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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

New Approaches to Quantifying Transport Model Error in Atmospheric CO2 Simulations

In recent years, much progress has been made in observing CO2 distributions from space. However, the use of these observations to infer source/sink distributions in inversion studies continues to be complicated by difficulty in quantifying atmospheric transport model errors. We will present results from several different experiments designed to quantify different aspects of transport error using the Goddard Earth Observing System, Version 5 (GEOS-5) Atmospheric General Circulation Model (AGCM). In the first set of experiments, an ensemble of simulations is constructed using perturbations to parameters in the model s moist physics and turbulence parameterizations that control sub-grid scale transport of trace gases. Analysis of the ensemble spread and scales of temporal and spatial variability among the simulations allows insight into how parameterized, small-scale transport processes influence simulated CO2 distributions. In the second set of experiments, atmospheric tracers representing model error are constructed using observation minus analysis statistics from NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA). The goal of these simulations is to understand how errors in large scale dynamics are distributed, and how they propagate in space and time, affecting trace gas distributions. These simulations will also be compared to results from NASA's Carbon Monitoring System Flux Pilot Project that quantified the impact of uncertainty in satellite constrained CO2 flux estimates on atmospheric mixing ratios to assess the major factors governing uncertainty in global and regional trace gas distributions.

Ott, L.↗
DOE OSTI2022

Simulating self-powered neutron detector responses to infer burnup-induced power distribution perturbations in next-generation light water reactors

Understanding how 3D power distribution will be monitored throughout reactor core volumetric space in next-generation nuclear power reactors is crucial to the design, deployment, and licensing of these reactors. Although numerous techniques exist for 3D power distribution monitoring based on the response of both in situ and ex situ sensors currently implemented or proposed for use in the US reactor fleet, crucial details about these techniques are often unclear. The publicly available documentation does not include information such as how well these techniques are characterized and optimized in their implementations and the levels of uncertainty in the inferred 3D power distribution. The work described herein investigated a recently developed 3D power distribution inferencing method as applied to two next-generation reactor simulations: (1) the NuScale small modular reactor design and (2) the Westinghouse AP1000 design, both of which contain in-core strings of vanadium self-powered neutron detectors (SPNDs). This investigation considered a range of SPND string sensor densities, as well as a range of 3D power distribution axial segment sizes. In this work, SPND response simulation is informed by neutron flux calculations in representative homogenized cores. For the different sensor densities and power distribution axial segment sizes in these simulations, the average solution error, solver iterations, and run time were tracked to parameterize the sensor-core configuration.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Machine learning assisted bayesian inference of mix and hot-spot conditions in NIF implosions

Experiments on the National Ignition Facility (NIF) have provided clear evidence of ablator material mixing into the Hot-Spot, leading to degraded performance. However, inferring the amount of mix and Hot-Spot conditions from typical experimental observations (e.g. x-ray spectra and images) is highly challenging. Here, we have developed an analysis method that utilizes machine learning assisted Bayesian inference to find the probability distributions of the Hot-Spot and mix conditions. This approach uses a neural network, trained on an idealized 2-dimensional representation of the Hot-Spot and mix distribution, and Bayesian inference to find the statistical distributions of Hot-Spot conditions that provide a match with observations. We have tested this method with synthetic data from simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1980

A comparison of secondary craters on the moon, Mercury, and Mars

The present investigation is concerned with the distribution of secondary crater sizes and shapes and the relation of these distributions to inferred Martian lithology and ballistic history. Differences in secondary-crater size/range distributions around unmodified craters can be attributed to differences in gravitational potential, lithologic properties, and atmospheric interactions. Wide variations found in secondary crater populations around medium-size (5-50 km) Martian craters can be interpreted as effects of contrasting target lithologies. Extensive secondary cratering and elongate secondaries of Crater I in Chryse Planitia are consistent with a competent lithology of basaltic plains, as inferred from various photogeologic studies. The relatively few large secondaries around Arandas, and their high circularity, are consistent with a lithology exhibiting low yield strength, as inferred for the fractured plains.

