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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Role of the coupling between neck and radial degrees of freedom in evolution from dinucleus to mononucleus

The coupled Langevin equations in two-dimensional collective space are used to study the dynamics of nuclear neck growth. Special attention is paid to the effects of coupling between neck and radial degrees of freedom on the evolution from dinucleus to mononuleus. The dynamic model is applied for the study of neck evolution of the mass asymmetric system {sup 50}Ti + {sup 249}Cf. In order to estimate the effects of the coupling, we use the equations in the coupled and uncoupled cases. Our results show that the coupling between neck and radial motions reduces the neck growth velocity and delays the transition from dinuclear to mononuclear regimes. In addition, by solving these dynamic equations we get the probability distributions of radial degree of freedom at the injection point s{sub in} in the asymmetric fission valley. In this way, s{sub in} is no longer an adjustable parameter in the fusion-by-diffusion model. The distributions obtained are located at positions very close to s=0. Moreover, the coupling significantly reduces the fluctuation in the s{sub in} space. Our results show that the quasifissionlike events are unlikely to take place during the transition period for the mass asymmetric systems. Based on the s{sub in} distribution obtained, the evaporation residue (ER) cross sections for 3n and 4n evaporation channels in the {sup 50}Ti + {sup 249}Cf reaction leading to formation of {sup 296}120 and {sup 295}120 isotopes are calculated. The maximum ER cross sections in 3n and 4n channels with the s{sub in} distributions are equal to 0.1 and 0.065 pb, respectively, which are more than two times larger than those obtained by the fusion-by-diffusion model with the s{sub in}=0 assumption.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
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 OSTI2012

Physics of Intrinsic Rotation in Flux-Driven ITG Turbulence

Global, heat flux-driven ITG gyrokinetic simulations which manifest the formation of macroscopic, mean toroidal flow profiles with peak thermal Mach number 0.05, are reported. Both a particle-in-cell (XGC1p) and a semi-Lagrangian (GYSELA) approach are utilized without a priori assumptions of scale-separation between turbulence and mean fields. Flux-driven ITG simulations with different edge flow boundary conditions show in both approaches the development of net unidirectional intrinsic rotation in the co-current direction. Intrinsic torque is shown to scale approximately linearly with the inverse scale length of the ion temperature gradient. External momentum input is shown to effectively cancel the intrinsic rotation profile, thus confirming the existence of a local residual stress and intrinsic torque. Fluctuation intensity, intrinsic torque and mean flow are demonstrated to develop inwards from the boundary. The measured correlations between residual stress and two fluctuation spectrum symmetry breakers, namely E x B shear and intensity gradient, are similar. Avalanches of (positive) heat flux, which propagate either outwards or inwards, are correlated with avalanches of (negative) parallel momentum flux, so that outward transport of heat and inward transport of parallel momentum are correlated and mediated by avalanches. The probability distribution functions of the outward heat flux and the inward momentum flux show strong structural similarity

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Characterizing wind gusts in complex terrain

Abstract. Wind gusts are a key driver of aerodynamic loading, especially for tall structures such a bridges and wind turbines. However, gust characteristics in complex terrain are not well understood and common approximations used to describe wind gust behavior may not be appropriate at heights relevant to wind turbines and other structures. Data collected in the Perdigão experiment are analyzed herein to provide a foundation for improved wind gust characterization and process-level understanding of flow intermittency in complex terrain. High-resolution observations from sonic anemometers and vertically pointing Doppler lidars are used to conduct a detailed study of gust characteristics with a specific focus on the parent distributions of nine gust parameters (that describe velocity, time, and length scales), their joint distributions, height variation, and coherence in the vertical and horizontal planes. Best-fit distributional forms for varying gust properties show good agreement with those from previous experiments in moderately complex terrain but generate nonconservative estimates of the gust properties that are of key importance to structural loading. Probability distributions of gust magnitude derived from vertically pointing Doppler lidars exhibit good agreement with estimates from sonic anemometers despite differences arising from volumetric averaging and the terrain complexity. Wind speed coherence functions during gusty periods (which are important to structural wind loading) are similar to less complex sites for small vertical displacements (10 to 40 m), but do not exhibit an exponential form for larger horizontal displacements (800 to 1500 m).

