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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

A GCM parameterization for the shortwave radiative properties of water clouds

A new parameterization was developed for predicting the shortwave radiative properties of water clouds, suitable for inclusion in general circulation models (GCMs). The parameterization makes use of the simple relationships found by Slingo and Schrecker, giving the three input parameters required to calculate the cloud radiative properties (the optical depth, single scatter albedo and asymmetry parameter) in terms of the liquid water path and equivalent radius of the drop size distribution. The input parameters are then used to derive the cloud radiative properties, using standard two-stream equations for a single layer. The relationships were originally derived for fairly narrow spectral bands but it was found that it is possible to average the coefficients so as to use a much smaller number of bands, without sacrificing accuracy in calculating the cloud radiative properties. This makes the parameterization fast enough to be included in GCMs. The parameterization was programmed into the radiation scheme used in the U.K. Meteorological Office GCM. This scheme and the 24 band Slingo/Schrecker scheme were compared with each other and with observations, using a variety of published datasets. There is good agreement between the two schemes for both cloud albedo and absorption, even when only four spectral bands are employed in the GCM.

Slingo, A.↗
NASA NTRS1991

Statistical structural analysis of rotor impact ice shedding

The statistical characteristics of impact ice shear strength are analyzed, with emphasis placed on the most probable shear strength and statistical distribution of an ice deposit. Several distribution types are considered: the Weibull, two-parameter Weibull, and exponential distributions, as well as the Gumbell distribution of the smallest extreme and the Gumbell distribution of the largest extreme. It is concluded that the Weibull distribution yields the best results; however, the expected life, shape parameter, and scale parameter should be determined separately for each case of varying wind speed and droplet size. The theoretical predictions of shear stresses in a specific rotating ice shape are compared, and it is noted that when the effects of lift are added to the theoretical model and the interference is calculated with a new mean and standard deviation, the probability of ice shed is computed as 36.64 pct.

Kellacky, C. J.↗
NASA NTRS2014

A Multiscale Progressive Failure Modeling Methodology for Composites that Includes Fiber Strength Stochastics

A multiscale modeling methodology was developed for continuous fiber composites that incorporates a statistical distribution of fiber strengths into coupled multiscale micromechanics/finite element (FE) analyses. A modified two-parameter Weibull cumulative distribution function, which accounts for the effect of fiber length on the probability of failure, was used to characterize the statistical distribution of fiber strengths. A parametric study using the NASA Micromechanics Analysis Code with the Generalized Method of Cells (MAC/GMC) was performed to assess the effect of variable fiber strengths on local composite failure within a repeating unit cell (RUC) and subsequent global failure. The NASA code FEAMAC and the ABAQUS finite element solver were used to analyze the progressive failure of a unidirectional SCS-6/TIMETAL 21S metal matrix composite tensile dogbone specimen at 650 degC. Multiscale progressive failure analyses were performed to quantify the effect of spatially varying fiber strengths on the RUC-averaged and global stress-strain responses and failure. The ultimate composite strengths and distribution of failure locations (predominately within the gage section) reasonably matched the experimentally observed failure behavior. The predicted composite failure behavior suggests that use of macroscale models that exploit global geometric symmetries are inappropriate for cases where the actual distribution of local fiber strengths displays no such symmetries. This issue has not received much attention in the literature. Moreover, the model discretization at a specific length scale can have a profound effect on the computational costs associated with multiscale simulations.models that yield accurate yet tractable results.

Generalized Method of Cells↗
DOE OSTI2020

Probabilistic Groundwater Modeling of the RDX Plume at Los Alamos National Laboratory to Support Risk Assessment - 20359