Schultz, P. H.↗
DOE OSTI2021

Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks

Distributed Bayesian inference provides a full quantification of uncertainty offering numerous advantages over point estimates that autonomous sensor networks are able to exploit. However, fully-decentralized Bayesian inference often requires large communication overheads and low network latency, resources that are not typically available in practical applications. In this paper, we propose a decentralized Bayesian inference approach based on stochastic gradient Langevin dynamics, which produces full posterior distributions at each of the nodes with significantly lower communication overhead. We provide analytical results on convergence of the proposed distributed algorithm to the centralized posterior, under typical network constraints. Finally, we also provide extensive simulation results to demonstrate the validity of the proposed approach.

42 ENGINEERING↗
NASA NTRS1988

Diffuse hard X-ray and gamma-ray emission from M87

Radio measurements are used to infer the relativistic electron distributions in the central and halo regions of M87. From these distributions and the inferred relativistic proton components, the hard X-ray and gamma-ray spectrum of M87, is predicted, taking into account all relevant radiative processes. The feasibility of measuring the high-energy spectrum of M87 with the High Energy Gamma-Ray Telescope (Egret) on board the Gamma Ray Observatory (GRO) is discussed. Comparison of our calculation with present gamma-ray flux upper limits leads to a lower limit of 1.5 micro-G on the mean value of the halo magnetic field.

Dermer, Charles D.↗
NASA NTRS2002

The Velocity Distribution of Isolated Radio Pulsars

We infer the velocity distribution of radio pulsars based on large-scale 0.4 GHz pulsar surveys. We do so by modelling evolution of the locations, velocities, spins, and radio luminosities of pulsars; calculating pulsed flux according to a beaming model and random orientation angles of spin and beam; applying selection effects of pulsar surveys; and comparing model distributions of measurable pulsar properties with survey data using a likelihood function. The surveys analyzed have well-defined characteristics and cover approx. 95% of the sky. We maximize the likelihood in a 6-dimensional space of observables P, dot-P, DM, absolute value of b, mu, F (period, period derivative, dispersion measure, Galactic latitude, proper motion, and flux density). The models we test are described by 12 parameters that characterize a population's birth rate, luminosity, shutoff of radio emission, birth locations, and birth velocities. We infer that the radio beam luminosity (i) is comparable to the energy flux of relativistic particles in models for spin-driven magnetospheres, signifying that radio emission losses reach nearly 100% for the oldest pulsars; and (ii) scales approximately as E(exp 1/2) which, in magnetosphere models, is proportional to the voltage drop available for acceleration of particles. We find that a two-component velocity distribution with characteristic velocities of 90 km/ s and 500 km/ s is greatly preferred to any one-component distribution; this preference is largely immune to variations in other population parameters, such as the luminosity or distance scale, or the assumed spin-down law. We explore some consequences of the preferred birth velocity distribution: (1) roughly 50% of pulsars in the solar neighborhood will escape the Galaxy, while approx. 15% have velocities greater than 1000 km/ s (2) observational bias against high velocity pulsars is relatively unimportant for surveys that reach high Galactic absolute value of z distances, but is severe for spatially bounded surveys; (3) an important low-velocity population exists that increases the fraction of neutron stars retained by globular clusters and is consistent with the number of old objects that accrete from the interstellar medium; (4) under standard assumptions for supernova remnant expansion and pulsar spin-down, approx. 10% of pulsars younger than 20 kyr will appear to lie outside of their host remnants. Finally, we comment on the ramifications of our birth velocity distribution for binary survival and the population of inspiraling binary neutron stars relevant to some GRB models and potential sources for LIGO.

Arzoumanian, Z.↗
NASA NTRS1975

The inversion of aureole measurements to derive aerosol size distributions

An iterative method to invert size distributions from simulated scattered radiance measurements at small angles from the sun has been investigated. The inferred size distributions were represented by piecewise linear and cubic spline functions. Various relevant characteristics were investigated and it was found that: (1) the inverted size distribution was insensitive to the number of knots in the piecewise linear spline; (2) within the range of sensitivity, the choice of initial guess had little effect on the inverted size distribution; (3) five per cent random noise in the simulated radiances appreciably deteriorated the result but variations are still tolerable when compared with other methods for determining size distributions; (4) the inverted distribution was insensitive to the index of refraction used in the kernel for particle radii greater than 1 micron; (5) the choice of wavelength between 0.40 and 0.70 microns has a negligible effect on the inverted distribution; (6) a range of tropospheric aerosol size distributions gives acceptable inverted results; and (7) the cubic spline representation can give reasonable inverted distributions, but may become unstable.