Letson, Frederick (ORCID:0000000192750359)↗
DOE OSTI2021

Bayesian estimations of orientation distribution functions from small-angle scattering enable direct prediction of mechanical stress in anisotropic materials

Properties of soft materials are influenced by their anisotropic structuring under nonequilibrium fields. Although anisotropic structure-property relationships have been extensively explored theoretically, comparison to experiments requires determination of the microstructural orientation probability distribution function (OPDF) of microstructural elements. Small angle scattering (SAS) measurements encode information about the OPDF, but tools to navigate this connection are incomplete. Here, we develop and validate an explicit framework to link arbitrary OPDFs to SAS measurements. Specifically, we propose, validate, and apply a method, maximum a posteriori scattering inference (MAPSI), whereby the OPDF may be obtained from SAS measurements using a Bayesian estimation method. Using this method, we obtain estimates of the full 3D OPDF for two model semidilute fd-virus (rodlike) dispersions at concentrations that are approximately equal to and twice the overlap concentration. From the OPDF, we calculate its second and fourth moments and compare these to predictions for a dilute suspension of rigid rods and to a recent theory for semidilute suspensions. Finally, we use both the theoretical and measured moments to calculate the stress, both for dilute and semidilute suspensions. These predictions are not only compared to each other, but also to measured values of the shear stress, and point to new insights into the behavior of suspensions of highly elongated particles in the transition between dilute and semidilute behavior. We also use this new framework to provide perspective on the connection between scalar parameterizations of scattering and the OPDF that have frequently been used in the past. Here, the new tools developed in this work provide an unprecedented path toward experimental validation of dynamical theories of rodlike colloids and polymers, and for measurement of nonequilibrium structures and stresses of other complex fluids and soft materials with SAS.

36 MATERIALS SCIENCE↗
DOE OSTI2012

AUTOMATED SOLAR FLARE STATISTICS IN SOFT X-RAYS OVER 37 YEARS OF GOES OBSERVATIONS: THE INVARIANCE OF SELF-ORGANIZED CRITICALITY DURING THREE SOLAR CYCLES

We analyzed the soft X-ray light curves from the Geostationary Operational Environmental Satellites over the last 37 years (1975-2011) and measured with an automated flare detection algorithm over 300,000 solar flare events (amounting to Almost-Equal-To 5 times higher sensitivity than the NOAA flare catalog). We find a power-law slope of {alpha}{sub F} = 1.98 {+-} 0.11 for the (background-subtracted) soft X-ray peak fluxes that is invariant through three solar cycles and agrees with the theoretical prediction {alpha}{sub F} = 2.0 of the fractal-diffusive self-organized criticality (FD-SOC) model. For the soft X-ray flare rise times, we find a power-law slope of {alpha}{sub T} = 2.02 {+-} 0.04 during solar cycle minima years, which is also consistent with the prediction {alpha}{sub T} = 2.0 of the FD-SOC model. During solar cycle maxima years, the power-law slope is steeper in the range of {alpha}{sub T} Almost-Equal-To 2.0-5.0, which can be modeled by a solar-cycle-dependent flare pile-up bias effect. These results corroborate the FD-SOC model, which predicts a power-law slope of {alpha}{sub E} = 1.5 for flare energies and thus rules out significant nanoflare heating. While the FD-SOC model predicts the probability distribution functions of spatio-temporal scaling laws of nonlinear energy dissipation processes, additional physical models are needed to derive the scaling laws between the geometric SOC parameters and the observed emissivity in different wavelength regimes, as we derive here for soft X-ray emission. The FD-SOC model also yields statistical probabilities for solar flare forecasting.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

How many eigenvalues of a Gaussian random matrix are positive?