A plume of the contaminant hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) with concentrations greater than the New Mexico tap water drinking standard (9.66 ppb) is present in the regional aquifer near the southwestern boundary of Los Alamos National Laboratory (LANL). A risk assessment is performed for exposure to regional aquifer groundwater, with long-term predictions of RDX concentrations provided by a calibrated, probabilistic, numerical fate and transport model. The structure of the model is hierarchical, with the RDX Regional Aquifer (RA) groundwater model acting as the primary tool for analysis of downgradient RDX concentrations. The RA model is deeply informed by the conceptual site model (CSM) and is calibrated using site RDX concentration data and hydraulic head measurements, along with other analyses. The model is calibrated using data through December 2019. Where data are scarce other lines of evidence are used to inform inputs, including the multiphase RDX Vadose Zone (VZ) model and Pipe and Disk analytical screening tool. Model inputs are described with informative prior distributions using a robust approach to development that incorporates all available lines of evidence for every parameter used as an input to the RA model. Calibration is performed using a classical nonlinear optimization routine, which is then used to initialize a Bayesian calibration. The Bayesian calibration constrains the uncertainty in the classical calibration, ultimately providing posterior distributions for all model parameters. The challenges of the calibration include high-dimensional parameter space, including spatially heterogeneous hydraulic conductivities, comparatively sparse data, and low RDX concentrations. Posterior parameter distributions developed in the calibration process are then used for stochastic predictive model runs into the future. The result of the forward runs is spatially and temporally explicit estimates of head and concentration with uncertainty at all points in space and time. The probabilistic modeling approach presented here includes innovative computational and statistical methods that leverage high-performance computing (HPC) resources. It makes use of prior modeling work performed at the chromium plume site in the central LANL area, with extensive updates. The risk assessment will ultimately be used to support decision-making at the site, using multiple appropriately weighted sources of information, as well as uncertainty, sensitivity, and value-of-information analyses. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

An Efficient Bayesian Approach to Learning Droplet Collision Kernels: Proof of Concept Using “Cloudy,” a New n -Moment Bulk Microphysics Scheme

The small-scale microphysical processes governing the formation of precipitation particles cannot be resolved explicitly by cloud resolving and climate models. Instead, they are represented by microphysics schemes that are based on a combination of theoretical knowledge, statistical assumptions, and fitting to data (“tuning”). Historically, tuning was done in an ad hoc fashion, leading to parameter choices that are not explainable or repeatable. Recent work has treated it as an inverse problem that can be solved by Bayesian inference. The posterior distribution of the parameters given the data—the solution of Bayesian inference—is found through computationally expensive sampling methods, which require over $\mathcal{O}$(10 5 ) evaluations of the forward model; this is prohibitive for many models. We present a proof of concept of Bayesian learning applied to a new bulk microphysics scheme named “Cloudy,” using the recently developed Calibrate-Emulate-Sample (CES) algorithm. Cloudy models collision-coalescence and collisional breakup of cloud droplets with an adjustable number of prognostic moments and with easily modifiable assumptions for the cloud droplet mass distribution and the collision kernel. The CES algorithm uses machine learning tools to accelerate Bayesian inference by reducing the number of forward evaluations needed to $\mathcal{O}$(10 2 ). It also exhibits a smoothing effect when forward evaluations are polluted by noise. In a suite of perfect-model experiments, we show that CES enables computationally efficient Bayesian inference of parameters in Cloudy from noisy observations of moments of the droplet mass distribution. In an additional imperfect-model experiment, a collision kernel parameter is successfully learned from output generated by a Lagrangian particle-based microphysics model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Initial development of a generic fluoride salt-cooled reactor model

Fluoride high-temperature reactors (FHRs) are high-temperature, low-pressure reactor concepts that use tri-structural isotropic (TRISO) fuel and molten fluoride salt coolant. These reactors have the potential to provide both electrical power and high-temperature process heat. We used generic FHR parameters for a pebble-bed FHR to develop an initial model with fresh fuel for a generic FHR (gFHR) in MELCOR and SCALE (NEWT and KENO). In this paper, we present the development of our gFHR models, which will serve as the baseline for a sensitivity and uncertainty analysis to quantify the range of possible source terms for FHRs in severe accidents. We present MELCOR results for fuel and coolant temperatures through the core, a nodalization study for the steady-state thermal hydraulic model, and development of reactor physics models in SCALE. As this work progresses, these models will be used to calculate source terms for a loss-of-forced-flow accident and to conduct a sensitivity study on this accident to establish a range of possible source terms. SCALE will provide reactor physics parameters like isotopic inventory, decay heat generation, and temperature coefficients of reactivity. Using the uncertainty quantification tools within SCALE, we will generate distributions for those parameters and will use the uncertainty quantification code RAVEN or DAKOTA to sample those distributions in MELCOR to quantify the impact of reactor physics and thermal hydraulic uncertainties on FHR source terms. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

