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At least 343 records · Page 19

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NASA NTRS1987

Classification, seasonality and persistence of low-frequency atmospheric circulation patterns

The seasonality and persistence of the major modes of interannual variability in the low-frequency atmospheric circulation were studied using orthogonally rotated principle component analysis (RPCA) of Northern Hemisphere 1-month mean 700 mb heights. The twice-daily data for the 1950-1984 period were used. Winter results are similar to those of other recent RPCA and teleconnection studies. The strongest summer pattern is the North Atlantic Oscillation, which is also the strongest winter pattern; it systematically contracts northward in summer and expands southward in winter, being the only pattern found for every month of the year. The robustness of the RPCA results was examined through consistency with results of other studies and of adjacent month solutions within this study, as well as by replicating the results using 3-month and 10-day means of 700-mb height. It is concluded that the RPCA method provides a physically meaningful and statistically stable product with the simplicity of teleconnection patterns but with superior pattern choice and depiction.

Barnston, Anthony G.↗

NASA NTRS1987

Relativistic beaming and the nuclei of double-lobed quasars

If the simple relativistic beaming model for extragalactic radio sources is at all correct, Doppler boosting may bias us toward choosing those objects pointing nearly at us in flux-density limited samples. In order to minimize any such effects in statistical tests of physical theories of these objects, an attempt is made to define a complete sample of sources with random orientations. For a sample of 26 double-lobed quasars, the dependence of a number of observable features on the angle to the line of sight and on the superluminal velocity is examined, and the distribution of superluminal velocities for the sources is measured. Two new superluminals are reported, and their apparent velocities and nuclear strengths are discussed.

Hough, David H.↗

NASA NTRS1993

Structures Of Turbulent Boundary Layers

Report presents second part of two-part comprehensive study of coherent motions in, and structures of, turbulent boundary-layer flows. Study critically reviews current fragmented knowledge with view toward eventual unification and understanding of physical causes and statistical properties of turbulence phenomena.

Robinson, Stephen K.↗

NASA NTRS1999

The GEOS Ozone Data Assimilation System: Design and Validation

An ozone data assimilation system has been developed at the Data Assimilation Office of the NASA/Goddard Space Flight Center to provide global three-dimensional analyzed ozone mixing ratio and total column ozone. The Total Ozone Mapping Spectrometer (TOMS) total column ozone and the Solar Backscatter Ultraviolet (SBUV) or SBUV/2 partial ozone profile data are assimilated. The analyzed winds from the Goddard Earth Observing System Data Assimilation System (GEOS-DAS) drive the ozone transport. Following every transport model timestep, the model prediction is combined with the observations using a global, physical-space based, statistical analysis scheme. Due to the smaller size of the ozone system than that of a global meteorological data assimilation system, new statistical analysis methodology, including anisotropic and flow-dependent forecast error correlation models, can be implemented and tested in the ozone system more easily. Sample results from the winter 1992 validation period are presented. There is a close agreement between the analyzed fields and the independent observations from ozone sondes and the Halogen Occultation Experiment (HALOE).

Stajner, I.↗

NASA NTRS2000

Atlas of Seasonal Means Simulated by the NSIPP 1 Atmospheric GCM

This atlas documents the climate characteristics of version 1 of the NASA Seasonal-to-Interannual Prediction Project (NSIPP) Atmospheric General Circulation Model (AGCM). The AGCM includes an interactive land model (the Mosaic scheme), and is part of the NSIPP coupled atmosphere-land-ocean model. The results presented here are based on a 20-year (December 1979-November 1999) "ANIIP-style" integration of the AGCM in which the monthly-mean sea-surface temperature and sea ice are specified from observations. The climate characteristics of the AGCM are compared with the National Centers for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasting (ECMWF) reanalyses. Other verification data include Special Sensor Microwave/Imager (SSNM) total precipitable water, the Xie-Arkin estimates of precipitation, and Earth Radiation Budget Experiment (ERBE) measurements of short and long wave radiation. The atlas is organized by season. The basic quantities include seasonal mean global maps and zonal and vertical averages of circulation, variance/covariance statistics, and selected physics quantities.