We study the probability distribution of the index N{sub +}, i.e., the number of positive eigenvalues of an NxN Gaussian random matrix. We show analytically that, for large N and large N{sub +} with the fraction 0{<=}c=N{sub +}/N{<=}1 of positive eigenvalues fixed, the index distribution P(N{sub +}=cN,N){approx}exp[-{beta}N{sup 2}{Phi}(c)] where {beta} is the Dyson index characterizing the Gaussian ensemble. The associated large deviation rate function {Phi}(c) is computed explicitly for all 0{<=}c{<=}1. It is independent of {beta} and displays a quadratic form modulated by a logarithmic singularity around c=1/2. As a consequence, the distribution of the index has a Gaussian form near the peak, but with a variance {Delta}(N) of index fluctuations growing as {Delta}(N){approx}lnN/{beta}{pi}{sup 2} for large N. For {beta}=2, this result is independently confirmed against an exact finite-N formula, yielding {Delta}(N)=lnN/2{pi}{sup 2}+C+O(N{sup -1}) for large N, where the constant C for even N has the nontrivial value C=({gamma}+1+3ln2)/2{pi}{sup 2}{approx_equal}0.185 248... and {gamma}=0.5772... is the Euler constant. We also determine for large N the probability that the interval [{zeta}{sub 1},{zeta}{sub 2}] is free of eigenvalues. Some of these results have been announced in a recent letter [Phys. Rev. Lett. 103, 220603 (2009)].

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Supernova Driving. IV. The Star-formation Rate of Molecular Clouds

We compute the star-formation rate (SFR) in molecular clouds (MCs) that originate ab initio in a new, higher-resolution simulation of supernova-driven turbulence. Because of the large number of well-resolved clouds with self-consistent boundary and initial conditions, we obtain a large range of cloud physical parameters with realistic statistical distributions, which is an unprecedented sample of star-forming regions to test SFR models and to interpret observational surveys. We confirm the dependence of the SFR per free-fall time, SFR{sub ff}, on the virial parameter, α {sub vir}, found in previous simulations, and compare a revised version of our turbulent fragmentation model with the numerical results. The dependences on Mach number, M, gas to magnetic pressure ratio, β, and compressive to solenoidal power ratio, χ at fixed α {sub vir} are not well constrained, because of random scatter due to time and cloud-to-cloud variations in SFR{sub ff}. We find that SFR{sub ff} in MCs can take any value in the range of 0 ≤ SFR{sub ff} ≲ 0.2, and its probability distribution peaks at a value of SFR{sub ff} ≈ 0.025, consistent with observations. The values of SFR{sub ff} and the scatter in the SFR{sub ff}–α {sub vir} relation are consistent with recent measurements in nearby MCs and in clouds near the Galactic center. Although not explicitly modeled by the theory, the scatter is consistent with the physical assumptions of our revised model and may also result in part from a lack of statistical equilibrium of the turbulence, due to the transient nature of MCs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Adaptive approximation of higher order posterior statistics

Filtering is an approach for incorporating observed data into time-evolving systems. Instead of a family of Dirac delta masses that is widely used in Monte Carlo methods, we here use the Wiener chaos expansion for the parametrization of the conditioned probability distribution to solve the nonlinear filtering problem. The Wiener chaos expansion is not the best method for uncertainty propagation without observations. Nevertheless, the projection of the system variables in a fixed polynomial basis spanning the probability space might be a competitive representation in the presence of relatively frequent observations because the Wiener chaos approach not only leads to an accurate and efficient prediction for short time uncertainty quantification, but it also allows to apply several data assimilation methods that can be used to yield a better approximate filtering solution. The aim of the present paper is to investigate this hypothesis. We answer in the affirmative for the (stochastic) Lorenz-63 system based on numerical simulations in which the uncertainty quantification method and the data assimilation method are adaptively selected by whether the dynamics is driven by Brownian motion and the near-Gaussianity of the measure to be updated, respectively.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