Twitty, J. T.↗
NASA NTRS2014

The Uncertainty of Biomass Estimates from Modeled ICESat-2 Returns Across a Boreal Forest Gradient

The Forest Light (FLIGHT) radiative transfer model was used to examine the uncertainty of vegetation structure measurements from NASA's planned ICESat-2 photon counting light detection and ranging (LiDAR) instrument across a synthetic Larix forest gradient in the taiga-tundra ecotone. The simulations demonstrate how measurements from the planned spaceborne mission, which differ from those of previous LiDAR systems, may perform across a boreal forest to non-forest structure gradient in globally important ecological region of northern Siberia. We used a modified version of FLIGHT to simulate the acquisition parameters of ICESat-2. Modeled returns were analyzed from collections of sequential footprints along LiDAR tracks (link-scales) of lengths ranging from 20 m-90 m. These link-scales traversed synthetic forest stands that were initialized with parameters drawn from field surveys in Siberian Larix forests. LiDAR returns from vegetation were compiled for 100 simulated LiDAR collections for each 10 Mg · ha(exp -1) interval in the 0-100 Mg · ha(exp -1) above-ground biomass density (AGB) forest gradient. Canopy height metrics were computed and AGB was inferred from empirical models. The root mean square error (RMSE) and RMSE uncertainty associated with the distribution of inferred AGB within each AGB interval across the gradient was examined. Simulation results of the bright daylight and low vegetation reflectivity conditions for collecting photon counting LiDAR with no topographic relief show that 1-2 photons are returned for 79%-88% of LiDAR shots. Signal photons account for approximately 67% of all LiDAR returns, while approximately 50% of shots result in 1 signal photon returned. The proportion of these signal photon returns do not differ significantly (p greater than 0.05) for AGB intervals greater than 20 Mg · ha(exp -1). The 50m link-scale approximates the finest horizontal resolution (length) at which photon counting LiDAR collection provides strong model fits and minimizes forest structure uncertainty in the synthetic Larix stands. At this link-scale AGB greater than 20 Mg · ha(exp -1) has AGB error from 20-50% at the 95% confidence level. These results suggest that the theoretical sensitivity of ICESat-2 photon counting LiDAR measurements alone lack the ability to consistently discern differences in inferred AGB at 10 Mg · ha(exp -1) intervals in sparse forests characteristic of the taiga-tundra ecotone.

LiDAR↗
DOE OSTI2023

Inferring the Energy and Distance Distributions of Fast Radio Bursts Using the First CHIME/FRB Catalog

Abstract Fast radio bursts (FRBs) are brief, energetic, typically extragalactic flashes of radio emission whose progenitors are largely unknown. Although studying the FRB population is essential for understanding how these astrophysical phenomena occur, such studies have been difficult to conduct without large numbers of FRBs and characterizable observational biases. Using the recently released catalog of 536 FRBs published by the Canadian Hydrogen Intensity Mapping Experiment/Fast Radio Burst (CHIME/FRB) collaboration, we present a study of the FRB population that also calibrates for selection effects. Assuming a Schechter function, we infer a characteristic energy cut-off of E char = 2.38 − 1.64 + 5.35 × 10 41 erg and a differential power-law index of γ = − 1.3 − 0.4 + 0.7 . Simultaneously, we infer a volumetric rate of [ 7.3 − 3.8 + 8.8 (stat.) − 1.8 + 2.0 ( sys . ) ] × 10 4 Gpc −3 yr −1 above a pivot energy of 10 39 erg and below a scattering timescale of 10 ms at 600 MHz, and find we cannot significantly constrain the cosmic evolution of the FRB population with star-formation rate. Modeling the host’s dispersion measure (DM) contribution as a log-normal distribution and assuming a total Galactic contribution of 80 pc cm −3 , we find a median value of DM host = 84 − 49 + 69 pc cm −3 , comparable with values typically used in the literature. Proposed models for FRB progenitors should be consistent with the energetics and abundances of the full FRB population predicted by our results. Finally, we infer the redshift distribution of FRBs detected with CHIME, which will be tested with the localizations and redshifts enabled by the upcoming CHIME/FRB Outriggers project.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Chemistry imaging and distribution analysis of rare earth elements in coal using LIBS and LA-ICP-MS instruments

