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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Evaluating the impact of anatomical and physiological variability on human equivalent doses using PBPK models

Abstract Addressing human anatomical and physiological variability is a crucial component of human health risk assessment of chemicals. Experts have recommended probabilistic chemical risk assessment paradigms in which distributional adjustment factors are used to account for various sources of uncertainty and variability, including variability in the pharmacokinetic behavior of a given substance in different humans. In practice, convenient assumptions about the distribution forms of adjustment factors and human equivalent doses (HEDs) are often used. Parameters such as tissue volumes and blood flows are likewise often assumed to be lognormally or normally distributed without evaluating empirical data for consistency with these forms. In this work, we performed dosimetric extrapolations using physiologically based pharmacokinetic (PBPK) models for dichloromethane (DCM) and chloroform that incorporate uncertainty and variability to determine if the HEDs associated with such extrapolations are approximately lognormal and how they depend on the underlying distribution shapes chosen to represent model parameters. We accounted for uncertainty and variability in PBPK model parameters by randomly drawing their values from a variety of distribution types. We then performed reverse dosimetry to calculate HEDs based on animal points of departure for each set of sampled parameters. Corresponding samples of HEDs were tested to determine the impact of input parameter distributions on their central tendencies, extreme percentiles, and degree of conformance to lognormality. This work demonstrates that the measurable attributes of human variability should be considered more carefully and that generalized assumptions about parameter distribution shapes may lead to inaccurate estimates of extreme percentiles of HEDs.

Toxicology↗
DOE OSTI2015

Molecular beam epitaxial growth and scanning tunneling microscopy studies of the gallium rich trench line structure on N-polar w-GaN( 000 1 ¯ )

In addition to the usual set of the well-known reconstructions that have been observed on the N-polar GaN surface, namely 1×1, 3×3, 6×6, and c(6×12), an additional structure is occasionally seen at high Ga coverage, which can extend over a large area of the surface. This structure, which is referred to as trench line structure, is partially ordered and consists of parallel-running dark (trench) lines separating wide and narrow strips of atomically ordered regions. There are also randomly placed defects interrupting the ideal ordering. Reflection high energy electron diffraction and scanning tunneling microscopy in ultrahigh vacuum are applied to investigate this trench line structure on samples prepared using molecular beam epitaxy. It is found that the trench line structure results from annealing the Ga-rich c(6×12) at high temperature followed by quenching to room temperature. By careful comparison of the scanning tunneling microscopy images with those from neighboring c(6×12) regions, it is found that the trench line structure can be decomposed into subunits of the c(6×12). Using these subunits, some simple models for the trench line structure are created. It is proposed that the trench line structure is composed of two primary c(6×12) subunits consisting of first and second layer Ga adatoms and that the trench lines are regions devoid of Ga adatoms, going down to the Ga adlayer.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Data-Consistent Solutions to Stochastic Inverse Problems Using a Probabilistic Multi-Fidelity Method Based on Conditional Densities

In this work, we build upon a recently developed approach for solving stochastic inverse problems based on a combination of measure-theoretic principles and Bayes' rule. We propose a multi-fidelity method to reduce the computational burden of performing uncertainty quantification using high-fidelity models. This approach is based on a Monte Carlo framework for uncertainty quantification that combines information from solvers of various fidelities to obtain statistics on the quantities of interest of the problem. In particular, our goal is to generate samples from a high-fidelity push-forward density at a fraction of the costs of standard Monte Carlo methods, while maintaining flexibility in the number of random model input parameters. Key to this methodology is the construction of a regression model to represent the stochastic mapping between the low- and high-fidelity models, such that most of the computations can be leveraged to the low-fidelity model. To that end, we employ Gaussian process regression and present extensions to multi-level-type hierarchies as well as to the case of multiple quantities of interest. Finally, we demonstrate the feasibility of the framework in several numerical examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Extreme-Scale Bayesian Inference for Uncertainty Quantification of Complex Simulations