Suarez, Max J.↗

NASA NTRS2002

Studying the Iron Line Complex in the Bright Seyfert Galaxy NGC 5506

This grant was to support the reduction and analysis of our approved XMM observation of the nearby Seyfert 2 galaxy NGC 5506. The observation has been carried out simultaneously with a BeppoSAX observation of the same source. The proposal was aimed to study in detail the Compton reflection component and the complex Iron K line of this source, combining the still unique capability of BeppoSAX in hard X-rays (to strongly constrain the reflection component, and then the intrinsic nuclear continuum), and the sensitivity of XMM at the energy of the Iron Line complex. NGC 5506 is one of the brightest AGN in hard X-rays and has been intensively studied in the past. GINGA detected the complex iron line as well as the reflection component. Both ASCA (spectroscopically) and Rossi-XTE (through variability analysis) suggested that the FeK line is complex, possibly made up of several distinct components. The centroid of the FeK complex in a subsequent BeppoSAX observation was bluer than the 6.4 keV energy of the relatively low-ionization iron Kalpha transition. NGC 5506 has been observed simultaneously by NewtonXMM and BeppoSAX on February 2-3 2001. we have reduced and analyzed both the NewtonXMM and the BeppoSAX data, and have written and published a paper on our results (appeared in Volume 377 (page 31) of A&A-Letters). Our main results can be summarized as follows: (a) we confirm that the FeK line is complex, and for the first time disentangle its components: we find that at least two components made up the FeK complex, one neutral and narrow, at 6.4 keV (rest energy), and another one either broader and highly ionized, at about 6.7 keV (rest frame), or, in turn, made up of two narrow and unresolved components from the He-like and the H-like ions of Fe; (b) the two possible solution for the high-ionization Fe-K component, are statistically indistinguishable. However, physically, a blend of two narrow lines from photoionized matter seems to be preferable to emission of a relativistically broadened line from an ionized accretion disk; (c) The bulk of the Compton reflection continuum originates in neutral matter, and is therefore associated with the narrow FeK line at 6.4 keV: they are most likely emitted by distant matter.

Nicastro, Fabrizio↗

NASA NTRS2018

Introduction to Data Assimilation

Atmospheric data assimilation is a class of techniques used for producing descriptions of fields of air temperature, pressure, humidity, wind, etc. on a spatial grid or in terms of a finite functional representation. These are then used to initialize numerical weather forecasts or to analyze the atmosphere for other purposes. The techniques combine past, present, and even future observations in an approximate statistically optimal way. Various types of statistical or physically-based models and their corresponding adjoints are employed to relate diverse fields in both time and space and to relate what is observed to what is being analyzed. Computationally, the problem is very demanding and onstraining on the techniques that can be employed on a routine basis.

Atmospheric data assimilation↗

NASA NTRS2022

Detection and Characterization of Instrumental Transients in LISA Pathfinder and Their Projection to LISA

The LISA Pathfinder (LPF) mission succeeded outstandingly in demonstrating key technologicalaspects of future space-borne gravitational-wave detectors, such as the Laser Interferometer SpaceAntenna (LISA). Specifically, LPF demonstrated with unprecedented sensitivity the measurementof the relative acceleration of two free-falling cubic test masses. Although most disruptive non-gravitational forces have been identified and their effects mitigated through a series of calibrationprocesses, some faint transient signals of yet unexplained origin remain in the measurements. If theyappear in the LISA data, these perturbations (also called glitches) could skew the characterizationof gravitational-wave sources or even be confused with gravitational-wave bursts. For the firsttime, we provide a comprehensive census of LPF transient events. Our analysis is based on aphenomenological shapelet model allowing us to derive simple statistics about the physical featuresof the glitch population. We then implement a generator of synthetic glitches designed to be usedfor subsequent LISA studies, and perform a preliminary evaluation of the effect of the glitches onfuture LISA data analyses.