BATSE software for the analysis of the gamma ray burst spatial distribution

The Burst and Transient Source Experiment (BATSE) on the Gamma Ray Observatory (GRO) is designed to study astronomical gamma ray sources and to provide better positional, spectral, and time resolution about these objects than has previously been possible from one experiment. The procedure to be used in the analysis of the gamma ray burst spatial distribution is presented. Data is input from BATSE via the Gamma Ray Burst Catalog (listing individual burst positions, flux values, and associated errors) and the Sky Sensitivity Map (which summarizes observational selection effects in table format). A FORTRAN program generates Monte Carlo burst catalogs, which are models to be compared to the actual distribution. The Monte Carlo models are then filtered through the Sky Sensitivity Map so that they suffer from the same selection effects as the actual catalog data. Additionally, each burst position is converted into a probability distribution to mimic BATSE positional sensitivity. The Burst Catalog, Monte Carlo burst catalog, and Sky Sensitivity Map are then passed onto an IDL program that compares the catalogs for statistical significance. The Sky Sensitivity Map is used to estimate how often each sky area is observed above the minimum flux level in question. Each burst found in this sky area is then weighted according to the frequency with which this sky area is observed. The catalogs are then compared via tests of homogeneity (based on their radial distributions) and isotropy (based upon their angular distributions). The results of the statistical comparisons along with graphs and charts of the summaries, are output from the IDL program for study.

Hakkila, Jon↗
NASA NTRSDate not supplied

Inter-Calibrating the CERES SW Instrument Fluxes Using Deep Convective Clouds(DCC) Within the Same Sun-Synchronous Orbit

The NASA CERES EBAF product provides the scientific community observed TOA fluxes to monitor the Earth’s energy imbalance and to validate climate models. To provide a seamless EBAF 24-year record, the CERES instrument calibration must be stable and consistent across satellite records. Once the Terra and Aqua spacecraft are decommissioned at the end of 2025, the CERES project will rely on CERES instrument observations on the SNPP and NOAA-20 1:30 PM sun-synchronous orbits. The SNPP and NOAA-20 satellite orbit placement will prevent any time matched observations for inter-calibration efforts. The future Libera instrument, which will continue the CERES record and scheduled for launch in 2028 onboard the NOAA-22 satellite, will also need to be inter-calibrated without the aid of time-matched observations. Deep convective clouds (DCC) are the most Lambertian, brightest, tropical Earth invariant targets located at the tropopause making them ideal to radiometrically scale the CERES SW observed radiance to a common reference. The SNPP, NOAA-20 and future NOAA-22 satellites are in the same 16-day repeatable orbits, allowing the DCC targets to be observed with the same angular configuration. The empirically derived SNPP CERES SW channel Bidirectional Reflectance Distribution Function (BRDF) can be easily be applied to the NOAA-20 CERES SW channel radiances since they observe the nearly the same DCC systems. The DCC BRDF corrected radiances are analyzed collectively into radiance probability distribution functions (PDF). By comparing the PDF statistics, the SNPP and NOAA-20 CERES SW channel can be inter-calibrated. The inter-calibration coefficients are validated with the CERES instrument calibration team coefficients to optimize the methodology.

David Doelling↗
DOE OSTI2011

Discriminating quantum-optical beam-splitter channels with number-diagonal signal states: Applications to quantum reading and target detection

