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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Domain Structures of Ni and NiFe (Oxy)Hydroxide Oxygen-Evolution Catalysts from X-ray Pair Distribution Function Analysis

Ni-Fe (oxy)hydroxides, Ni( i-z )Fe z O x H y , are among the fastest-known water oxidation catalysts in alkaline media on a per-cation basis. At current densities relevant for electrolysis (e.g., >0.5 A/cm( -2 )), mass and electron transport through catalyst films with high mass loading are critical and depend substantially on the extended and intermediate catalyst architecture. We use X-ray pair distribution function (PDF) analysis to determine the intermediate nanostructures of electrodeposited Ni( i-z )Fe z O x H y films. We report the effects of electrodeposition technique (pulsed versus continuous), electrochemical cycling, and Fe content on the structure of the catalyst film. The PDF patterns for Ni( i-z )Fe z O x H y films are best simulated by model structures consisting of brucite-like β-Ni(OH) 2 fragments 1 to 3 layers in thickness. Only the oxidation state of the film significantly affects the intralayer scattering behavior (i.e., metal-oxygen bond distance). The interlayer interactions, however, are affected by Fe content and deposition conditions. The domain size of many of the systems are similar, extending to similar to ~5 nm, which are best modeled by sheets containing upward of similar to ~250 metal cations. Smaller domains were found for films deposited through a larger number of electrochemical cathodic current pulses. Films can be cycled between as-deposited, oxidized, and reduced states, with minimal loss of intrasheet coherence, indicating a degree of structural stability. We estimate heterogeneity in the domain structures by modeling the PDF data to linear combinations of oxyhydroxide fragments with different sizes and numbers of layers.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Steady state likelihood ratio sensitivity analysis for stiff kinetic Monte Carlo simulations

Kinetic Monte Carlo simulation is an integral tool in the study of complex physical phenomena present in applications ranging from heterogeneous catalysis to biological systems to crystal growth and atmospheric sciences. Sensitivity analysis is useful for identifying important parameters and rate-determining steps, but the finite-difference application of sensitivity analysis is computationally demanding. Techniques based on the likelihood ratio method reduce the computational cost of sensitivity analysis by obtaining all gradient information in a single run. However, we show that disparity in time scales of microscopic events, which is ubiquitous in real systems, introduces drastic statistical noise into derivative estimates for parameters affecting the fast events. In this work, the steady-state likelihood ratio sensitivity analysis is extended to singularly perturbed systems by invoking partial equilibration for fast reactions, that is, by working on the fast and slow manifolds of the chemistry. Derivatives on each time scale are computed independently and combined to the desired sensitivity coefficients to considerably reduce the noise in derivative estimates for stiff systems. The approach is demonstrated in an analytically solvable linear system.

Núñez, M.↗
DOE OSTI2018

Gradient-based stochastic estimation of the density matrix

Fast estimation of the single-particle density matrix is key to many applications in quantum chemistry and condensed matter physics. The best numerical methods leverage the fact that the density matrix elements f(H) ij decay rapidly with distance rij between orbitals. This decay is usually exponential. However, for the special case of metals at zero temperature, algebraic decay of the density matrix appears and poses a significant numerical challenge. Here, we introduce a gradient-based probing method to estimate all local density matrix elements at a computational cost that scales linearly with system size. For zero-temperature metals, the stochastic error scales like S -(d+2)/2d , where d is the dimension and S is a prefactor to the computational cost. The convergence becomes exponential if the system is at finite temperature or is insulating.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Measuring the largest angular scale CMB B-mode polarization with galactic foregrounds on a cut sky

We consider the effectiveness of foreground cleaning in the recovery of Cosmic Microwave Background (CMB) polarization sourced by gravitational waves for tensor-to-scalar ratios in the range 0<r<0.1. Using the planned survey area, frequency bands, and sensitivity of the Cosmology Large Angular Scale Surveyor (CLASS), we simulate maps of Stokes Q and U parameters at 40, 90, 150, and 220 GHz, including realistic models of the CMB, diffuse Galactic thermal dust and synchrotron foregrounds, and Gaussian white noise. We use linear combinations (LCs) of the simulated multifrequency data to obtain maximum likelihood estimates of r, the relative scalar amplitude s, and LC coefficients. We find that for 10,000 simulations of a CLASS-like experiment using only measurements of the reionization peak (ℓ⩽23), there is a 95% C.L. upper limit of r<0.017 in the case of no primordial gravitational waves. For simulations with r=0.01, we recover at 68% C.L. r=0.012{sub −0.006}{sup +0.011}. The reionization peak corresponds to a fraction of the multipole moments probed by CLASS, and simulations including 30⩽ℓ⩽100 further improve our upper limits to r<0.008 at 95% C.L. (r=0.010{sub −0.004}{sup +0.004} for primordial gravitational waves with r = 0.01). In addition to decreasing the current upper bound on r by an order of magnitude, these foreground-cleaned low multipole data will achieve a cosmic variance limited measurement of the E-mode polarization’s reionization peak.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