On the Generalizability of Time-of-Flight Convolutional Neural Networks for Noninvasive Acoustic Measurements

Bulk wave acoustic time-of-flight (ToF) measurements in pipes and closed containers can be hindered by guided waves with similar arrival times propagating in the container wall, especially when a low excitation frequency is used to mitigate sound attenuation from the material. Convolutional neural networks (CNNs) have emerged as a new paradigm for obtaining accurate ToF in non-destructive evaluation (NDE) and have been demonstrated for such complicated conditions. However, the generalizability of ToF-CNNs has not been investigated. In this work, we analyze the generalizability of the ToF-CNN for broader applications, given limited training data. We first investigate the CNN performance with respect to training dataset size and different training data and test data parameters (container dimensions and material properties). Furthermore, we perform a series of tests to understand the distribution of data parameters that need to be incorporated in training for enhanced model generalizability. This is investigated by training the model on a set of small- and large-container datasets regardless of the test data. We observe that the quantity of data partitioned for training must be of a good representation of the entire sets and sufficient to span through the input space. The result of the network also shows that the learning model with the training data on small containers delivers a sufficiently stable result on different feature interactions compared to the learning model with the training data on large containers. To check the robustness of the model, we tested the trained model to predict the ToF of different sound speed mediums, which shows excellent accuracy. Furthermore, to mimic real experimental scenarios, data are augmented by adding noise. We envision that the proposed approach will extend the applications of CNNs for ToF prediction in a broader range.

47 OTHER INSTRUMENTATION↗
NASA NTRS2011

Probabilistic Analysis of a Composite Crew Module

An approach for conducting reliability-based analysis (RBA) of a Composite Crew Module (CCM) is presented. The goal is to identify and quantify the benefits of probabilistic design methods for the CCM and future space vehicles. The coarse finite element model from a previous NASA Engineering and Safety Center (NESC) project is used as the baseline deterministic analysis model to evaluate the performance of the CCM using a strength-based failure index. The first step in the probabilistic analysis process is the determination of the uncertainty distributions for key parameters in the model. Analytical data from water landing simulations are used to develop an uncertainty distribution, but such data were unavailable for other load cases. The uncertainty distributions for the other load scale factors and the strength allowables are generated based on assumed coefficients of variation. Probability of first-ply failure is estimated using three methods: the first order reliability method (FORM), Monte Carlo simulation, and conditional sampling. Results for the three methods were consistent. The reliability is shown to be driven by first ply failure in one region of the CCM at the high altitude abort load set. The final predicted probability of failure is on the order of 10-11 due to the conservative nature of the factors of safety on the deterministic loads.

FROM↗
DOE OSTI2022

Rain Drop Size Distributions Estimated from NOAA Snow-Level Radar Data

Using NOAA’s S-band High-Power Snow-Level Radar (HPSLR), a technique for estimating the rain drop size distribution (DSD) above the radar is presented. This technique assumes the DSD can be described by a four parameter, generalized gamma distribution (GGD). Using the radar’s measured average Doppler velocity spectrum and a value (assumed, measured, or estimated) of the vertical air motion w, an estimate of the GGD is obtained. Four different methods can be used to obtain w. One method that estimates a mean mass-weighted raindrop diameter D m from the measured reflectivity Z produces realistic DSDs compared to prior literature examples. These estimated DSDs provide evidence that the radar can retrieve the smaller drop sizes constituting the “drizzle” mode part of the DSD. Here, this estimation technique was applied to 19 h of observations from Hankins, North Carolina. Results support the concept that DSDs can be modeled using GGDs with a limited range of parameters. Further work is needed to validate the described technique for estimating DSDs in more varied precipitation types and to verify the vertical air motion estimates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Constraining the nuclear spin distribution using improved 197 Au neutron resonance parameters

