SEARCH · Search NASA

Results for “Probability Distribution Function”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26

All sources NASA NTRS DOE OSTI

NASA NTRS2017

Scale Dependence of Multiplier Distributions for Particle Concentration, Enstrophy, and Dissipation in the Inertial Range of Homogeneous Turbulence

Turbulent flows preferentially concentrate inertial particles depending on their stopping time or Stokes number, which can lead to significant spatial variations in the particle concentration. Cascade models are one way to describe this process in statistical terms. Here, we use a direct numerical simulation (DNS) dataset of homogeneous, isotropic turbulence to determine probability distribution functions (PDFs) for cascade multipliers, which determine the ratio by which a property is partitioned into subvolumes as an eddy is envisioned to decay into smaller eddies. We present a technique for correcting effects of small particle numbers in the statistics. We determine multiplier PDFs for particle number, flow dissipation, and enstrophy, all of which are shown to be scale dependent. However, the particle multiplier PDFs collapse when scaled with an appropriately defined local Stokes number. As anticipated from earlier works, dissipation and enstrophy multiplier PDFs reach an asymptote for sufficiently small spatial scales. From the DNS measurements, we derive a cascade model that is used it to make predictions for the radial distribution function (RDF) for arbitrarily high Reynolds numbers, Re, finding good agreement with the asymptotic, infinite Re inertial range theory of Zaichik and Alipchenkov [New J. Phys. 11, 103018 (2009)]. We discuss implications of these results for the statistical modeling of the turbulent clustering process in the inertial range for high Reynolds numbers inaccessible to numerical simulations.

turbulent flows↗

NASA NTRS2016

A Multi-Sensor Water Vapor, Temperature and Cloud Climate Data Record: Status of Our MEaSUREs 2012 Project

- Our primary objectives: Incorporate all A-Train water vapor, including small-scale, horizontal and vertical water vapor structure from MODIS (Moderate Resolution Imaging Spectroradiometer) and GPS (Global Positioning System); Use MODIS cloud classification that is collocated at the pixel-scale over the full AIRS/AMSU (Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit) swath; Extend summaries to PDFs (Probability Distribution Functions)/higher order moments of water vapor sorted by cloud classes; Establish robust, scale-dependent statistical relationships between cloud, temperature, and water vapor PDFs for the climate modeling community. - We will also: Update our current data record to new CloudSat/CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) cloud classification; Provide merged AIRS and MLS (Microwave Limb Sounder) water vapor profiles using averaging kernels; Include temperature and water vapor from models (ECMWF - European Centre for Medium-Range Weather Forecasts) and re-analyses (MERRA - Modern Era-Retrospective Analysis for Research and Applications); Classify METOP-A (Meteorological Operational Satellite-A) and NOAA (National Oceanic and Atmospheric Administration) satellite water vapor with AVHRR (Advanced Very High Resolution Radiometer) during SNOs (Solar Neutrino Oscillations); Compare with the NVAP-M (re-analysis and extension of the NASA Water Vapor Project (NVAP)) water vapor climatologies; Use existing data sources and mature algorithms, and document processing algorithms.

Fetzer, Eric↗

NASA NTRSDate not supplied

Assessing the Viability of Using GEOS-Forecast Product for Landslides Forecasting: A Step Toward Early Warning System

Landslides across the globe are mostly triggered by extreme rainfall events affecting infrastructure, transportation and livelihoods. The risks are rarely quantified due to lack of data, analytical skills and limited modeling techniques. Knowledge of local to global scale landslide risks provides communities and national agencies the ability to adapt disaster management practices to mitigate and recover from these hazards. In order to minimize the risks and improve characterization of community resilience to landslides, it is vital to have reliable information about the factors triggering landslides such as rainfall, well ahead in time. Forecasting potential landslide activity and impacts can be achieved through reliable precipitation forecast models. However, it is challenging because of the temporal and spatial variability of precipitation, an important factor in triggering landslides. Evaluation of the precipitation field, associated errors, and sampling uncertainties is integral for development of efficient and reliable landslide forecasting and early warning system. This study develops a methodology to assess the viability of using a precipitation field provided by a global model and its potential integration in the landslide forecasting system. The study focuses on the comparison between the IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Mission) and GEOS (NASA Goddard Earth Observing System)-Forecast product over contiguous United States (CONUS). GEOS model assimilates new observations every 6 hours, at 00, 06, 12, and 18 UTC. The framework is tested on the GEOS-Forecast Model initialized at 00 UTC using daily IMERG early product as reference using both categorical and continuous statistics. The categorical statistics includes the probability of detection (POD), success ratio (SR), critical success index (CSI), and the hit bias. Continuous statistics such as correlation, normalized standard deviation, and root-mean-square error are also evaluated. Overall, GEOS-Forecast precipitation field over the analysis period (~1 year) show underestimation with respect to IMERG early for the daily accumulated rainfall. However, the probability distribution function and cumulative distribution function of both show similar patterns. In terms of correlations, POD, SR, CSI, hit bias, the performance varies with respect to the rainfall threshold used.