The intrinsic bispectrum of the cosmic microwave background

We develop a new, efficient code for solving the second-order Einstein-Boltzmann equations, and use it to estimate the intrinsic CMB non-Gaussianity arising from the non-linear evolution of density perturbations. The full calculation involves contributions from recombination and less tractable contributions from terms integrated along the line of sight. We investigate the bias that this intrinsic bispectrum implies for searches of primordial non-Gaussianity. We find that the inclusion or omission of certain line of sight terms can make a large impact. When including all physical effects but lensing and time-delay, we find that the local-type f{sub NL} would be biased by f{sub NL}{sup intra} = 0.5, below the expected sensitivity of the Planck satellite. The speed of our code allows us to confirm the robustness of our results with respect to a number of numerical parameters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Submonolayer Ag films on Fe(100): A first-principles analysis of energetics controlling adlayer thermodynamics and kinetics

Epitaxial growth of Ag on Fe(100) and postdeposition relaxation have been studied in several experiments. We provide a first-principles density functional theory analysis of key adatom interaction energies and diffusion barriers controlling growth and relaxation kinetics for the submonolayer regime, as these have not been assessed previously. A cluster expansion approach is used to obtain an extensive set of conventional lateral interactions between adatoms on fourfold hollow adsorption sites. We find robust oscillatory decay of pair interactions with increasing separation, and of trio interactions with increasing perimeter length. First- and second-nearest-neighbor pair interactions, as well as compact linear and bent trio interactions, dominate. The adatom terrace diffusion barrier is estimated to be E d ≈ 0.39 eV. We also provide a limited analysis of unconventional interactions for which one adatom is at the bridge-site transition state for hopping and one or more others are at fourfold hollow sites. Furthermore, energy barriers for diffusion along island edges can be determined with the aid of both conventional and unconventional interactions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Data-driven predictive probability density function control of fiber length stochastic distribution shaping in refining process

Pulp is the most important raw material in paper in paper industries, whose Fiber Length Stochastic Distribution (FLSD) shaping directly determines the energy consumption and paper quality of the subsequent papermaking processes. However, the mean and variance are insufficient to describe the output FLSD shaping, which display non-Gaussian distribution properties. Therefore, the traditional control, method based on the mean and variance of fiber length is difficult to control the FLSD shaping effectively. In this paper, a novel data-driven predictive probability density function (PDF) control method is proposed for output FLSD shaping in refining process. Primarily, in order to improve the approximation accuracy of the PDF employing RBF neural network, the parameters (center value and width) of each RBF basis functions are tuned via utilizing iterative learning control (ILC) law, and the corresponding estimations of weights law can be obtained. Secondly, considering that the conventional linear model of weights vector has drawbacks of low accuracy and weak generalization ability, random vector functional link networks (RVFLNs) based data driven nonlinear modelling method is employed to characterize the prediction model between the input variables and weights vector. Finally, in order to reduce the randomness of the output FLSD, a minimum entropy control method under mean constraint is employed to design the predictive PDF controller for the output FLSD shaping with the help of the established nonlinear stochastic distribution model. Both simulations and experiments are given to illustrate the effectiveness and practicability of the proposed method.