New neutron transmission data at resonance energies using a 197 Au sample were measured using an early version of the Device for Indirect Capture Experiments on Radionuclides (DICER), which is under development at the Los Alamos Neutron Science Center (LANSCE). These data were combined with previous neutron transmission and capture data in a simultaneous R-matrix analysis to extract improved neutron resonance parameters for this nuclide. As a result, total radiation widths, Γ γ , were obtained for 33 J=1 and 44 J=2 197 Au+n resonances. Γ γ distributions for these two spins states were compared to distributions calculated according to the nuclear statistical model using published nuclear level density (NLD) and photon strength functions (PSF) measured using the Oslo technique. The calculated distributions were found to be narrower and the average values for the two spins states closer together than the data. The calculation can be brought into agreement with the data by substantial modifications to the spin distribution in 198 Au as a function of excitation energy. As far as we know, the spin distribution currently is otherwise poorly constrained. The modified spin distribution changes the shapes of the NLD and PSF extracted using the Oslo technique and so could have broad implications.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1981

The distribution of free electrons in the inner galaxy from pulsar dispersion measures

The dispersion measures of a sample of 149 pulsars in the inner Galaxy (absolute value of l 50 deg) were statistically analyzed to deduce the large-scale distribution of free thermal electrons in this region. The dispersion measure distribution of these pulsars shows significant evidence for a decrease in the electron scale height from a local value greater than the pulsar scale height to a value less than the pulsar scale height at galactocentric radii inside of approximately 7 kpc. An increase in the electron density (to a value around .15/cu cm at 4 to 5 kpc) must accompany such a decrease in scale height. There is also evidence for a large-scale warp in the electron distribution below the b + 0 deg plane inside the Solar circle. A model is proposed for the electron distribution which incorporates these features and Monte Carlo generated dispersion measure distributions are presented for parameters which best reproduce the observed pulsar distributions.

Harding, D. S.↗
NASA NTRS1991

Shot model parameters for Cygnus X-1 through phase portrait fitting

Shot models for systems having about 1/f power density spectrum are developed by utilizing a distribution of shot durations. Parameters of the distribution are determined by fitting the power spectrum either with analytic forms for the spectrum of a shot model with a given shot profile, or with the spectrum derived from numerical realizations of trial shot models. The shot fraction is specified by fitting the phase portrait, which is a plot of intensity at a given time versus intensity at a delayed time and in principle is sensitive to different shot profiles. These techniques have been extensively applied to the X-ray variability of Cygnus X-1, using HEAO 1 A-2 and an Exosat ME observation. The power spectra suggest models having characteristic shot durations lasting from milliseconds to a few seconds, while the phase portrait fits give shot fractions of about 50 percent. Best fits to the portraits are obtained if the amplitude of the shot is a power-law function of the duration of the shot. These fits prefer shots having a symmetric exponential rise and decay. Results are interpreted in terms of a distribution of magnetic flares in the accretion disk.

Lochner, James C.↗
NASA NTRS1997

Rapid Temporal Changes of Midtropospheric Winds

The statistical distribution of the magnitude of the vector wind change over 0.25-, 1-, 2-. and 4-h periods based on data from October 1995 through March 1996 over central Florida is presented. The wind changes at altitudes from 6 to 17 km were measured using the Kennedy Space Center 50-MHz Doppler radar wind profiler. Quality controlled profiles were produced every 5 min for 112 gates, each representing 150 m in altitude. Gates 28 through 100 were selected for analysis because of their significance to ascending space launch vehicles. The distribution was found to be lognormal. The parameters of the lognormal distribution depend systematically on the time interval. This dependence is consistent with the behavior of structure functions in the f(exp 5/3) spectral regime. There is a small difference between the 1995 data and the 1996 data, which may represent a weak seasonal effect.

Merceret, Francis J.↗
NASA NTRS1991

Procedures for Including Secondary Electron Emission in Numerical Simulations of Plasma-Insulator Interactions

Previous Monte Carlo simulations provide a data base for properties of secondary electron emission (SEE) from insulators and metals. Incident primary electrons are considered at energies up to 1200 eV. The behavior of secondary electrons is characterized by (1) yield vs. primary energy E(sub p), (2) distribution vs. secondary energy E(sub s), and (3) distribution vs. angle of emission theta. Special attention is paid to the low energy range E(sub p) up to 50 eV, where the number and energy of secondary electrons is limited by the finite band gap of the insulator. For primary energies above 50 eV the SEE yield curve can be conveniently parameterized by a Haffner formula. The energy distribution of secondary electrons is described by an empirical formula with average energy about 8.0 eV. The angular distribution of secondaries is slightly more peaked in the forward direction than the customary cos theta distribution. Empirical formulas and parameters are given for all yield and distribution curves. Procedures and algorithms are described for using these results to find the SEE yield, and then to choose the energy and angle of emergence of each secondary electron. These procedures can readily be incorporated into numerical simulations of plasma-solid surface interactions in low earth orbit.