Uncertainty quantification (UQ)—that is, quantifying uncertainties in complex mathematical models and their large-scale computational implementations—is widely viewed as one of the outstanding challenges facing the field of CS&E over the coming decade. The EUREKA project set to address the most difficult class of UQ problems: those for which both the underlying PDE model as well as the uncertain parameters are of extreme scale. In the project we worked on these extreme-scale challenges in the following four areas: 1. Scalable parallel algorithms for sampling and characterizing the posterior distribution that exploit the structure of the underlying PDEs and parameter-to-observable map. These include structure-exploiting versions of the randomized maximum likelihood method, which aims to overcome the intractability of employing conventional MCMC methods for solving extreme-scale Bayesian inversion problems by appealing to and adapting ideas from large-scale PDE-constrained optimization, which have been very successful at exploring high-dimensional spaces. 2. Scalable parallel algorithms for construction of prior and likelihood functions based on learning methods and non-parametric density estimation. Constructing problem-specific priors remains a critical challenge in Bayesian inference, and more so in high dimensions. Another challenge is construction of likelihood functions that capture unmodeled couplings between observations and parameters. We will create parallel algorithms for non-parametric density estimation using high dimensional N-body methods and combine them with supervised learning techniques for the construction of priors and likelihood functions. 3. Bayesian inadequacy models, which augment physics models with stochastic models that represent their imperfections. The success of the Bayesian inference framework depends on the ability to represent the uncertainty due to imperfections of the mathematical model of the phenomena of interest. This is a central challenge in UQ, especially for large-scale models. We propose to develop the mathematical tools to address these challenges in the context of extreme-scale problems. 4. Parallel scalable algorithms for Bayesian optimal experimental design (OED). Bayesian inversion yields quantified uncertainties in the model parameters, which can be propagated forward through the model to yield uncertainty in outputs of interest. This opens the way for designing new experiments to reduce the uncertainties in the model parameters and model predictions. Such experimental design problems have been intractable for large-scale problems using conventional methods; we will create OED algorithms that exploit the structure of the PDE model and the parameter-to-output map to overcome these challenges. Parallel algorithms for these four problems were created, analyzed, prototyped, implemented, tuned, and scaled up for leading-edge supercomputers, including UT-Austin’s own 10 petaflops Stampede system, ANL’s Mira system, and ORNL’s Titan system. While our focus is on fundamental mathematical/computational methods and algorithms, we will assess our methods on model problems derived from several DOE mission applications, including multiscale mechanics and ice sheet dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Litter Production of Oak-Hickory Forest at Missouri Ozark (MOFLUX) Site: 2003-2022

This dataset contains measurements of litter production at the second-growth upland oak-hickory forest at the Missouri Ozark AmeriFlux (MOFLUX) site. The MOFLUX site is located in the University of Missouri Baskett Forest situated in the Ozark Border Region of central Missouri, USA, and is part of the AmeriFlux network (site ID: US-MOz). During 2003, 24 circular vegetation plots (each 0.08 hectares) were established in the MOFLUX forest. The plots were situated 50 meters apart on 5 linear transects radiating out from the flux tower base in southeast, south, southwest, west, and northwest directions—there were 5 plots per transect except for the northwest one, which had only 4 due to the presence of a small pond at the terminus (Fig. 1). Litter measurements were initiated in October 2003 and continue to the present. Data through 2022 are reported in this dataset. On each plot, two subplots were established for the collection of litter. Subplots were setup using a stratified random design to ensure the collectors were not immediately adjacent to one another. Litter collectors were usually emptied at monthly intervals, or less during the autumn leaf drop. Samples were dried to constant mass and then sorted before determining the dry mass of leaf, vegetative and woody litter. These litter data have been valuable in modeling efforts to examine the impacts of intensifying moisture extremes (i.e., moisture excess and deficit conditions) on soil carbon cycling. More recent data will be added periodically with no changes to previously released data or the format and structure of the data file. This dataset contains one data file in comma separate (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

Demographic and psychological variables affecting test subject evaluations of ride quality

Ride-quality experiments similar in objectives, design, and procedure were conducted, one using the U.S. Air Force Total In-Flight Simulator and the other using the Langley Passenger Ride Quality Apparatus to provide the motion environments. Large samples (80 or more per experiment) of test subjects were recruited from the Tidewater Virginia area and asked to rate the comfort (on a 7-point scale) of random aircraft motion typical of that encountered during STOL flights. Test subject characteristics of age, sex, and previous flying history (number of previous airplane flights) were studied in a two by three by three factorial design. Correlations were computed between one dependent measure, the subject's mean comfort rating, and various demographic characteristics, attitudinal variables, and the scores on Spielberger's State-Trait Anxiety Inventory. An effect of sex was found in one of the studies. Males made higher (more uncomfortable) ratings of the ride than females. Age and number of previous flights were not significantly related to comfort ratings. No significant interactions between the variables of age, sex, or previous number of flights were observed.

