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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

The Correlation between Halo Mass and Stellar Mass for the Most Massive Galaxies in the Universe

We present measurements of the clustering of galaxies as a function of their stellar mass in the Baryon Oscillation Spectroscopic Survey. We compare the clustering of samples using 12 different methods for estimating stellar mass, isolating the method that has the smallest scatter at fixed halo mass. In this test, the stellar mass estimate with the smallest errors yields the highest amplitude of clustering at fixed number density. We find that the PCA stellar masses of Chen et al. clearly have the tightest correlation with halo mass. The PCA masses use the full galaxy spectrum, differentiating them from other estimates that only use optical photometric information. Using the PCA masses, we measure the large-scale bias as a function of M{sub ∗} for galaxies with logM{sub ∗}⩾11.4, correcting for incompleteness at the low-mass end of our measurements. Using the abundance matching ansatz to connect dark matter halo mass to stellar mass, we construct theoretical models of b(M{sub ∗}) that match the same stellar mass function but have different amounts of scatter in stellar mass at fixed halo mass, σ{sub logM∗}. Using this approach, we find σ{sub logM∗}=0.18{sub −0.02}{sup +0.01}. This value includes both intrinsic scatter as well as random errors in the stellar masses. To partially remove the latter, we use repeated spectra to estimate statistical errors on the stellar masses, yielding an upper limit to the intrinsic scatter of 0.16 dex.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Method of solar power prediction

Techniques for estimating direct and diffuse sunlight reaching a location as a function of time using color whole sky images are provided. In one aspect, a method for forecasting solar insolation is provided that includes the steps of: obtaining color whole sky images using a digital camera which are taken at different times, and wherein an image of a solar disk is present in at least one of the color whole sky images; creating sky transparency images from the color whole sky images; determining atmospheric solar scattering based on a width of the solar disk in the color whole sky images; determining cloud motion trajectories from a time series of the sky transparency images; and calculating the solar insolation as a function of time using the sky transparency, the atmospheric solar scattering, and the cloud motion trajectories. A system for forecasting solar insolation is also provided.

Hamann, Hendrik F.↗
DOE OSTI2016

Material Properties and Temperature Effects Quantified for Slurry Wear - 16141

Literature indicates that the hardness of solid particles relative to the target material hardness and temperature influence erosion. Investigations of temperature effects on erosion also show that erosion resistance is a function of target material's homologous temperature to melting point temperature. While relative hardness effects on erosion and temperature effects on erosion have been investigated independently, their combined effect has not been evaluated. This paper summarizes a review of relevant literature on relative hardness and temperature effects on erosion and via probabilistic calculations, quantifies the combined effect of relative hardness and temperature on liquid-particle induced wear, or slurry wear. These insights can assist material selection and plant operations at waste processing facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Effect of reaction atmosphere on structural and optical properties of hexagonal molybdenum oxide (h-MoO{sub 3})

The present work aims to synthesize single phase h-MoO{sub 3} nanocrytals by chemical precipitation method exposed under different reaction atmospheres. The reaction atmosphere have been successfully tuned as air, nitrogen and argon and studied its effects on structural, functional, morphology and optical properties by using X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared (FT-IR) spectroscopy and diffuse reflectance spectroscopy (DRS) measurements. The XRD result indicates that the sample exhibits characteristic hexagonal phase of MoO{sub 3}. The crystallite size is estimated by well known Scherrer’s method. The crystallite size is relative small in the case of sample prepared at argon atmosphere. The functional groups such as Mo-O, N-H and O-H are identified from FT-IR spectroscopy. The particle exhibits rod like morphology with perfect hexagonal cross-section. The optical absorption observed at 420-450 nm corresponds to fundamental optical absorption by h-MoO{sub 3}. The band gap values are estimated using Kublka-Munk (K-M) function and found to be 2. 87 eV, 2.93 eV and 2.97 eV for samples synthesized under air, nitrogen and argon, respectively.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions

Abstract Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping). We introduce the traitstrap R package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow, trait_fill , allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample. Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community‐weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community‐weighting. Random sampling across all species outperformed both size‐ and abundance‐biased sampling. Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems.