Probability debsity function, Fiber length distrib↗
DOE OSTI2016

Towards 3D printed multifunctional immobilization for proton therapy: Initial materials characterization

Purpose: 3D printing technology is investigated for the purpose of patient immobilization during proton therapy. It potentially enables a merge of patient immobilization, bolus range shifting, and other functions into one single patient-specific structure. In this first step, a set of 3D printed materials is characterized in detail, in terms of structural and radiological properties, elemental composition, directional dependence, and structural changes induced by radiation damage. These data will serve as inputs for the design of 3D printed immobilization structure prototypes. Methods: Using four different 3D printing techniques, in total eight materials were subjected to testing. Samples with a nominal dimension of 20 × 20 × 80 mm{sup 3} were 3D printed. The geometrical printing accuracy of each test sample was measured with a dial gage. To assess the mechanical response of the samples, standardized compression tests were performed to determine the Young’s modulus. To investigate the effect of radiation on the mechanical response, the mechanical tests were performed both prior and after the administration of clinically relevant dose levels (70 Gy), multiplied with a safety factor of 1.4. Dual energy computed tomography (DECT) methods were used to calculate the relative electron density to water ρ{sub e}, the effective atomic number Z{sub eff}, and the proton stopping power ratio (SPR) to water SPR. In order to validate the DECT based calculation of radiological properties, beam measurements were performed on the 3D printed samples as well. Photon irradiations were performed to measure the photon linear attenuation coefficients, while proton irradiations were performed to measure the proton range shift of the samples. The directional dependence of these properties was investigated by performing the irradiations for different orientations of the samples. Results: The printed test objects showed reduced geometric printing accuracy for 2 materials (deviation > 0.25 mm). Compression tests yielded Young’s moduli ranging from 0.6 to 2940 MPa. No deterioration in the mechanical response was observed after exposure of the samples to 100 Gy in a therapeutic MV photon beam. The DECT-based characterization yielded Z{sub eff} ranging from 5.91 to 10.43. The SPR and ρ{sub e} both ranged from 0.6 to 1.22. The measured photon attenuation coefficients at clinical energies scaled linearly with ρ{sub e}. Good agreement was seen between the DECT estimated SPR and the measured range shift, except for the higher Z{sub eff}. As opposed to the photon attenuation, the proton range shifting appeared to be printing orientation dependent for certain materials. Conclusions: In this study, the first step toward 3D printed, multifunctional immobilization was performed, by going through a candidate clinical workflow for the first time: from the material printing to DECT characterization with a verification through beam measurements. Besides a proof of concept for beam modification, the mechanical response of printed materials was also investigated to assess their capabilities for positioning functionality. For the studied set of printing techniques and materials, a wide variety of mechanical and radiological properties can be selected from for the intended purpose. Moreover the elaborated hybrid DECT methods aid in performing in-house quality assurance of 3D printed components, as these methods enable the estimation of the radiological properties relevant for use in radiation therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Modeling energy balance while correcting for measurement error via free knot splines

Measurements of energy balance components (energy intake, energy expenditure, changes in energy stores) are often plagued with measurement error. Doubly-labeled water can measure energy intake (EI) with negligible error, but is expensive and cumbersome. An alternative approach that is gaining popularity is to use the energy balance principle, by measuring energy expenditure (EE) and change in energy stores (ES) and then back-calculate EI. Gold standard methods for EE and ES exist and are known to give accurate measurements, albeit at a high cost. We propose a joint statistical model to assess the measurement error in cheaper, non-intrusive measures of EE and ES. We let the unknown true EE and ES for individuals be latent variables, and model them using a bivariate distribution. We try both a bivariate Normal as well as a Dirichlet Process Mixture Model, and compare the results via simulation. Our approach, is the first to account for the dependencies that exist in individuals’ daily EE and ES. We employ semiparametric regression with free knot splines for measurements with error, and linear components for error free covariates. We adopt a Bayesian approach to estimation and inference and use Reversible Jump Markov Chain Monte Carlo to generate draws from the posterior distribution. Based on the semiparameteric regression, we develop a calibration equation that adjusts a cheaper, less reliable estimate, closer to the true value. Along with this calibrated value, our method also gives credible intervals to assess uncertainty. A simulation study shows our calibration helps produce a more accurate estimate. Furthermore, our approach compares favorably in terms of prediction to other commonly used models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Closed Loop Geothermal Working Group: GeoCLUSTER App, Subsurface Simulation Results, and Publications