Duncan, N. C.↗
NASA NTRS1986

The Shane-Wirtanen counts - Plate correction factors and correlation functions

It has recently been argued that the break in the galaxy correlation function observed in the Lick counts of Shane and Wirtanen may be an artifact of plate variations in limiting magnitude. This has stimulated us to generalize our analysis of the effect of errors in the plate depths to include a possible systematic term, and to develop tests for the random and systematic errors. The tests show that neither can have appreciably affected the distinctive feature found in the galaxy two-point correlation function for the Lick sample.

Groth, Edward J.↗
NASA NTRS1990

Radar observations of orbital debris at NASA Johnson Space Center

This paper describes the original Debris Environment Characterization Radar concept and the design verification experiments developed and carried out with the purpose of establishing methods for assessing the hazards to the Space Station Freedom from radar measurements. Three design verification experiments, utilizing the Arecibo, the Goldstone, and the ALCOR radars were performed. Data obtained from RCS measurements of a representative sample of debris from ground-test hypervelocity impacts and from explosions of simulated satellites will be used to establish a relationship between the physical size of a randomly shaped debris object and its RCS.

Stansbery, E. G.↗
NASA NTRS1993

The velocity dispersion of the caustic network due to random motion of individual stars in the lensing galaxy

We present a method of computing the velocity distribution of the caustic network due to the random motion of stars in the lensing galaxy. This method is illustrated on the example of the two-point mass lens and then applied to a large sample of stars. We conclude that the proper motion of the stars increases significantly the frequency of the high magnification events in comparison with a static lens configuration with constant stream or constant bulk velocity. The stream velocity is the velocity of the star field relative to the global bulk velocity of the galaxy. We show that the global bulk and the stream velocity of the star field have to be considered separately for any microlensing situation. The higher the surface mass density of the stars in the lensing galaxy, the higher the influence of proper motion of stars on the statistics of high magnification events. The influence of a Gaussian velocity distribution of the stars in the lensing galaxy compared with a constant stream velocity of the stars increases the number of high magnification events by a factor 1.30 +/- 0.06 for a normalized surface density of the stars cr = 0.1 and by a factor 1.7 +/- 0.1 for sigma = 0.5. This means that for some microlensing situations the proper motion of the stars in a lensing galaxy has to be considered for exact microlensing predictions.

Kundik, Tomislav↗
DOE OSTI2025

Machine Learning for Well Log Analysis in Uranium Mining

This project explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate well log analysis for uranium mining. Geophysical log data—spontaneous potential, resistivity, and gamma ray—were used to classify lithology, correlate well logs and identify roll front zonation patterns, which are critical for locating uranium ore bodies. Supervised ML algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Random Forest were trained to classify lithology with high accuracy. Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks were also used for role front zone identification. Moreover, a Fast Dynamic Time Warping (FastDTW) algorithm was employed for well log correlation. Additionally, sample lag was addressed using dynamic programming. Results demonstrate the potential of AI and ML to streamline well log analysis and enhance uranium exploration workflows.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Monte Carlo chord length sampling for d-dimensional Markov binary mixtures

The Chord Length Sampling (CLS) algorithm is a powerful Monte Carlo method that models the effects of stochastic media on particle transport by generating on-the-fly the material interfaces seen by the random walkers during their trajectories. This annealed disorder approach, which formally consists of solving the approximate Levermore–Pomraning equations for linear particle transport, enables a considerable speed-up with respect to transport in quenched disorder, where ensemble-averaging of the Boltzmann equation with respect to all possible realizations is needed. However, CLS intrinsically neglects the correlations induced by the spatial disorder, so that the accuracy of the solutions obtained by using this algorithm must be carefully verified with respect to reference solutions based on quenched disorder realizations. When the disorder is described by Markov mixing statistics, such comparisons have been attempted so far only for one-dimensional geometries, of the rod or slab type. In this work we extend these results to Markov media in two-dimensional (extruded) and three-dimensional geometries, by revisiting the classical set of benchmark configurations originally proposed by Adams, Larsen and Pomraning and extended by Brantley. In particular, we examine the discrepancies between CLS and reference solutions for scalar particle flux and transmission/reflection coefficients as a function of the material properties of the benchmark specifications and of the system dimensionality.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Hot Forging Options for Thick Castings