Maitner, Brian S.↗
DOE OSTI2019

Seismoacoustic Analysis of Chemical Explosions at the Nevada National Security Site

In recent years, two sets of chemical explosions have been conducted at the Nevada National Security Site (NNSS): 6 explosions from Phase I of the Source Physics Experiment (SPE) and 4 explosions from the Forensics Surface Events (FSE). We have estimated the explosive yield and depth-of-burial/height-of-burst of both from the synthesis of seismic and low-frequency acoustic data. Analysis of the seismic signal is performed using a waveform envelope technique. Using seismic data only, however, there is a tradeoff between the yield and depth or height of the explosion, which becomes especially acute for near-surface events. This tradeoff between yield and depth/height can be broken by including complementary analysis of the acoustic signal using the acoustic waveform inversion method. The acoustic signal also has a strong tradeoff between a yield and depth/height, but the slope is opposite that of the seismic for near-surface events. We use a likelihood function to combine the seismic and acoustic analysis and improve our estimates of yield and depth/height for these sets of explosions. The results are generally consistent with the known parameters, but can be improved upon by higher-frequency seismic analysis and improved acoustic impulse scaling for large scaled depths-of-burial. These events serve as prime examples of data fusion from multiple data streams. The challenge will be to apply the method to events at longer regional distances where the seismic signals will include phases traveling in the upper mantle, and the acoustic signals will be recorded as infrasound signals, which are subject to atmospheric variability.

58 GEOSCIENCES↗
DOE OSTI2016

CHIRP-Like Signals: Estimation, Detection and Processing A Sequential Model-Based Approach

Chirp signals have evolved primarily from radar/sonar signal processing applications specifically attempting to estimate the location of a target in surveillance/tracking volume. The chirp, which is essentially a sinusoidal signal whose phase changes instantaneously at each time sample, has an interesting property in that its correlation approximates an impulse function. It is well-known that a matched-filter detector in radar/sonar estimates the target range by cross-correlating a replicant of the transmitted chirp with the measurement data reflected from the target back to the radar/sonar receiver yielding a maximum peak corresponding to the echo time and therefore enabling the desired range estimate. In this application, we perform the same operation as a radar or sonar system, that is, we transmit a “chirp-like pulse” into the target medium and attempt to first detect its presence and second estimate its location or range. Our problem is complicated by the presence of disturbance signals from surrounding broadcast stations as well as extraneous sources of interference in our frequency bands and of course the ever present random noise from instrumentation. First, we discuss the chirp signal itself and illustrate its inherent properties and then develop a model-based processing scheme enabling both the detection and estimation of the signal from noisy measurement data.

42 ENGINEERING↗
DOE OSTI2017

Surrogate Models for TREAT Transient Calculations

The Transient Reactor Test Facility (TREAT) at Idaho National Laboratory (INL) is currently preparing for resumption of operation after more than 20 years in a long-term standby state. In parallel with preparation for operations and a new transient testing program, the DOENE Advanced Modeling and Simulation program (NEAMS) has been supporting the development of three-dimensional transient multi-physics modeling capabilities within the reactor physics application MAMMOTH. Recently, the MAMMOTH reactor physics team has begun development of uncertainty and sensitivity analysis for the TREAT core. This research will be used to support the analysis of reactor operation and experiment planning. Traditional approaches, i.e., Monte Carlo simulations, for such tasks require a large number of model evaluations under both steady state and transient conditions. However, exhaustive sets of sampling runs are simply not practical for TREAT since evaluations of each model can take a substantial amount of time. A solution to this problem is to build a surrogate model employing a smaller number of model evaluations. The surrogate model is designed to provide an accurate and fast approximation to the detailed TREAT model. In this manner, the surrogate model can be used in place of the TREAT transient model for uncertainty and sensitivity analysis at a fraction of the computational cost. The high dimensional model representation (HDMR) method is employed here for approximating the responses of interest in terms of functions of lower dimensions. This method has been implemented in RAVEN and all TREAT transient calculations are performed using the multiphysics code MAMMOTH. In addition, a code interface has also been developed in order to couple RAVEN with MAMMOTH. The surrogate model is then tested and compared for TREAT startup testing program Transient Test no. 15 core, and its accuracy is measured by its ability to estimate the mean, coefficient of variance, skewness, excess kurtosis and probability density function of the outputs of Transient Test no. 15 model, as well as root mean square error metric.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