Beyst, Brian↗
NASA NTRS1975

Procedures for the design of low-pitching-moment airfoils

The pitching moment of a given airfoil is decreased by specifying appropriate modifications to its pressure distribution; by prescribing parameters in a special formula for the Theodorsen epsilon-function; and with superposition of a thickness distribution and subsequent tailoring. Advantages and disadvantages of the three methods are discussed.

Barger, R. L.↗
NASA NTRS1975

Land use classification for hydrologic models using interactive machine classification of LANDSAT data

Models designed to simulate the hydrology of urban areas require input parameters describing the land use and degree of imperviousness of the watershed. Unfortunately, the magnitude and spatial distribution of these parameters are rather difficult to estimate when a large watershed is involved. Trade-offs between accuracy of the model parameters and the time or money available for their determination must be made. Because of the necessity of such trade-offs, a study was developed to investigate the use of computer aided analysis of LANDSAT multispectral data in estimating percent of imperviousness and associated land uses needed in urban hydrologic modeling. An interactive computer was used to delineate seven land use classifications in the 342 sq. km. Maryland portion of the Anacostia River Basin from LANDSAT data. These results compared favorably with those of an earlier study which obtained the same information through analysis of aerial photographs having a scale of 1:4800. Approximately 94 man days were required to complete the land use analysis using the aerial photographs while less than three man days were required to accomplish similar tasks using the LANDSAT data.

Thomas J. Jackson↗
NASA NTRS1985

The cleft ion fountain - A two-dimensional kinetic model

The transport of ionospheric ions from a source in the polar cleft ionosphere through the polar magnetosphere is investigated using a two-dimensional, kinetic, trajectory-based code. The transport model includes the effects of gravitation, longitudinal magnetic gradient force, convection electric fields, and parallel electric fields. Individual ion trajectories as well as distribution functions and resulting bulk parameters of density, parallel average energy, and parallel flux for a presumed cleft ionosphere source distribution are presented for various conditions to illustrate parametrically the dependences on source energies, convection electric field strengths, ion masses, and parallel electric field strengths. The essential features of the model are consistent with the concept of a cleft-based ion fountain supplying ionospheric ions to the polar magnetosphere, and the resulting plasma distributions and parameters are in general agreement with recent low-energy ion measurements from the DE 1 satellite.

Horwitz, J. L.↗
DOE OSTI2024

Parametric description of intermittent probability distribution functions in solar wind and magnetohydrodynamic turbulence

ABSTRACT In this work, we find empirical evidence that the scale-dependent statistical properties of solar wind and magnetohydrodynamic (MHD) turbulence can be described in terms of a family of parametric probability distribution functions (PDFs) known as Normal Inverse Gaussian (NIG). Understanding these PDFs is one of the most important goals in turbulence theory, as they are inherently connected to the intermittent properties of solar wind turbulence. We investigate the properties of PDFs of Elsasser increments based on a large statistical sample from solar wind observations and high-resolution numerical simulations of MHD turbulence. In order to measure the PDFs and their corresponding properties, three experiments are presented: fast and slow solar wind for experimental data and a simulation of reduced MHD (RMHD) turbulence. Conditional statistics on a 23-yr-long sample of WIND data near 1 au and high-resolution pseudo-spectral simulation of steadily driven RMHD turbulence on a $2048^3$ mesh are used to construct scale-dependent PDFs. The empirical PDFs are fitted to NIG distributions, which depend on four free parameters. Our analysis shows that NIG distributions accurately capture the evolution of the PDFs, with scale-dependent parameters, from large scales characterized by a Gaussian distribution, turning to exponential tails within the inertial range and stretched exponentials at dissipative scales. We also show that empirically-measured NIG parameters exhibit well-defined scaling properties that are similar across the three empirical data sets, which may be indicative of universal behaviour.

Astronomy & Astrophysics↗