To better understand the heat production, electricity generation performance, and economic viability of closed-loop geothermal systems in hot-dry rock, the Closed-Loop Geothermal Working Group -- a consortium of several national labs and academic institutions has tabulated time-dependent numerical solutions and levelized cost results of two popular closed-loop heat exchanger designs (u-tube and co-axial). The heat exchanger designs were evaluated for two working fluids (water and supercritical CO2) while varying seven continuous independent parameters of interest (mass flow rate, vertical depth, horizontal extent, borehole diameter, formation gradient, formation conductivity, and injection temperature). The corresponding numerical solutions (approximately 1.2 million per heat exchanger design) are stored as multi-dimensional HDF5 datasets and can be queried at off-grid points using multi-dimensional linear interpolation. A Python script was developed to query this database and estimate time-dependent electricity generation using an organic Rankine cycle (for water) or direct turbine expansion cycle (for CO2) and perform a cost assessment. This document aims to give an overview of the HDF5 database file and highlights how to read, visualize, and query quantities of interest (e.g., levelized cost of electricity, levelized cost of heat) using the accompanying Python scripts. Details regarding the capital, operation, and maintenance and levelized cost calculation using the techno-economic analysis script are provided. This data submission will contain results from the Closed Loop Geothermal Working Group study that are within the public domain, including publications, simulation results, databases, and computer codes. GeoCLUSTER is a Python-based web application created using Dash, an open-source framework built on top of Flask that streamlines the building of data dashboards. GeoCLUSTER provides users with a collection of interactive methods for streamlining the exploration and visualization of an HDF5 dataset. The GeoCluster app and database are contained in the compressed file geocluster_vx.zip, where the "x" refers to the version number. For example, geocluster_v1.zip is Version 1 of the app. This zip file also contains installation instructions. **To use the GeoCLUSTER app in the cloud, click the link to "GeoCLUSTER on AWS" in the Resources section below. To use the GeoCLUSTER app locally, download the geocluster_vx.zip to your computer and uncompress this file. When uncompressed this file comprises two directories and the geocluster_installation.pdf file. The geo-data app contains the HDF5 database in condensed format, and the GeoCLUSTER directory contains the GeoCLUSTER app in the subdirectory dash_app, as app.py. The geocluster_installation.pdf file provides instructions on installing Python, the needed Python modules, and then executing the app.

15 GEOTHERMAL ENERGY↗
DOE OSTI2014

FFAG Designs for Muon Collider Acceleration

I estimate FFAG parameters for a muon collider with a 70mm longitudinal emittance. I do not discuss the lower emittance beam for a Higgs factory. I produce some example designs, giving only parameters relevant to estimating cost and performance. The designs would not track well, but the parameters of a good design will be close to those described. I compare these cost estimates to those for a fast-ramping synchrotron and a recirculating linear accelerator. I conclude that FFAGs do not appear to be cost-effective for the large longitudinal emittance in a high-energy muon collider.

43 PARTICLE ACCELERATORS↗
DOE OSTI2017

Precision and Accuracy in PDV and VISAR

This is a technical report discussing our current level of understanding of a wide and varying distribution of uncertainties in velocity results from Photonic Doppler Velocimetry in its application to gas gun experiments. Using propagation of errors methods with statistical averaging of photon number fluctuation in the detected photocurrent and subsequent addition of electronic recording noise, we learn that the velocity uncertainty in VISAR can be written in closed form. For PDV, the non-linear frequency transform and peak fitting methods employed make propagation of errors estimates notoriously more difficult to write down in closed form expect in the limit of constant velocity and low time resolution (large analysis-window width). An alternative method of error propagation in PDV is to use Monte Carlo methods with a simulation of the time domain signal based on results from the spectral domain. A key problem for Monte Carlo estimation for an experiment is a correct estimate of that portion of the time-domain noise associated with the peak-fitting region-of-interesting in the spectral domain. Using short-time Fourier transformation spectral analysis and working with the phase dependent real and imaginary parts allows removal of amplitude-noise cross terms that invariably show up when working with correlation-based methods or FFT power spectra. Estimation of the noise associated with a given spectral region of interest is then possible. At this level of progress, we learn that Monte Carlo trials with random recording noise and initial (uncontrolled) phase yields velocity uncertainties that are not as large as those observed. In a search for additional noise sources, a speckleinterference modulation contribution with off axis rays was investigated, and was found to add a velocity variation beyond that from the recording noise (due to random interference between off axis rays), but in our experiments the speckle modulation precision was not as important as the recording noise precision. But from these investigations we do appreciate that the velocity-uncertainty itself has a wide distribution of values that varies with signal-amplitude modulation (is not a single value). To provide a rough rule of thumb for the velocity uncertainty, we computed the average of the relative standard deviation distributions from 60 recorded traces (with distributions of uncertainties roughly between 0.1 % to 1 % in each trace) and found a mean of the distribution of uncertainties for our experiments is not better than 0.4 % at an analysis window width of 5 ns (although for brief intervals it can be as good as 0.1 %). Further imagination and testing may be needed to reveal other possible hydrodynamics-related sources of velocity error in PDV.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