Quantitative analysis of desorption and decomposition kinetics of formic acid on Cu(111): The importance of hydrogen bonding between adsorbed species

Quantitative analysis of desorption and decomposition kinetics of formic acid (HCOOH) on Cu(111) was performed by temperature programmed desorption (TPD), X-ray photoelectron spectroscopy, and time-resolved infrared reflection absorption spectroscopy. The activation energy for desorption is estimated to be 53–75 kJ/mol by the threshold TPD method as a function of coverage. Vibrational spectra of the first layer HCOOH at 155.3 K show that adsorbed molecules form a polymeric structure via the hydrogen bonding network. Adsorbed HCOOH molecules are dissociated gradually into monodentate formate species. The activation energy for the dissociation into monodentate formate species is estimated to be 65.0 kJ/mol at a submonolayer coverage (0.26 molecules/surface Cu atom). The hydrogen bonding between adsorbed HCOOH species plays an important role in the stabilization of HCOOH on Cu(111). The monodentate formate species are stabilized at higher coverages, because of the lack of vacant sites for the bidentate formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Soft pions and transport near the chiral critical point

During the expansion of a heavy ion collision, the system passes close to the $\textit{O}$(4) critical point of QCD, and thus the fluctuations of the order parameter $(σ, \overrightarrow{π}$) are expected to be enhanced. In this work, our goal is to compute how these enhanced fluctuations modify the transport coefficients of QCD near the pseudocritical point. We also make a phenomenological estimate for how chiral fluctuations could effect the momentum spectrum of soft pions. We first formulate the appropriate stochastic hydrodynamic equations close to the $\textit{O}$(4) critical point. Then, working in mean field, we determine the correlation functions of the stress tensor and the currents which result from this stochastic real-time theory, and use these correlation functions to determine the scaling behavior of the transport coefficients. The hydrodynamic theory also describes the propagation of pion waves, fixing the scaling behavior of the dispersion curve of soft pions. We present scaling functions for the shear viscosity and the charge conductivities near the pseudocritical point and estimate the absolute magnitude of the critical fluctuations to these parameters and the bulk viscosity. Using the calculated pion dispersion curve, we estimate the expected critical enhancement of soft pion yields, and this estimate provides a plausible explanation for the excess seen in experiment relative to ordinary hydrodynamic computations. Our results motivate further phenomenological and numerical work on the implications of chiral symmetry on real-time properties of thermal QCD near the pseudocritical point.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Nonparametric, data-based kernel interpolation for particle-tracking simulations and kernel density estimation

Traditional interpolation techniques for particle tracking include binning and convolutional formulas that use pre-determined (i.e., closed-form, parameteric) kernels. In many instances, the particles are introduced as point sources in time and space, so the cloud of particles (either in space or time) is a discrete representation of the Green’s function of an underlying PDE. As such, each particle is a sample from the Green’s function; therefore, each particle should be distributed according to the Green’s function. In short, the kernel of a convolutional interpolation of the particle sample “cloud” should be a replica of the cloud itself. This idea gives rise to an iterative method by which the form of the kernel may be discerned in the process of interpolating the Green’s function. When the Green’s function is a density, this method is broadly applicable to interpolating a kernel density estimate based on random data drawn from a single distribution. We formulate and construct the algorithm and demonstrate its ability to perform kernel density estimation of skewed and/or heavy-tailed data including breakthrough curves.