Sana Khan↗

NASA NTRS2001

Laboratory characterization of silicon avalanche photodiodes (APD) for pulse position modulation (PPM) detection

Two commercially available large area silicon avalanche photodiodes (APD) were characterized in the laboratory. Specifically, the response of the APD’s to a sequence of 8-bit pulse position modulated (256-PPM) laser pulses, with and without background noise, was recorded and stored for post analysis. Empirical probability distribution functions (pdf) were constructed from the signal and noise slot data and compared to pdf’s predicted by an analytical model based on Webb/Gaussian statistics. The limited pulse sequence was also used to generate bit-error rate (BER) versus signal photons per pulse values, albeit with large error bars. These BER measurements were also compared to results predicted using the Gaussian and Webb/Gaussian models for APD channel statistics. While the measurements qualitatively reflect features predicted by theory, significant quantitative deviations are displayed between the measurements and theory. The source of these discrepancies is not currently well understood, but it is surmised that inaccurate knowledge of detector parameters such as gain and noise equivalent temperature models may explain the discrepancies.

Biswas, A.↗

NASA NTRSDate not supplied

Sensitivity Study of Dust-Laden Flows in the DLR GBK Facility using the DUST-US3D Framework

The current work is aimed at performing a detailed numerical sensitivity study of dust-laden flow through the DLR GBK facility using the DUST-US3D frame-work. A three-dimensional volumetric grid of the GBK facility with emphasis on a precise reproduction of the dust particle injection process and the transit of particles through the facility has been developed for this work. The sensitivity study on this grid involves varying particle injection parameters such as the diameter distribution probability function, input velocity magnitude, and input velocity direction and accounting for particle-particle collisions. The resulting impact on particle-related statistics such as the dust mass flow rate is studied, and comparisons made to experimental data.

Dusty Flows↗

NASA NTRSDate not supplied

Modeling the Meteoroid Environment Far from the Ecliptic Plane: Can a Tilted Plane of Symmetry Explain Seasonal Variations in the Meteoroid Environment?

NASA's Meteoroid Engineering Model (MEM) describes the meteoroid environment encountered by spacecraft in the inner solar system. MEM's algorithms take advantage of the fact that the vast majority of spacecraft remain close to the ecliptic plane in order to make several simplifying assumptions. However, this results in a model that cannot describe the environment for spacecraft such as Ulysses that travel far from the ecliptic, and limits the potential to validate the model using impact signatures from asteroids on inclined orbits. We are in the process of developing a new version of the code, numbered 3.1-alpha, that correctly computes the meteoroid flux and directionality far from the ecliptic. We present a new formulation of the spatial probability distribution function for fully precessed meteoroid models and compare our results with zodiacal light data.

Althea Moorhead↗

NASA NTRS2023

Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within-The-Atmosphere Observations: A Three-Way Street

Aerosol forcing uncertainty represents the largest climate forcing uncertainty overall. Its magnitude has remained virtually undiminished over the past 20 years despite considerable advances in understanding most of the key contributing elements. Recent work has produced modest increases only in the confidence of the uncertainty estimate itself. This review summarizes the contributions toward reducing the uncertainty in the aerosol forcing of climate made by satellite observations, measurements taken within the atmosphere, as well as modeling and data assimilation. We adopt a more measurement-oriented perspective than most reviews of the subject in assessing the strengths and limitations of each; gaps and possible ways to fill them are considered. Currently planned programs supporting advanced, global-scale satellite and surface-based aerosol, cloud, and precursor gas observations, climate modeling, and intensive field campaigns aimed at characterizing the underlying physical and chemical processes involved, are all essential. But in addition, new efforts are needed: (a) to obtain systematic aircraft in situ measurements capturing the multi-variate probability distribution functions of particle optical, microphysical, and chemical properties (and associated uncertainty estimates), as well as co-variability with meteorology, for the major aerosol airmass types; (b) to conceive, develop, and implement a suborbital (aircraft plus surface-based) program aimed at systematically quantifying the cloud-scale microphysics, cloud optical properties, and cloud-related vertical velocities associated with aerosol-cloud interactions; and (c) to focus much more research on integrating the unique contributions of satellite observations, suborbital measurements, and modeling, to reduce the persistent uncertainty in aerosol climate forcing.