We consider the problem of distinguishing, with minimum probability of error, two optical beam-splitter channels with unequal complex-valued reflectivities using general quantum probe states entangled over M signal and M' idler mode pairs of which the signal modes are bounced off the beam splitter while the idler modes are retained losslessly. We obtain a lower bound on the output state fidelity valid for any pure input state. We define number-diagonal signal (NDS) states to be input states whose density operator in the signal modes is diagonal in the multimode number basis. For such input states, we derive series formulas for the optimal error probability, the output state fidelity, and the Chernoff-type upper bounds on the error probability. For the special cases of quantum reading of a classical digital memory and target detection (for which the reflectivities are real valued), we show that for a given input signal photon probability distribution, the fidelity is minimized by the NDS states with that distribution and that for a given average total signal energy N{sub s}, the fidelity is minimized by any multimode Fock state with N{sub s} total signal photons. For reading of an ideal memory, it is shown that Fock state inputs minimize the Chernoff bound. For target detection under high-loss conditions, a no-go result showing the lack of appreciable quantum advantage over coherent state transmitters is derived. A comparison of the error probability performance for quantum reading of number state and two-mode squeezed vacuum state (or EPR state) transmitters relative to coherent state transmitters is presented for various values of the reflectances. While the nonclassical states in general perform better than the coherent state, the quantitative performance gains differ depending on the values of the reflectances. The experimental outlook for realizing nonclassical gains from number state transmitters with current technology at moderate to high values of the reflectances is argued to be good.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

STATISTICAL DECOUPLING OF A LAGRANGIAN FLUID PARCEL IN NEWTONIAN COSMOLOGY

The Lagrangian dynamics of a single fluid element within a self-gravitational matter field is intrinsically non-local due to the presence of the tidal force. This complicates the theoretical investigation of the nonlinear evolution of various cosmic objects, e.g., dark matter halos, in the context of Lagrangian fluid dynamics, since fluid parcels with given initial density and shape may evolve differently depending on their environments. In this paper, we provide a statistical solution that could decouple this environmental dependence. After deriving the evolution equation for the probability distribution of the matter field, our method produces a set of closed ordinary differential equations whose solution is uniquely determined by the initial condition of the fluid element. Mathematically, it corresponds to the projected characteristic curve of the transport equation of the density-weighted probability density function (ρPDF). Consequently it is guaranteed that the one-point ρPDF would be preserved by evolving these local, yet nonlinear, curves with the same set of initial data as the real system. Physically, these trajectories describe the mean evolution averaged over all environments by substituting the tidal tensor with its conditional average. For Gaussian distributed dynamical variables, this mean tidal tensor is simply proportional to the velocity shear tensor, and the dynamical system would recover the prediction of the Zel’dovich approximation (ZA) with the further assumption of the linearized continuity equation. For a weakly non-Gaussian field, the averaged tidal tensor could be expanded perturbatively as a function of all relevant dynamical variables whose coefficients are determined by the statistics of the field.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Nearest neighbour distributions: New statistical measures for cosmological clustering

ABSTRACT The use of summary statistics beyond the two-point correlation function to analyse the non-Gaussian clustering on small scales, and thereby, increasing the sensitivity to the underlying cosmological parameters, is an active field of research in cosmology. In this paper, we explore a set of new summary statistics – the k-Nearest Neighbour Cumulative Distribution Functions (kNN-CDF). This is the empirical cumulative distribution function of distances from a set of volume-filling, Poisson distributed random points to the k-nearest data points, and is sensitive to all connected N-point correlations in the data. The kNN-CDF can be used to measure counts in cell, void probability distributions, and higher N-point correlation functions, all using the same formalism exploiting fast searches with spatial tree data structures. We demonstrate how it can be computed efficiently from various data sets – both discrete points, and the generalization for continuous fields. We use data from a large suite of N-body simulations to explore the sensitivity of this new statistic to various cosmological parameters, compared to the two-point correlation function, while using the same range of scales. We demonstrate that the use of kNN-CDF improves the constraints on the cosmological parameters by more than a factor of 2 when applied to the clustering of dark matter in the range of scales between 10 and $40\, h^{-1}\, {\rm Mpc}$. We also show that relative improvement is even greater when applied on the same scales to the clustering of haloes in the simulations at a fixed number density, both in real space, as well as in redshift space. Since the kNN-CDF are sensitive to all higher order connected correlation functions in the data, the gains over traditional two-point analyses are expected to grow as progressively smaller scales are included in the analysis of cosmological data, provided the higher order correlation functions are sensitive to cosmology on the scales of interest.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