42 ENGINEERING↗
DOE OSTI2019

Future climate emulations using quantile regressions on large ensembles

The study of climate change and its impacts depends on generating projections of future temperature and other climate variables. For detailed studies, these projections usually require some combination of numerical simulation and observations, given that simulations of even the current climate do not perfectly reproduce local conditions. We present a methodology for generating future climate projections that takes advantage of the emergence of climate model ensembles, whose large amounts of data allow for detailed modeling of the probability distribution of temperature or other climate variables. The procedure gives us estimated changes in model distributions that are then applied to observations to yield projections that preserve the spatiotemporal dependence in the observations. We use quantile regression to estimate a discrete set of quantiles of daily temperature as a function of seasonality and long-term change, with smooth spline functions of season, long-term trends, and their interactions used as basis functions for the quantile regression. A particular innovation is that more extreme quantiles are modeled as exceedances above less extreme quantiles in a nested fashion, so that the complexity of the model for exceedances decreases the further out into the tail of the distribution one goes. We apply this method to two large ensembles of model runs using the same forcing scenario, both based on versions of the Community Earth System Model (CESM), run at different resolutions. The approach generates observation-based future simulations with no processing or modeling of the observed climate needed other than a simple linear rescaling. The resulting quantile maps illuminate substantial differences between the climate model ensembles, including differences in warming in the Pacific Northwest that are particularly large in the lower quantiles during winter. We show how the availability of two ensembles allows the efficacy of the method to be tested with a “perfect model” approach, in which we estimate transformations using the lower-resolution ensemble and then apply the estimated transformations to single runs from the high-resolution ensemble. Finally, we describe and implement a simple method for adjusting a transformation estimated from a large ensemble of one climate model using only a single run of a second, but hopefully more realistic, climate model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Sci-Fri AM: MRI and Diagnostic Imaging - 01: Estimating the Transverse Relaxation Time of Taurine Protons in Rat Brain at 9.4 T with Optimized PRESS Sequence Timings

Purpose: To investigate the response of taurine (Tau) protons as a function of PRESS echo times, TE{sub 1} and TE{sub 2}, to determine two TE combinations that can be employed to estimate the T{sub 2} (transverse relaxation) value of Tau at 9.4 T. The Tau protons are involved in J-coupling interactions; therefore, the two timing combinations should result in similar signal losses due to J-coupling. Methods: Experiments were performed with a 9.4 T animal MRI scanner. Numerical calculations of the response of Tau as a function of PRESS TE{sub 1} and TE{sub 2} were calculated and two TE combinations that yield a similar area for the 3.42 ppm Tau resonance were selected as optimal. The timings were verified on a 50 mM Tau/10 mM Cr (creatine) phantom. In-vivo experiments were performed on four rats. Spectra were acquired with the timings from a voxel placed in the rat brain and Tau peak areas were fit to monoexponentially decaying functions to obtain T{sub 2} values. Results: The PRESS TE combinations selected for Tau T{sub 2} determination are (TE{sub 1}, TE{sub 2}) = (17 ms, 10 ms) and (80 ms, 70 ms); the signal yield for the two timings differs by 5 % theoretically. The average Tau T{sub 2} for the four rats was found to be 106 ms with a standard deviation of 12 ms. Conclusions: We have demonstrated that acquiring PRESS spectra with (TE{sub 1}, TE{sub 2}) = (17 ms, 10 ms) and (TE{sub 1}, TE{sub 2}) = (80 ms, 70 ms) enables T{sub 2} corrected measures of Tau to be obtained at 9.4 T.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

State Preparation in Quantum Algorithms for Fragment-Based Quantum Chemistry

State preparation for quantum algorithms is crucial for achieving high accuracy in quantum chemistry and competing with classical algorithms. The localized active space–unitary coupled cluster (LAS–UCC) algorithm iteratively loads a fragment-based multireference wave function onto a quantum computer. Here, in this study, we compare two state preparation methods, quantum phase estimation (QPE) and direct initialization (DI), for each fragment. We test the two state preparation methods on three systems, ranging from a model system, a set of interacting hydrogen molecules, to more realistic chemical problems, like the C–C double bond breaking in transbutadiene and the spin ladder in a bimetallic system. We analyze the impact of QPE parameters, such as the number of ancilla qubits and Trotter steps, on the prepared state. We find a trade-off between the methods, where DI requires fewer resources for smaller fragments, while QPE is more efficient for larger fragments. Our resource estimates highlight the benefits of system fragmentation in state preparation for subsequent quantum chemical calculations. These findings have broad applications for preparing multireference quantum chemical wave functions on quantum circuits that can be used for realistic chemical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Surrogate Models for TREAT Transient Calculations