Uranium alloyed with 10 wt% molybdenum (U-10Mo) is a monolithic nuclear fuel relevant to the National Nuclear Security Administration’s nonproliferation efforts. Research has been underway to optimize processing techniques for the U-10Mo fuel. This study investigated the use of hot compression or “hot forging” on a thick (~1") cast and homogenized U-10Mo plate before standard hot and cold rolling procedures. After plates were cast and homogenized, six samples were cut and forged at 700°C at a strain rate of either 0.10 s –1 or 0.01 s –1 at six levels of reduction. After forging, all samples underwent hot and cold rolling followed by annealing to achieve a final foil thickness of about 0.0085". Samples were taken at each stage of the casting and thermomechanical processing to assess the microstructural evolution. Chemical composition, microstructure, and uranium carbide morphology are presented and assessed in this study. Upon hot forging, dislocations accumulate along the grain boundaries, which serve as nucleation sites for randomly oriented, strain-free grains during subsequent annealing steps. Hot forging and subsequent annealing produced very heterogeneous grain sizes. However, no molybdenum segregation was observed after forging. No obvious trend was observed between forging conditions (strain rate and reduction percentage) and the microstructure after final thermomechanical processing. Upon hot rolling to 0.04" and annealing (700°C for 45 min), the average grain diameters from OM was 17 ± 2 µm across the six different forged samples. The subsequent cold rolling to 0.0085" and then annealing (700°C for 45 min) resulted in an average of 13 ± 2 µm between the six samples. Thus, the starting, as-forged microstructure did not appear to significantly influence the final microstructure of the cold-rolled foil. These results will help with understanding and expanding hot working capabilities for thicker U-10Mo castings. They also provide useful information on the effects of hot forging and its potential use to minimize defects that can arise during subsequent hot and cold rolling procedures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Direct observations of field-induced assemblies in magnetite ferrofluids

Evolution of microstructures in magnetite-based ferrofluids with weak dipolar moments (particle size ≤ 10 nm) is studied with an emphasis on examining the effects of particle concentration (ϕ) and magnetic field strength (H) on the structures. Nanoparticles are dispersed in water at three different concentrations, ϕ = 0.15%, 0.48%, and 0.59% (w/v) [g/ml%] and exposed to uniform magnetic fields in the range of H = 0.05–0.42 T. Cryogenic transmission electron microscopy is employed to provide in-situ observations of the field-induced assemblies in such systems. As the magnetic field increases, the Brownian colloids are observed to form randomly distributed chains aligned in the field direction, followed by head-to-tail chain aggregation and then lateral aggregation of chains termed as zippering. By increasing the field in low concentration samples, the number of chains increases, though their length does not change dramatically. Increasing concentration increases the length of the linear particle assemblies in the presence of a fixed external magnetic field. Thickening of the chains due to zippering is observed at relatively high fields. Through a systematic variation of concentration and magnetic field strength, this study shows that both magnetic field strength and change in concentration can strongly influence formation of microstructures even in weak dipolar systems. Additionally, the results of two commonly used support films on electron microscopy grids, continuous carbon and holey carbon films, are compared. Holey carbon film allows us to create local regions of high concentrations that further assist the development of field-induced assemblies. The experimental observations provide a validation of the zippering effect and can be utilized in the development of models for thermophysical properties such as thermal conductivity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Synthesis and characterization of visible-active molybdenum disulfide (2H-MoS{sub 2}) nanospheres

In current study, a novel 2H-MoS{sub 2} nanospheres were successfully synthesized and underwent structural- as well as optical-property characterizations. The MoS{sub 2} were prepared by one pot hydrothermal approach through adopting L-cysteine as environmentally-benignchalcogenide precursor. TEM image shows that the as-synthesized MoS{sub 2} appear to be spherical in shape with size distribution in the range of 120 nm – 180 nm. HRTEM lattice-fringes imaging further elucidate that the interlayer spacing at the edges is equal to be 0.62 nm that correspond to (002) plane stacking. Also, the HRTEM image clearly-illustrate that the internal microstructure of MoS{sub 2} composed of randomly-arrayed alternating layers, which render the postulation that the formation of nanosphere is driven by self-assembly of individual layers into globular morphology. XRD diffractogram that appear to be broad and unresolved reveal the partially crystalline nature of the sample. Optical-absorption spectra depicts the sample is visible active with featureless absorption, which can attribute to indirect transition of the excitions generated. By using Tauc plot, the bandgap energy is determined to be 1.75 eV, which reflect the nanospheres are indeed visible-active nanostructures.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