aerosol climate forcing↗

DOE OSTI2013

Long-Term Probability Distribution of Wind Turbine Planetary Bearing Loads (Poster)

Among the various causes of bearing damage and failure, metal fatigue of the rolling contact surface is the dominant failure mechanism. The fatigue life is associated with the load conditions under which wind turbines operate in the field. Therefore, it is important to understand the long-term distribution of the bearing loads under various environmental conditions. The National Renewable Energy Laboratory's 750-kW Gearbox Reliability Collaborative wind turbine is studied in this work. A decoupled analysis using several computer codes is carried out. The global aero-elastic simulations are performed using HAWC2. The time series of the drivetrain loads and motions from the global dynamic analysis are fed to a drivetrain model in SIMPACK. The time-varying internal pressure distribution along the raceway is obtained analytically. A series of probability distribution functions are then used to fit the long-term statistical distribution at different locations along raceways. The long-term distribution of the bearing raceway loads are estimated under different environmental conditions. Finally, the bearing fatigue lives are calculated.

17 WIND ENERGY↗

DOE OSTI2014

A stochastic analysis of steady and transient heat conduction in random media using a homogenization approach

We present a new stochastic analysis for steady and transient one-dimensional heat conduction problem based on the homogenization approach. Thermal conductivity is assumed to be a random field K consisting of random variables of a total number N. Both steady and transient solutions T are expressed in terms of the homogenized solution (symbol) and its spatial derivatives (equation), where homogenized solution (symbol) is obtained by solving the homogenized equation with effective thermal conductivity. Both mean and variance of stochastic solutions can be obtained analytically for K field consisting of independent identically distributed (i.i.d) random variables. The mean and variance of T are shown to be dependent only on the mean and variance of these i.i.d variables, not the particular form of probability distribution function of i.i.d variables. Variance of temperature field T can be separated into two contributions: the ensemble contribution (through the homogenized temperature (symbol)); and the configurational contribution (through the random variable Ln(x)Ln(x)). The configurational contribution is shown to be proportional to the local gradient of (symbol). Large uncertainty of T field was found at locations with large gradient of (symbol) due to the significant configurational contributions at these locations. Numerical simulations were implemented based on a direct Monte Carlo method and good agreement is obtained between numerical Monte Carlo results and the proposed stochastic analysis.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2013

Uncertainty of Personnel Dosimetry Systems Using Performance Data Testing

While accreditation programs require a study of uncertainty for each dosimeter model for which accreditation is sought (Torres 2005, DOE 1986), no one method is specifically recommended by accrediting bodies. Typically, the methods used to determine uncertainty have included the partial differentiation method described in the U.S. Guide to Uncertainty in Measurements (ANSI 1997) or the use of Monte Carlo techniques and probability distribution functions to generate simulated dose results. Each of these techniques has particular strengths and should be employed when the areas of uncertainty are required to be understood in detail. However, the uncertainty of dosimeter results can also be determined using a Model II One-Way Analysis of Variance technique and accreditation testing data (Cummings and Flood 2010). The strengths of this method are (1) the method is based in robust and well-accepted statistical techniques; (2) the method is straightforward and easily applied to dose algorithms, which are so complex as to render the use of other methods unwieldy; and (3) the method uses dosimeter performance data obtained during accreditation. The general method and criteria of inclusion of radiation types and fields are discussed.