The Transient Reactor Test Facility (TREAT) at Idaho National Laboratory (INL) is currently preparing for resumption of operation after more than 20 years in a long-term standby state. In parallel with preparation for operations and a new transient testing program, the DOE-NE Advanced Modeling and Simulation program (NEAMS) has been supporting the development of three-dimensional transient multi-physics modeling capabilities within the reactor physics application MAMMOTH [1]. Recently, the MAMMOTH reactor physics team has begun development of uncertainty and sensitivity analysis for the TREAT core. This research will be used to support the analysis of reactor operation and experiment planning. Traditional approaches, i.e., Monte Carlo simulations, for such tasks require a large number of model evaluations under both steady state and transient conditions. However, exhaustive sets of sampling runs are simply not practical here since evaluations of each model can take a substantial amount of time. A solution to this problem is to build a surrogate model employing a smaller number of model evaluations. The surrogate model is designed to provide an accurate and fast approximation to the detailed TREAT model. In this manner, the surrogate model can be used in place of the TREAT transient model for uncertainty and sensitivity analysis at a fraction of the computational cost. The high dimensional model representation (HDMR) [2] method is employed here for approximating the responses of interest in terms of functions of lower dimensions. This method has been implemented in RAVEN [3] and all TREAT transient calculations are performed using the multiphysics code MAMMOTH. In addition, a code interface has also been developed in order to couple RAVEN with MAMMOTH. The surrogate model is then tested and compared for the MAMMOTH model of the TREAT startup testing program Transient Test #15 core [4], and its accuracy is measured by its ability to estimate the mean, coefficient of variance, skewness, excess kurtosis and probability density function of the outputs of Transient Test #15 model, as well as root mean square error metric.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Simple Models of Lateral Heterostructures

General analytical expressions for densities of states of a lateral heterostructure, formed by the contact of two square semi-infinite lattices with single-band and two-band spectra, were obtained in the tight-binding approximation by the Green’s function method. The semi-elliptical density of states was used for numerical estimates, and the model of two interacting dimers was proposed to estimate the charge transfer. Application of this approach to description of lateral epitaxial and graphene-like heterostructures is discussed.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

CAMERA: A method for cost-aware, adaptive, multifidelity, efficient reliability analysis

Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Failure probability estimation involves resolving tails of probability distributions, and Monte Carlo sampling methods are intractable when expensive high-fidelity simulations have to be queried. Here, we propose a method to use models of multiple fidelities that trade accuracy for computational efficiency. Specifically, we propose the use of multifidelity Gaussian process models to efficiently fuse models at multiple fidelity, thereby offering a cheap surrogate model that emulates the original model at all fidelities. Furthermore, we propose a novel sequential acquisition function based experiment design framework that can automatically select samples from appropriate fidelity models to make predictions about quantities of interest at the highest fidelity. We use our proposed approach in an importance sampling setting and demonstrate our method on the failure level set and probability estimation on synthetic test functions and two real-world applications, namely, the reliability analysis of a gas turbine engine blade using a finite element method and a transonic aerodynamic wing test case using Reynolds-averaged Navier-Stokes equations. We show that our method predicts the failure boundary and probability more accurately and at a fraction of the computational cost compared with using just a single expensive high-fidelity model. Finally, we show that our sequential approach is guaranteed to asymptotically converge to the true failure boundary with high probability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Correlation function of the luminosity distances

We present the correlation function of the luminosity distances in a flat ΛCDM universe. Decomposing the luminosity distance fluctuation into the velocity, the gravitational potential, and the lensing contributions in linear perturbation theory, we study their individual contributions to the correlation function. The lensing contribution is important at large redshift ( z ∼> 0.5) but only for small angular separation (θ ∼< 3°), while the velocity contribution dominates over the other contributions at low redshift or at larger separation. However, the gravitational potential contribution is always subdominant at all scale, if the correct gauge-invariant expression is used. The correlation function of the luminosity distances depends significantly on the matter content, especially for the lensing contribution, thus providing a novel tool of estimating cosmological parameters.

79 ASTRONOMY AND ASTROPHYSICS↗