A double peak in the seasonality of California's photosynthesis as observed from space

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and gross primary productivity (GPP). The recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal-to-noise ratio to retrieve SIF from space. Here, we present a downscaling method to obtain 500 m spatial resolution SIF over California. We report daily values based on a 14 d window. TROPOMI SIF data show a strong correspondence with daily GPP estimates at AmeriFlux sites across multiple ecosystems in California. We find a linear relationship between SIF and GPP that is largely invariant across ecosystems with an intercept that is not significantly different from zero. Measurements of SIF from TROPOMI agree with MODerate Resolution Imaging Spectroradiometer (MODIS) vegetation indices – the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation index (NIR$_v$) – at annual timescales but indicate different temporal dynamics at monthly and daily timescales. TROPOMI SIF data show a double peak in the seasonality of photosynthesis, a feature that is not present in the MODIS vegetation indices. The different seasonality in the vegetation indices may be due to a clear-sky bias in the vegetation indices, whereas previous work has shown SIF to have a low sensitivity to clouds and to detect the downregulation of photosynthesis even when plants appear green. We further decompose the spatiotemporal patterns in the SIF data based on land cover. The double peak in the seasonality of California's photosynthesis is due to two processes that are out of phase: grasses, chaparral, and oak savanna ecosystems show an April maximum, while evergreen forests peak in June. An empirical orthogonal function (EOF) analysis corroborates the phase offset and spatial patterns driving the double peak. The EOF analysis further indicates that two spatiotemporal patterns explain 84 % of the variability in the SIF data. Results shown here are promising for obtaining global GPP at sub-kilometer spatial scales and identifying the processes driving carbon uptake.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1973

Investigation of empirical damping laws for the space shuttle

An analysis of dynamic test data from vibration testing of a number of aerospace vehicles was made to develop an empirical structural damping law. A systematic attempt was made to fit dissipated energy/cycle to combinations of all dynamic variables. The best-fit laws for bending, torsion, and longitudinal motion are given, with error bounds. A discussion and estimate are made of error sources. Programs are developed for predicting equivalent linear structural damping coefficients and finding the response of nonlinearly damped structures.

Bernstein, E. L.↗
NASA NTRS1973

Range of earth structure nonuniqueness implied by body wave observations.

The Herglotz-Wiechert integral for the direct inversion of ray parameter versus distance curves can be manipulated to find the envelope of all possible models consistent with geometrical body wave observations (travel time and ray parameter versus distance). Such an extremal inversion approach has been used to find the uncertainty bounds for the velocity structure in the mantle and core. It is found, for example, that there is an uncertainty of plus or minus 40 km in the radius of the inner core boundary, plus or minus 18 km at the core-mantle boundary, and plus or minus 35 km at the 435-km transition zone. The velocity uncertainty is about plus or minus 0.08 km/sec for P and S waves in the lower mantle and about plus or minus 0.20 km/sec in the core. Experiments with various combinations of ray types in the core indicate that rather crude observations of SKKS-SKS travel times confine the range of possible models far more dramatically than do the most precise estimates of PmKP travel times. Comparisons of results from extremal inversion and linearized perturbation inversions indicate that body wave behavior is too strongly nonlinear for linearized schemes to be effective for predicting uncertainty.

Wiggins, R. A.↗
NASA NTRS1978

Galileo satellite tour maneuver strategies

In January of 1982 the United States will launch Galileo, the first space mission designed to perform an intensive tour of the Galilean satellites of Jupiter. This paper discusses the results of preliminary maneuver strategy studies for typical interplanetary and orbital phases of the Galileo mission. These studies include the effects of maneuver placement and various linear control algorithms on propellant consumption as determined by Monte Carlo analysis. Estimates of total propellant required for various probabilities of mission success are also given.

Miller, L. J.↗