Cummings, Frederick (Rick) M.↗

DOE OSTI2013

A Study of Turbulence-Driven Inward Momentum Flux & Construction of a Difference Scheme for Fokker-Planck-Landau Operator

Momentum transport driven by microinstability is studied via analytic theories, gyrokinetic simulations, and experimental data analysis. A scenario for rotation profile evolution is constructed based on experimental observation that parallel flow initiates at edge region and penetrates into core region of tokamak [1]. In particular, turbulent diffusion and inward convective pinch are studied according to this scenario. For study of convective momentum pinch, toroidal ion temperature gradient mode near marginal stability is examined in kinetic limit. It is found from quasilinear estimation that the inward turbulent equipartition momentum pinch remains as the most robust part of pinch. In addition, it is found that ion temperature gradient driven momentum pinch is inward for typical parameters, while ion density gradient driven momentum pinch is outward. It is shown by theoretical formulas and experimental data analysis that momentum pinch velocity is sensitive to the electron to ion temperature ratio. For study of diffusive momentum transport, parallel shear flow instability is studied in fluid limit. Diffusion coefficient using mixing length estimate from nonlocal theory reveals that it has a strong dependence on the parallel flow shear and is mainly carried by fluctuations in short wavelength regime. Gyrokinetic simulation of collisionless parallel shear flow instability shows that the analytic prediction of diffusion coefficient is comparable to numerical values in the linear phase of the simulations. In the nonlinear phase, fluctuation energy gets transferred to the long wavelength regime. A two-dimensional, uniform velocity-grid based solver for the nonlinear Fokker-Planck-Landau (FPL) collision operator has been developed in the limit of strong magnetic field. The conservative and entropy increasing semidiscretized numerical scheme for the isotropic FPL equation by C. Buet et al. (2002) has been extended to 2D velocity space. A Picard fixed point method is used for semi-implicit time advance. One time evaluation of interpolation coefficients for probability distribution function is introduced so that numerical equilibrium is achieved with Maxwellian. However, positivity property is sacrificed at the saving of computational costs. In addition, a highly practical method is suggested to apply the velocity-grid based collision solver to particle-in-cell code.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2017

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method: Preprint

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power, and they are currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.

17 WIND ENERGY↗

DOE OSTI2017

Grid Frequency Extreme Event Analysis and Modeling: Preprint

Sudden losses of generation or load can lead to instantaneous changes in electric grid frequency and voltage. Extreme frequency events pose a major threat to grid stability. As renewable energy sources supply power to grids in increasing proportions, it becomes increasingly important to examine when and why extreme events occur to prevent destabilization of the grid. To better understand frequency events, including extrema, historic data were analyzed to fit probability distribution functions to various frequency metrics. Results showed that a standard Cauchy distribution fit the difference between the frequency nadir and prefault frequency (f_(C-A)) metric well, a standard Cauchy distribution fit the settling frequency (f_B) metric well, and a standard normal distribution fit the difference between the settling frequency and frequency nadir (f_(B-C)) metric very well. Results were inconclusive for the frequency nadir (f_C) metric, meaning it likely has a more complex distribution than those tested. This probabilistic modeling should facilitate more realistic modeling of grid faults.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Generalized Bayesian Framework for Evaluation of Integral Benchmark Experiments

A recently published generalized Bayesian optimization framework has provided a way to retract any or all of the three common assumptions underlying the conventional Generalized Linear Least Squares (GLLS) optimization method based on the concepts introduced in reference two. These assumptions are: 1. Perfection: The model used for data evaluation and the prior probability distribution function (PDF) of generalized* data are perfect; 2. Normality: The prior and posterior PDF are normal; and 3. Linearity: The model is linear. In this work we outline how the framework in 1 could be directly adopted for improved evaluation of nuclear criticality integral benchmark experiments (IBEs) by: 1. Removing the first assumption alone by utilizing the concept of imperfections introduced in 1 to enable evaluation in the presence of discrepancies between the data and model or of missing covariance information by a GLLS method that will be seen as a generalization of the conventional GLLS method employed by the TSURFER code, and by 2. Removing the remaining two assumptions by implement- ing a Markov Chain Monte Carlo method for computation of the posterior PDF in the SAMPLER code, where TSURFER and SAMPLER are the uncertainty quantification (UQ) codes for IBEs in the SCALE code system based on the GLLS, and the stochastic method, respectively.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Bayesian Monte Carlo Evaluation Framework for Imperfect Data and Models [Abstract]

