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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Photometric and spectroscopic footprint corrections in the SLOAN Digital Sky Survey’s 6th data release

We identify and correct numerous errors within the photometric and spectroscopic footprints (SFs) of the Sloan Digital Sky Survey’s (SDSS) 6th data release (DR6). Within the SDSS’s boundaries hundreds of millions of objects have been detected. Yet we present evidence that the boundaries themselves contain a significant number of mistakes that are being revealed for the first time within this paper. Left unaddressed, these can introduce systematic biases into galaxy clustering statistics. Using the DR6 Main Galaxy Sample (MGS) targets as tracers, we reveal inconsistencies between the photometric and SF definitions provided in the Catalog Archive Server (CAS), and the measurements of targets therein. First, we find that 19.7 deg{sup 2} of the DR6 photometric footprint are devoid of MGS targets. In volumes of radii 7 h{sup −1} Mpc, this can cause errors in the expected number of galaxies to exceed 60%. Second, we identify seven areas that were erroneously included or excluded from the SF. Moreover, the tiling algorithm that positioned spectroscopic fibers during and between DRs caused many areas along the edge of the SF to be significantly undersampled relative to the footprint’s interior. Through our corrections, we increase the completeness 2.2% by trimming 3.6% of the area from the existing SF. The sum total of these efforts has generated the most accurate description of the SDSS DR6 footprints ever created.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

CV4Quantum: Reducing the Sampling Overhead in Probabilistic Error Cancellation Using Control Variates

Quasiprobabilistic decompositions (QPDs) play a key role in maximizing the utility of near-term quantum hardware. For example, Probabilistic Error Cancellation (PEC) (an error mitigation technique) and circuit cutting (which enables large quantum computations to be performed on quantum hardware with a limited number of qubits) both involve QPDs. Computations based on QPDs typically incur large sampling overheads that grow exponentially with the number of error-terms mitigated or number of circuit-cuts employed, limiting their practical feasibility. In this work, we adapt the control variates variance reduction technique from the statistics literature in order to reduce the sampling overhead in QPD-based computations. We demonstrate our method using simulation experiments that mimic a realistic PEC scenario. In our experiments, we observed a more than 50% reduction in the number of samples needed to achieve a given precision, in more than 50% of the PEC-based estimations performed in the study when using our approach. We discuss how future research on constructing good control variates can lead to even stronger sampling overhead reduction.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

How Bayesian methods can improve R -matrix analyses of data: The example of the d t reaction

The 3 H(d, n) 4 He reaction is of significant interest in nuclear astrophysics and nuclear applications. It is an important, early step in big-bang nucleosynthesis and a key process in nuclear fusion reactors. We use one- and two-level R-matrix approximations to analyze data on the cross section for this reaction at center-of-mass energies below 215 keV. We critically examine the data sets using a Bayesian statistical model that allows for both common-mode and additional point-to-point un- certainties. We use Markov Chain Monte Carlo sampling to evaluate this R-matrix-plus-statistical model and find two-level R-matrix results that are stable with respect to variations in the channel radii. The S factor at 40 keV evaluates to 25.36(19) MeV b (68% credibility interval). We discuss our Bayesian analysis in detail and provide guidance for future applications of Bayesian methods to R-matrix analyses. We also discuss possible paths to further reduction of the S-factor uncertainty.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Subgrain geometrically necessary dislocation density mapping in spalled Ta in three dimensions

Here, in this work, the intragranular distributions of the densities of geometrically necessary dislocations (GNDs) generated in a polycrystalline tantalum (Ta) sample loaded in shock compression to spall are investigated. The sample studied is a highly resolved three-dimensional (3D) map of the orientations, grain boundary morphology, and voids across a 0.276mm 3 (with sample dimensions of 688.5µm x 669µm x 600µm) region of the microstructure about the spall plane obtained by TriBeam tomography. We present a formulation for calculating the crystallographic screw and edge character GND densities at a material point within body centered cubic (BCC) crystals when provided any number of potential slip systems or modes. Combining the 3D characterization, GND formulation, and a sample containing ≈ 6000 grains, we sought statistically relevant correlations between GND density per grain with grain size, grain orientation, void formation, and proximity from the spall plane. We show that GND density per grain increases with decreasing distance from the spall plane. Most grains had heterogeneously distributed GND densities but were free of any detectable voids, at least within the ≈ 1.5µm resolution of the characterization technique. It is found that the GND density for most grains is insensitive to grain size and grain orientation. Only the fraction of grains with the highest GND densities tended to have a [001] parallel to shock-direction orientation. Intragranular 3D maps of GNDs reveals that grains that contain voids have high GND density concentrations in the intragranular region immediately surrounding the void. The local intragranular GND mapping also showed voids that are connected to grain boundaries preferred to grow into the grain with the larger size, regardless of orientation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Projection pursuit adaptation on polynomial chaos expansions