Quentin Baghi↗

DOE OSTI2019

Parfume (particle Fuel Model)

PARFUME is an advanced gas-cooled reactor fuel performance modeling and analysis code. It has been developed as an integrated mechanistic code that evaluates the thermal, mechanical, and physio-chemical behavior of fuel particles during irradiation and postulated accident conditions to determine the failure probability of a population of fuel particles given the particle-to-particle statistical variations in physical dimensions and material properties that arise from the fuel fabrication process, accounting for all viable mechanisms that can lead to particle failure. The code also determines the diffusion of fission products from the fuel through the particle coating layers, and through the fuel matrix to the coolant boundary. The subsequent release of fission products is calculated at the fuel element level (release of fission products from the compact or pebble). PARFUME calculates the release fraction as the ratio of the number of atoms released from the fuel element to the amount.

Gougar, Hans D.↗

DOE OSTI2020

Comparison between PARFUME and Bison Using the AGR-2 Irradiation Experiment

This report documents comparisons between Fuel Model (PARFUME) model predictions versus Bison for selected compacts from the second irradiation test of the Advanced Gas Reactor (AGR) program that occurred from June 2010 to October 2013 in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). PARFUME is a fuel performance analysis and modeling code, used for evaluating gas-reactor tristructural isotropic (TRISO) coated particle fuel for prismatic, pebble bed, plate, and cylindrical type fuel geometries. PARFUME is an integrated mechanistic computer code that evaluates the thermal, mechanical, and physico-chemical behavior of TRISO coated-fuel particles and the probability for fuel failure given the particle-to-particle statistical variations in physical dimensions and material properties that arise during the fuel fabrication process. Bison is a nuclear fuel performance application built using the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element library (Permann 2020). Bison is capable of modeling multiple fuel forms in a wide variety of dimensions and geometries. It solves coupled nonlinear partial differential equations, including heat conduction, mechanics, fission product species transport etc., in a fully implicit manner. Comparisons between PARFUME and Bison were performed using four compacts from the AGR-2 experiment. Selected outputs were chosen based on their impact on the probability of the SiC layer failing. In general there was good agreement between PARFUME and Bison with the exception of predicting the gap formed between the buffer and IPyC. Further comparisons between PARFUME and Bison are planned to further develop Bison’s capabilities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1992

Possibilistic measurement and set statistics

Set-based statistics are necessary to generate possibility distributions from measured data. Methods by which physical measurements can generate statistical data on real intervals are considered, including the following: trials from multiple heterogeneous measurement devices rather than a single instrument at multiple times; classes of consistent intervals constructed from statistical data around a common point focus or interval core; and consonant intervals constructed from statistical data.

Joslyn, Cliff↗

DOE OSTI2023

Effective dissipation rate in a Liouvillian-graph picture of high-temperature quantum hydrodynamics

At high temperature, generic strongly interacting spin systems are expected to display hydrodynamics: local transport of conserved quantities, governed by classical partial differential equations like the diffusion equation. I argue that the emergence of this dissipative long-wavelength dynamics from the system's unitary microscopic dynamics is controlled by the structure of the Liouvillian graph of the system's Hamiltonian, that is, the graph induced on Pauli strings by commutation with that Hamiltonian. The Liouvillian graph decomposes naturally into subgraphs of Pauli strings of constant diameter, and the coherent dynamics of these subgraphs determines the rate at which operator weight spreads to long operators. In conclusion, this argument provides a quantitative theory of the emergence of a dissipative effective dynamics from unitary microscopic dynamics; it also leads to an effective model with Hilbert space dimension linear in system size and exponential in the UV cutoff for diffusion.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Data-driven prediction of scaling and ignition of inertial confinement fusion experiments