Nuclear data evaluation methods conventionally make the following assumptions: prior and posterior probability distribution functions (PDFs) of all model parameters and data are normal (Gaussian); the linear approximation is sufficiently accurate for minimization of a cost function (even for non-linear models); and that both the model (of, e.g., neutron cross section) and experimental data (including their covariance data) are perfect. These assumptions are inherent to the well-known generalized linear least squares (GLLS) minimization method commonly used for evaluations of resolved resonance region (RRR) neutron cross sections. However, these assumptions are often not justified due to the presence of non-normal PDFs, non-linear models (e.g. R -matrix formalism), and inherent imperfections in data and models (e.g. discrepant data sets, discrepancies between the previous evaluation and newly measured data, or imperfect covariance data). We remove the said assumptions in a mathematical framework of Bayes’ theorem, and implement it using the Metropolis-Hastings Monte Carlo method. Parameters of a new kind are introduced to parameterize inherent imperfections, e.g. , any discrepancies between the theoretical model and measured data. These new parameters enable evaluators to quantify their expert judgement about any discrepancies or imperfections in a reproducible manner. We demonstrate the framework with an ongoing evaluation of 233 U in the eV region using the ENDF-B/VIII library and transmission data measured by Guber, et al. , and compare the posterior parameters to those obtained by conventional evaluation methods.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2013

Estimating anisotropic diffusion of neutrons near the boundary of a pebble bed random system

Due to the arrangement of the pebbles in a Pebble Bed Reactor (PBR) core, if a neutron is located close to a boundary wall, its path length probability distribution function in directions of flight parallel to the wall is significantly different than in other directions. Hence, anisotropic diffusion of neutrons near the boundaries arises. We describe an analysis of neutron transport in a simplified 3-D pebble bed random system, in which we investigate the anisotropic diffusion of neutrons born near one of the system's boundary walls. While this simplified system does not model the actual physical process that takes place near the boundaries of a PBR core, the present work paves the road to a formulation that may enable more accurate diffusion simulations of such problems to be performed in the future. Monte Carlo codes have been developed for (i) deriving realizations of the 3-D random system, and (ii) performing 3-D neutron transport inside the heterogeneous model; numerical results are presented for three different choices of parameters. These numerical results are used to assess the accuracy of estimates for the mean-squared displacement of neutrons obtained with the diffusion approximations of the Atomic Mix Model and of the recently introduced [1] Non-Classical Theory with angular-dependent path length distribution. The Non-Classical Theory makes use of a Generalized Linear Boltzmann Equation in which the locations of the scattering centers in the system are correlated and the distance to collision is not exponentially distributed. We show that the results predicted using the Non-Classical Theory successfully model the anisotropic behavior of the neutrons in the random system, and more closely agree with experiment than the results predicted by the Atomic Mix Model. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2015

Calculation and use of pointwise effective cross-sections in the unresolved resonance region - 14605

The pointwise neutron elastic scattering, absorption, fission and total cross-sections in the Unresolved Resonance Region (URR) can be calculated with a code C-007 [1]. The volume of these data is large and estimated to make about 106 numbers for each of the reactions. These data volumes may cause time and memory problems in solving radiation transport by the Monte Carlo method. There are methods to thin the data. These are, for example, the widely used probability tables [2] and subgroups [3]. The paper offers another data thinning approach which was successfully used for the Resolved Resonance Region (RRR) [4,5]. It transforms the pointwise resonance cross-sections into the pointwise monotone ones within some energy intervals with the use of cross-section probability distribution functions P(σ). A similar approach was used to calculate pointwise cross-sections in the URR [6]. (authors)

42 ENGINEERING↗

DOE OSTI2022

Thermal scattering law for structure-dependent-Doppler broadening in FLASSH

Traditional Doppler broadening methods assume that target nuclei velocities follow a Maxwell-Boltzmann distribution which inherently assumes that the atoms are a free gas or that their velocities are independent of each other. This approximation is reasonable at high target temperatures and high neutron energies. However, nuclei are not independent: the lattice temperature and chemical binding will define the atomic motion. The thermal scattering law (i.e., TSL or S(α,β)) is a material property which describes the chemical binding and temperature response in terms of available momentum and energy states. In the thermal energy range, this TSL will define the thermal scattering cross sections. In the resonance region, the TSL offers a first-principles description of the probability distribution function for the velocity of the target nuclei. Using the TSL in Doppler broadening captures the structure of the material consistently from the thermal range into the resonance cross sections. In this work, both traditional free-gas and precise TSL Doppler broadening methods for resonance cross section evaluation have been implemented within the Full Law Analysis Scattering System Hub (FLASSH). This framework provides a generalized formulation for evaluating low-lying resonance data and streamlines nuclear data processing methods. TSL data generated using ab initio lattice dynamics (AILD) can be used to broaden cross section libraries in ENDF format for high-fidelity input into reactor physics calculations. These capabilities are demonstrated for the lowest absorption resonances of {sup 238}U in UO{sub 2}, UC and UN. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