Atom counting in expanding ultracold clouds

We study the counting statistics of ultracold bosonic atoms that are released from an optical lattice. We show that the counting probability distribution of the atoms collected at a detector located far away from the optical lattice can be used to characterize the initially trapped states. We consider trapped superfluid and insulating states with different occupation patterns. We analyze how the correlations between the modes that develop during the expansion in the gravitational field appear in the counting distribution and find that the ratio of the detector size with respect to the expanded wave function determines whether short-range or long-range correlations of the initial state are reflected in the counting statistics. We find that detectors which are large compared to the size of the expanded wave function distinguish insulating and superfluid phases irrespective of the occupation pattern. We show that using detectors that are small compared to the size of the expanded wave function, occupation patterns in insulating and supersolid states can be distinguished. Finally, we show how the magnetic phase patterns are dramatically reflected in the number distribution.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS2012

Global Warming Induced Changes in Rainfall Characteristics in IPCC AR5 Models

Changes in rainfall characteristic induced by global warming are examined from outputs of IPCC AR5 models. Different scenarios of climate warming including a high emissions scenario (RCP 8.5), a medium mitigation scenario (RCP 4.5), and 1% per year CO2 increase are compared to 20th century simulations (historical). Results show that even though the spatial distribution of monthly rainfall anomalies vary greatly among models, the ensemble mean from a sizable sample (about 10) of AR5 models show a robust signal attributable to GHG warming featuring a shift in the global rainfall probability distribution function (PDF) with significant increase (>100%) in very heavy rain, reduction (10-20% ) in moderate rain and increase in light to very light rains. Changes in extreme rainfall as a function of seasons and latitudes are also examined, and are similar to the non-seasonal stratified data, but with more specific spatial dependence. These results are consistent from TRMM and GPCP rainfall observations suggesting that extreme rainfall events are occurring more frequently with wet areas getting wetter and dry-area-getting drier in a GHG induced warmer climate.

Lau, William K. M.↗
DOE OSTI2015

A Scenario Generation Method for Wind Power Ramp Events Forecasting

Wind power ramp events (WPREs) have received increasing attention in recent years due to their significant impact on the reliability of power grid operations. In this paper, a novel WPRE forecasting method is proposed which is able to estimate the probability distributions of three important properties of the WPREs. To do so, a neural network (NN) is first proposed to model the wind power generation (WPG) as a stochastic process so that a number of scenarios of the future WPG can be generated (or predicted). Each possible scenario of the future WPG generated in this manner contains the ramping information, and the distributions of the designated WPRE properties can be stochastically derived based on the possible scenarios. Actual data from a wind power plant in the Bonneville Power Administration (BPA) was selected for testing the proposed ramp forecasting method. Results showed that the proposed method effectively forecasted the probability of ramp events.

17 WIND ENERGY↗
DOE OSTI2025

LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields

Model ensembles are effective tools for estimating prediction uncertainty in deep learning atomistic force fields. However, their widespread adoption is hindered by high computational costs and overconfident error estimates. In this work, we address these challenges by leveraging distributions of per-sample errors obtained during training and employing a distance-based similarity search in the model latent space. Our method, which we call LTAU (Loss Trajectory Analysis for Uncertainty), efficiently estimates the full probability distribution function of errors for any test point using the logged training errors, achieving speeds that are 2–3 orders of magnitudes faster than typical ensemble methods and allowing it to be used for tasks where training or evaluating multiple models would be infeasible. We apply LTAU towards estimating parametric uncertainty in atomistic force fields (LTAU-FF), demonstrating that it produces well-calibrated confidence intervals and predicts errors that correlate strongly with the true errors for data near the training domain. Furthermore, we show that the errors predicted by LTAU-FF can be used in practical applications for detecting out-of-domain data, tuning model performance, and predicting failure during simulations. We believe that LTAU will be a valuable tool for uncertainty quantification in atomistic force fields and is a promising method that should be further explored in other domains of machine learning.

97 MATHEMATICS AND COMPUTING↗