Recent advances in inertial confinement fusion (ICF) at the National Ignition Facility (NIF), including ignition and energy gain, are enabled by a close coupling between experiments and high-fidelity simulations. Neither simulations nor experiments can fully constrain the behavior of ICF implosions on their own, meaning pre- and postshot simulation studies must incorporate experimental data to be reliable. Linking past data with simulations to make predictions for upcoming designs and quantifying the uncertainty in those predictions has been an ongoing challenge in ICF research. We have developed a data-driven approach to prediction and uncertainty quantification that combines large ensembles of simulations with Bayesian inference and deep learning. The approach builds a predictive model for the statistical distribution of key performance parameters, which is jointly informed by past experiments and physics simulations. The prediction distribution captures the impact of experimental uncertainty, expert priors, design changes, and shot-to-shot variations. We have used this new capability to predict a 10× increase in ignition probability between Hybrid-E shots driven with 2.05 MJ compared to 1.9 MJ, and validated our predictions against subsequent experiments. We describe our new Bayesian postshot and prediction capabilities, discuss their application to NIF ignition and validate the results, and finally investigate the impact of data sparsity on our prediction results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2017

Emulating Simulations of Cosmic Dawn for 21 cm Power Spectrum Constraints on Cosmology, Reionization, and X-Ray Heating

Current and upcoming radio interferometric experiments are aiming to make a statistical characterization of the high-redshift 21 cm fluctuation signal spanning the hydrogen reionization and X-ray heating epochs of the universe. However, connecting 21 cm statistics to the underlying physical parameters is complicated by the theoretical challenge of modeling the relevant physics at computational speeds quick enough to enable exploration of the high-dimensional and weakly constrained parameter space. In this work, we use machine learning algorithms to build a fast emulator that can accurately mimic an expensive simulation of the 21 cm signal across a wide parameter space. We embed our emulator within a Markov Chain Monte Carlo framework in order to perform Bayesian parameter constraints over a large number of model parameters, including those that govern the Epoch of Reionization, the Epoch of X-ray Heating, and cosmology. As a worked example, we use our emulator to present an updated parameter constraint forecast for the Hydrogen Epoch of Reionization Array experiment, showing that its characterization of a fiducial 21 cm power spectrum will considerably narrow the allowed parameter space of reionization and heating parameters, and could help strengthen Planck's constraints on σ{sub 8}. We provide both our generalized emulator code and its implementation specifically for 21 cm parameter constraints as publicly available software.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1991

Evidence of chaotic pattern in solar flux through a reproducible sequence of period-doubling-type bifurcations

A preliminary study of the limits to solar flux intensity prediction, and of whether the general lack of predictability in the solar flux arises from the nonlinear chaotic nature of the Sun's physical activity is presented. Statistical analysis of a chaotic signal can extract only its most gross features, and detailed physical models fail, since even the simplest equations of motion for a nonlinear system can exhibit chaotic behavior. A recent theory by Feigenbaum suggests that nonlinear systems that can be led into chaotic behavior through a sequence of period-doubling bifurcations will exhibit a universal behavior. As the control parameter is increased, the bifurcation points occur in such a way that a proper ratio of these will approach the universal Feigenbaum number. Experimental evidence supporting the applicability of the Feigenbaum scenario to solar flux data is sparse. However, given the hypothesis that the Sun's convection zones are similar to a Rayleigh-Bernard mechanism, we can learn a great deal from the remarkable agreement observed between the prediction by theory (period doubling - a universal route to chaos) and the amplitude decrease of the signal's regular subharmonics. It is shown that period-doubling-type bifurcation is a possible route to a chaotic pattern of solar flux that is distinguishable from the logarithm of its power spectral density. This conclusion is the first positive step toward a reformulation of solar flux by a nonlinear chaotic approach. The ultimate goal of this research is to be able to predict an estimate of the upper and lower bounds for solar flux within its predictable zones. Naturally, it is an important task to identify the time horizons beyond which predictability becomes incompatible with computability.

