and compare the performance of competing methods in simulations and an application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Bayesian Approach for Extracting Fluorescence Lifetimes from Sparse Data Sets and Its Significance for Imaging Experiments

The measurement of fluorescence lifetimes, especially in small sample volumes, presents the dual challenge of probing a small number of fluorophores and fitting the concomitant sparse data set to the appropriate excited-state decay function. A common method of analysis, such as the maximum likelihood (ML) technique, assumes a uniform probability distribution of the parameters describing the fluorescence decay function. An improvement is thus suggested by implementing a suitable nonuniform distribution, as is provided by a Bayesian framework, where the distribution of parameters is obtained from both their prior knowledge and the evidence-based likelihood of an event for a given set of parameters. We have also considered the Dirichlet prior distribution, whose mathematical form enables analytical solutions of the fitting parameters to be rapidly obtained. If Gaussian and exponential prior distributions are judiciously chosen, they reproduce the experimental target lifetime to within 20% with as few as 20 total photon counts for the data set, as does the Dirichlet prior distribution. But because of the analytical solutions afforded by the Dirichlet prior distribution, it is proposed to employ a Dirichlet prior to search parameter space rapidly to provide, if necessary, appropriate parameters for subsequent employment of a Gaussian or exponential prior distribution

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Collective motion in prolate γ-rigid nuclei within minimal length concept via a quantum perturbation method

Based on the minimal length concept, inspired by Heisenberg algebra, a closed analytical formula is derived for the energy spectrum of the prolate γ-rigid Bohr–Mottelson Hamiltonian of nuclei, within a quantum perturbation method (QPM), by considering a scaled Davidson potential in β shape variable. In the resulting solution, called X(3)-D-ML, the ground state and the first β-band are all studied as a function of the free parameters. The fact of introducing the minimal length concept with a QPM makes the model very flexible and a powerful approach to describe nuclear collective excitations of a variety of vibrational-like nuclei. The introduction of scaling parameters in the Davidson potential enables us to get a physical minimum of this latter in comparison with previous works. The analysis of the corrected wave function, as well as the probability density distribution, shows that the minimal length parameter has a physical upper bound limit.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Correlation function distributions for O ( N ) lattice field theories in the disordered phase

Numerical computations in strongly interacting quantum field theories are often performed using Monte Carlo sampling methods. A key task in these calculations is to estimate the value of a given physical quantity from the distribution of stochastic samples that are generated using the Monte Carlo method. Typically, the sample mean and sample variance are used to define the expectation values and uncertainties of computed quantities. However, the Monte Carlo sample distribution contains more information than these basic properties, and it is useful to investigate it more generally. In this work, the exact form of the probability distributions of two-point correlation functions at zero momentum in O ( N ) lattice field theories in the disordered phase and in infinite volume are determined. These distributions allow for a robust investigation of the efficacy of the Monte Carlo sampling procedure and are shown also to allow for improved estimators of the target physical quantity to be constructed. The theoretical expectations are shown to agree with numerical calculations in the O ( 2 ) model. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2013

Computational analysis of kidney scintigrams

The scintigraphic investigation of normal and pathological kidneys was carried out using specialized gamma-camera device from nuclear medicine hospital department. Technetium 90m isotope with gamma radiation emission, coupled with vector molecules for kidney tissues was introduced into the subject body, its dynamics being recorded as data source for kidney clearance capacity. Two representative data series were investigated, corresponding to healthy and pathological organs respectively. The semi-quantitative tests applied for the comparison of the two distinct medical situations were: the shape of probability distribution histogram, the power spectrum, the auto-correlation function and the Lyapunov exponent. While power spectrum led to similar results in both cases, significant differences were revealed by means of distribution probability, Lyapunov exponent and correlation time, recommending these numerical tests as possible complementary tools in clinical diagnosis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Quantum mechanics on phase space: The hydrogen atom and its Wigner functions

Symplectic quantum mechanics (SQM) considers a non-commutative algebra of functions on a phase space Γ and an associated Hilbert space H{sub Γ}, to construct a unitary representation for the Galilei group. From this unitary representation the Schrödinger equation is rewritten in phase space variables and the Wigner function can be derived without the use of the Liouville–von Neumann equation. In this article the Coulomb potential in three dimensions (3D) is resolved completely by using the phase space Schrödinger equation. The Kustaanheimo–Stiefel(KS) transformation is applied and the Coulomb and harmonic oscillator potentials are connected. In this context we determine the energy levels, the amplitude of probability in phase space and correspondent Wigner quasi-distribution functions of the 3D-hydrogen atom described by Schrödinger equation in phase space.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