Currently, demand for rare earth elements (REEs) increased significantly. Coal is actively evaluated as potential economic sources for extraction of REEs. Here, in this work, laser-induced breakdown spectroscopy (LIBS) was evaluated for rapid estimation of REEs content and their distribution in the natural coal samples. The results were compared with similar laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS) measurements. Thirteen coal samples (nine standard samples and five natural samples) were used in this study. Powder samples were pressed into pellets while coal chunks were directly ablated for data recording. Pellets of the powder standard samples were used to optimize the data acquisition system and then data recorded with this optimized system was used to identify the proper data acquisition and analysis models. After establishing the proper data acquisition system and analysis model using the standard samples, natural coal samples in powder form and their chunks were utilized to record LIBS and LA-ICP-MS spectra. Multivariate calibration models were developed using four of the natural samples, which were evaluated by predicting the REE content in the fifth sample. Principal component analysis was performed on the LIBS data obtained from the natural samples and it classified all the samples with high accuracy. Two-dimensional (2D) elemental mapping on coal chunk samples was also performed using both LIBS and LA-ICP-MS to study the distribution of REEs in the samples. The resulting elemental images and their correlations can be used to infer mineral distributions.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS1989

Inference of stratospheric aerosol composition and size distribution from SAGE II satellite measurements

A method for inferring stratospheric aerosol composition and size distribution from the water vapor concentration and aerosol extinction measurements obtained in the Stratospheric Aerosol and Gas Experiment (SAGE) II and the associated temperature from the NMC. The aerosols are assumed to be sulfuric acid-water droplets. A modified Levenberg-Marquardt algorithm is used to determine model size distribution parameters based on the SAGE II multiwavelength aerosol extinctions. It is found that the best aerosol size information is contained in the aerosol radius range between about 0.25 and 0.80 micron.

Wang, Pi-Huan↗
NASA NTRS1992

On the consequences of bi-Maxwellian plasma distributions for parallel electric fields

The objective is to use the measurements of the equatorial particle distributions to obtain the parallel electric field structure and the evolution of the plasma distribution function along the field line. Appropriate uses of kinetic theory allows us to use the measured ( and inferred) particle distributions to obtain the electric field, and hence the variation on plasma density along the magnetic field line. The approach, here, is to utilize the adiabatic invariants, and assume the plasma distributions are in equilibrium.

Olsen, Richard C.↗
DOE OSTI2025

Accelerating Hamiltonian Monte Carlo for Bayesian inference in neural networks and neural operators

Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high dimensionality of the network’s parameter space and the non-convexity of their posterior distributions. Therefore, various approximation techniques, such as variational inference (VI) or stochastic gradient MCMC, are often employed to infer the posterior distribution of the network parameters. Such approximations introduce inaccuracies in the inferred distributions, resulting in unreliable uncertainty estimates. In this work, we propose a hybrid approach that combines inexpensive VI and accurate HMC methods to efficiently and accurately quantify uncertainties in neural networks and neural operators. The proposed approach leverages an initial VI training on the full network. We examine the influence of individual parameters on the prediction uncertainty, which shows that a large proportion of the parameters do not contribute substantially to uncertainty in the network predictions. This information is then used to significantly reduce the dimension of the parameter space, and HMC is performed only for the subset of network parameters that strongly influence prediction uncertainties. This yields a framework for accelerating the full batch HMC for posterior inference in neural networks. We demonstrate the efficiency and accuracy of the proposed framework on deep neural networks and operator networks, showing that inference can be performed for large networks with tens to hundreds of thousands of parameters. Finally, we show that this method can effectively learn surrogates for complex physical systems by modeling the operator that maps from upstream conditions to wall-pressure data on a cone in hypersonic flow.