Here, the present work addresses the issue of accurate stochastic approximations in high-dimensional parametric space using tools from uncertainty quantification (UQ). The basis adaptation method and its accelerated algorithm in polynomial chaos expansions (PCE) were recently proposed to construct low-dimensional approximations adapted to specific quantities of interest (QoI). The present paper addresses one difficulty with these adaptations, namely their reliance on quadrature point sampling, which limits the reusability of potentially expensive samples. Projection pursuit (PP) is a statistical tool to find the “interesting” projections in high-dimensional data and thus bypass the curse-of-dimensionality. In the present work, we combine the fundamental ideas of basis adaptation and projection pursuit regression (PPR) to propose a novel method to simultaneously learn the optimal low-dimensional spaces and PCE representation from given data. While this projection pursuit adaptation (PPA) can be entirely data-driven, the constructed approximation exhibits mean-square convergence to the solution of an underlying governing equation and thus captures the supports and probability distributions associated with the physics constraints. The proposed approach is demonstrated on a borehole problem and a structural dynamics problem, demonstrating the versatility of the method and its ability to discover low-dimensional manifolds with high accuracy with limited data. In addition, the method can learn surrogate models for different quantities of interest while reusing the same data set.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Multi-stage heat release of multi-component fuels: Insights and implications for advanced engine operation

Multi-stage heat release (MSHR) is a unique phenomenon typically seen in lean/diluted and low- to intermediate-temperature combustion. Despite its relevance to advanced engine operation, the MSHR of multi-component fuels has barely been quantified. This study aims to characterize the MSHR of multi-component fuels in a rapid compression machine (RCM) at conditions representative of advanced combustion engines. New experimental data are first reported in the RCM at an equivalence ratio of 0.4, pressure of 60 bar and temperatures from 702 to 795 K for a research grade, multi-component gasoline surrogate, termed PACE-20. Here, experiments confirm the existence of MSHR for PACE-20 at all temperatures, and reveal the strong inhibiting effect of temperature on MSHR, where increasing temperature inhibits MSHR by suppressing first stage heat release and promoting second and third stage heat release. A detailed chemical kinetic model is also adopted to model the experiments, with good agreement observed. The response of MSHR characteristics to changes in different engine operating parameters (i.e., temperature, pressure, equivalence ratio and CO 2 dilution level) and fuel compositions (i.e., mole fraction of n-pentane, n-heptane, isooctane, cyclopentane, 1-hexene, ethanol and aromatics in PACE-20) is further evaluated via statistical analysis coupling quasi-random sampling, extensive computer experiments and high-dimensional model representation. The change in MSHR is characterized through 8 quantities of interest (QOIs), i.e., duration and extent of each heat release stage, and their standard deviations. The analysis highlights the dependence of the QOIs on the individual parameters as well as their interplays, with temperature exhibiting the strongest impact among all parameters. Furthermore, it is demonstrated that if MSHR is to be facilitated and combustion phasing is to be extended to enable engine operation at higher compression ratios, it is recommended to operate the engine at low intake temperatures, boosted intake pressures and high CO 2 dilutions (i.e., high EGR levels) with gasoline fuels containing more n-pentane and less aromatics.

42 ENGINEERING↗
DOE OSTI2019

Multistate hybrid time-dependent density functional theory with surface hopping accurately captures ultrafast thymine photodeactivation

We report an efficient analytical implementation of first-order nonadiabatic derivative couplings between arbitrary Born–Oppenheimer states in the hybrid time-dependent density functional theory (TDDFT) framework using atom-centered basis functions. Our scheme is based on quadratic response theory and includes orbital relaxation terms neglected in previous approaches. Simultaneous computation of multiple derivative couplings and energy gradients enables efficient multistate nonadiabatic molecular dynamics simulations in conjunction with Tully's fewest switches surface hopping (SH) method. We benchmark the thus obtained multistate TDDFT-SH scheme by simulating ultrafast decay of UV-photoexcited thymine, for which accurate gas-phase data from ultrafast spectroscopy experiments are available. The calculations predict a fast 153 fs decay from the bright S 2 to the dark S 1 excited state, followed by a much slower 14 ps S 1 deactivation to the ground state; statistical uncertainties were estimated using bootstrap sampling. These results agree well with the experimentally observed time constants of 100–200 fs and 5–7 ps, respectively, unlike previous multiconfigurational self-consistent field and second-order algebraic diagrammatic construction calculations. Furthermore, our results support the S 1 -trapping hypothesis. For thymine, the computational cost of a single TDDFT-SH time-step including the lowest 3 states, all couplings and gradients, is ~5 times larger than the cost of a single Born–Oppenheimer dynamics time step for the ground state in our implementation. Thus, ps nonadiabatic dynamics simulations using multistate hybrid TDDFT-SH for systems with up to ~100 atoms are possible without drastic approximations on single workstation nodes. Our implementation will be made available through Turbomole.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Tunability, dielectric, and piezoelectric properties of Ba{sub (1−x)}Ca{sub x}Ti{sub (1−y)}Zr{sub y}O{sub 3} ferroelectric thin films