Ashrafi, S.↗

NASA NTRS2005

Regression Analysis of Long-Term Profile Ozone Data Set from BUV Instruments

We have produced a profile merged ozone data set (MOD) based on the SBUV/SBUV2 series of nadir-viewing satellite backscatter instruments, covering the period from November 1978 - December 2003. In 2004, data from the Nimbus 7 SBUV and NOAA 9, ll, and 16 SBUV/2 instruments were reprocessed using the Version 8 (V8) algorithm and most recent calibrations. More recently, data from the Nimbus 4 BUT instrument, which was operational from 1970 - 1977, were also reprocessed using the V8 algorithm. As part of the V8 profile calibration, the Nimbus 7 and NOAA 9 (1993-1997 only) instrument calibrations have been adjusted to match the NOAA 11 calibration, which was established based on comparisons with SSBUV shuttle flight data. Differences between NOAA 11, Nimbus 7 and NOAA 9 profile zonal means are within plus or minus 5% at all levels when averaged over the respective periods of data overlap. NOAA 16 SBUV/2 data have insufficient overlap with NOAA 11, so its calibration is based on pre-flight information. Mean differences over 4 months of overlap are within plus or minus 7%. Given the level of agreement between the data sets, we simply average the ozone values during periods of instrument overlap to produce the MOD profile data set. Initial comparisons of coincident matches of N4 BUV and Arosa Umkehr data show mean differences of 0.5 (0.5)% at 30km; 7.5 (0.5)% at 35 km; and 11 (0.7)% at 40 km, where the number in parentheses is the standard error of the mean. In this study, we use the MOD profile data set (1978-2003) to estimate the change in profile ozone due to changing stratospheric chlorine levels. We use a standard linear regression model with proxies for the seasonal cycle, solar cycle, QBO, and ozone trend. To account for the non-linearity of stratospheric chlorine levels since the late 1990s, we use a time series of Effective Chlorine, defined as the global average of Chlorine + 50 * Bromine at 1 hPa, as the trend proxy. The Effective Chlorine data are taken from the 3-D Goddard CTM. We will show the latest trend results using this statistical model. In addition, the Nimbus 4 BUV data offer an opportunity to test the physical properties of our statistical model. From ground-based comparisons we will establish an uncertainty range for the Nimbus 4 data. We then extrapolate our statistical model fit backwards in time and compare to the Nimbus 4 data. We compare the characteristics of the residual, defined as the difference between the data and statistical regression fit, during the Nimbus 4 time period and the 1978-2003 period over which the statistical model coefficients were estimated, and present these results.

Stolarski, Richard S.↗

DOE OSTI2021

Physics guided machine learning using simplified theories

Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this Letter, we introduce a modular physics guided machine learning framework to improve the accuracy of such data-driven predictive engines. The chief idea in our approach is to augment the knowledge of the simplified theories with the underlying learning process. To emphasize their physical importance, our architecture consists of adding certain features at intermediate layers rather than in the input layer. To demonstrate our approach, we select a canonical airfoil aerodynamic problem with the enhancement of the potential flow theory. We include the features obtained by a panel method that can be computed efficiently for an unseen configuration in our training procedure. By addressing the generalizability concerns, our results suggest that the proposed feature enhancement approach can be effectively used in many scientific machine learning applications, especially for the systems where we can use a theoretical, empirical, or simplified model to guide the learning module.

42 ENGINEERING↗

DOE OSTI2022

Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters

The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single-variable regressors that did not capture all contributions to energy consumption. Further, new modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model is developed to create a general predictor for industrial building energy consumption. The model uses features of air enthalpy, solar radiation, and wind speed to predict weather-dependency; motor, steam, and compressed air system parameters to capture support equipment contributions; and operating schedule, production rate, number of employees, and floor area to determine production-dependency. Results showed that a model that used a linear regressor over a transformed feature space could outperform a support vector machine and utilize features more representative of physical systems. Using informed parameters to build a reliable predictor will more accurately characterize a manufacturing facility's energy savings opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