The influence of packing structure and interparticle forces on ultrasound transmission in granular media

Ultrasound propagation through externally stressed, disordered granular materials was experimentally and numerically investigated. Experiments employed piezoelectric transducers to excite and detect longitudinal ultrasound waves of various frequencies traveling through randomly packed sapphire spheres subjected to uniaxial compression. The experiments featured in situ X-ray tomography and diffraction measurements of contact fabric, particle kinematics, average per-particle stress tensors, and interparticle forces. The experimentally measured packing configuration and inferred interparticle forces at different sample stresses were used to construct spring networks characterized by Hessian and damping matrices. The ultrasound responses of these network were simulated to investigate the origins of wave velocity, acoustic paths, dispersion, and attenuation. Results revealed that both packing structure and interparticle force heterogeneity played an important role in controlling wave velocity and dispersion, while packing structure alone quantitatively explained most of the observed wave attenuation. This research provides insight into time- and frequency-domain features of wave propagation in randomly packed granular materials, shedding light on the fundamental mechanisms controlling wave velocities, dispersion, and attenuation in such systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Mapping wall-to-wall fractional cover of Arctic tundra plant functional types in Alaska using 20-m spatial resolution satellite imagery and harmonized plot observations

Estimates of fractional cover (fCover) across given land surfaces are used to assess, and often model, vegetation composition and diversity, which are crucial for understanding the health and functioning of terrestrial ecosystems. Remote sensing provides a useful means for scaling local, plot-measured fCover estimates to regional scales. Leveraging a recently synthesized and harmonized plot database, this study generated wall-to-wall maps of fCover for six Alaskan-Arctic plant functional types (PFT), including non-vascular plants, forbs, graminoids, and deciduous and evergreen shrubs, using 20-m satellite data (Sentinel-1, Sentinel-2, ArcticDEM) using a machine learning regression approach, specifically the random forest (RF) algorithm, which is well-suited for handling nonlinear relationships and high-dimensional satellite datasets. This study additionally addressed the spatio-temporal inconsistencies e.g., sampling scale, plot size, and collection year in plot measured fCover by adopting a multivariate outlier detection approach—Cook’s distance—to identify high-quality plots for model training and validation. Our approach achieves high accuracy (R 2 = 0.59–0.93, root mean squared errors = 0.02–0.10 for all PFTs) between plot-observed and satellite-derived fCover when using high-quality plot samples. The mapped fCover characterizes the spatial patterns of different PFTs across the tundra biome at a 20-m resolution, providing key information needed for improved representation of Arctic tundra vegetation in terrestrial biosphere models to better understand climate-vegetation feedback across the Arctic tundra.

Arctic tundra↗
DOE OSTI2021

A review of imputation strategies for isobaric labeling-based shotgun proteomics

The throughput efficiency and increased depth of coverage provided by isobaric-labeled proteomics measurements have led to increased usage of these techniques. However, the structure of missing data is uniquely different than unlabeled studies. In this review, we compare the efficacy of nine imputation methods on a CPTAC proteomics iTRAQ dataset. Imputation methods were evaluated with regard to accuracy, variability, statistical hypothesis test inference and run time over datasets consisting of varying number of iTRAQ plexes and percentages of missing data. In general, expectation maximization and random forest imputation methods yielded the best performances, and constant-based methods performed poorly consistently across all dataset sizes and percentages of missing values. For datasets with small sample sizes and higher percentages of missing data, results indicate that statistical inference with no imputation may be preferable. Based on the findings in this review, there are core imputation methods that perform higher for isobaric-labeled proteomics data, but great care and consideration as to whether imputation should be used should be given for datasets comprised of a small number of samples, as well as to factors such as computational time and reproducibility of imputation values.

Bramer, Lisa M.↗