Bayesian inference↗
DOE OSTI2021

Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distributions

Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSFs) or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from golden eagles Aquila chrysaetos from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During the summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Quantifying electron temperature distributions from time-integrated x-ray emission spectra

K-shell x-ray emission spectroscopy is a standard tool used to diagnose the plasma conditions created in high-energy-density physics experiments. In the simplest approach, the emissivity-weighted average temperature of the plasma can be extracted by fitting an emission spectrum to a single temperature condition. It is known, however, that a range of plasma conditions can contribute to the measured spectra due to a combination of the evolution of the sample and spatial gradients. In this work, we define a parameterized model of the temperature distribution and use Markov Chain Monte Carlo sampling of the input parameters, yielding uncertainties in the fit parameters to assess the uniqueness of the inferred temperature distribution. Here we present the analysis of time-integrated S and Fe x-ray spectroscopic data from the Orion laser facility and demonstrate that while fitting each spectral region to a single temperature yields two different temperatures, both spectra can be fit simultaneously with a single temperature distribution. We find that fitting both spectral regions together requires a maximum temperature of $1310^{+90}_{-70}$ eV with significant contributions from temperatures down to 200 eV.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

High Resolution Ion Energy Distribution Measurements for Energization Studies on Madison Symmetric Torus RFP Plasmas

We propose to continue a comprehensive and aggressive collaborative study of the critical issues of RFP and fusion physics at MST by enhancing the inferred ion distribution function and the high (spatial and temporal) resolution of magnetic fluctuation measurements. For this, we propose to make novel use of a retarding potential before a neutral particle analyzer as well as a novel millimeter magnetic probe. The measurement of relationships between ion and electron temperatures and magnetic field fluctuations can provide otherwise unobtainable insights on the role of turbulence in fusion plasmas with special focus on the impact of magnetic field turbulence on plasma confinement and scaling. These insights can be particularly valuable when they are coupled with an increased understanding of the issues which drive the turbulence onset, enhance steady-state sustainment, and enable turbulent dissipation. With these new insights, MST will be particularly well positioned to provide important insights in a manner portable to a broad class of fusion devices. We propose to exploit this opportunity and, at the same time, take full advantage of the associated educational enhancements for plasma physics students as FAMU continues to expand the diversity of the fusion scientific workforce.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2011

A Search for Optically Faint GEO Debris

Existing optical surveys for debris at geosynchronous orbit (GEO) have been conducted with meter class telescopes, which have detection limits in the range of 18th-19th magnitude. We report on a new search for optically faint debris at GEO using the 6.5-m Magellan 1 telescope Walter Baade at Las Campanas Observatory in Chile. Our goal is to go as faint as possible and characterize the brightness distribution of debris fainter than R = 20th magnitude, corresponding to a size smaller than 10 cm assuming an albedo of 0.175. We wish to compare the inferred size distribution for GEO debris with that for LEO debris. We describe results obtained during 9.4 hours of observing time during 25-27 March 2011. We used the IMACS f/2 instrument, which has a mosaic of 8 CCDs, and a field of view of 30 arc-minutes in diameter. This is the widest field of view of any instrument on either Magellan telescope. All observations were obtained through a Sloan r filter. The limiting magnitude for 5 second exposures is estimated to be fainter than 22. With this small field of view and the limited observing time, our objective was to search for optically faint objects from the Titan 3C Transtage (1968-081) fragmentation in 1992. Eight debris pieces and the parent rocket body are in the Space Surveillance Network public catalog. We successfully tracked two cataloged pieces of Titan debris (SSN # 25001 and 33519) with the 6.5-m telescope, followed by a survey for objects on similar orbits but with a spread in mean anomaly. To detect bright objects over a wider field of view (1.6x1.6 degrees), we observed the same field centers at the same time through a similar filter with the 0.6-m MODEST (Michigan Orbital DEbris Survey Telescope), located 100 km to the south of Magellan at Cerro Tololo Inter-American Observatory, Chile. We will describe our experiences using Magellan, a telescope never used previously for orbital debris research, and our initial results.

Seitzer, Patrick↗