Tunable ferroelectric capacitors, which exhibit a decrease of the dielectric permittivity (ϵ) under electric field, are widely used in electronics for RF tunable applications (e.g., antenna impedance matching). Current devices use barium strontium titanate as the tunable dielectric, and the need for performance enhancement of the tunable element is the key for device improvement. We report here on libraries of Ba{sub 0.97}Ca{sub 0.03}Ti{sub 1−x}Zr{sub x}O{sub 3} thin films (0 ≤ x ≤ 27%) with a thickness of about 130 nm deposited on IrO{sub 2}/SiO{sub 2}/Si substrates using combinatorial pulsed laser deposition allowing for gradients of composition on one sample. A total of 600 capacitors on a single sample were characterized in order to statistically investigate the dielectric properties. We show that the tunabilty is maximum at intermediate compositions, reaching values up to 60% for an electric field of about 400 kV cm{sup −1}. We attribute the high tunability in the intermediate compositions to the paraelectric-ferroelectric phase transition, which is brought down to room temperature by the addition of Zr. In addition, the piezoelectric coefficient is found to be decreasing with increasing Zr content.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Seven broad absorption line quasars with excess broadband absorption near 2250 Å

We report the discovery of excess broadband absorption (EBBA) near 2250 Å in the spectra of seven broad absorption line (BAL) quasars. By comparing with the statistical results from the control quasar sample, the significance for the detections are all above the ≳4σ level, with five above >5σ. The detections have also been verified by several other independent methods. The EBBAs present broader and weaker bumps at smaller wavenumbers than the Milky Way, and similar to the Large Magellanic Cloud. The EBBA bump may be related to the 2175 Å bump seen in the Local Group and may be a counterpart of the 2175 Å bump under different conditions in the early universe. Furthermore, five objects in this sample show low-ionization BAL (LoBALs), such as Mg ii and Al iii, in addition to the high-ionization BAL of C iv and Si iv. The fraction of LoBALs in our sample, ∼70%, is surprisingly high compared to that of general BAL quasars, ∼10%. Although the origin of the bump is still not clear, the coexistence of both BALs and bumps and the significantly high fraction of LoBALs may indicate that the bump carriers are closely related to the early evolution phase of quasars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

From H i to stars: H I depletion in starbursts and star-forming galaxies in the ALFALFA Hα survey

H i in galaxies traces the fuel for future star formation and reveals the effects of feedback on neutral gas. Using a statistically uniform, H i-selected sample of 565 galaxies from the Arecibo Legacy Fast ALFA (ALFALFA) Hα survey, we explore H i properties as a function of star formation activity. ALFALFA Hα provides R-band and Hα imaging for a volume-limited subset of the 21 cm ALFALFA survey. We identify eight starbursts based on Hα equivalent width and six with enhanced star formation relative to the main sequence. Both starbursts and non-starbursts have similar H i-to-stellar mass ratios (M{sub H I}/M{sub ∗}), which suggests that feedback is not depleting the starbursts’ H i. Consequently, the starbursts do have shorter H i depletion times (t{sub dep}), implying more efficient H i-to-H{sub 2} conversion. While major mergers likely drive this enhanced efficiency in some starbursts, the lowest-mass starbursts may experience periodic bursts, consistent with enhanced scatter in t{sub dep} at low M{sub ∗}. Two starbursts appear to be pre-coalescence mergers; their elevated M{sub H I}/M{sub ∗} suggest that H i-to-H{sub 2} conversion is still ongoing at this stage. By comparing with the GASS sample, we find that t{sub dep} anticorrelates with stellar surface density for disks, while spheroids show no such trend. Among early-type galaxies, t{sub dep} does not correlate with bulge-to-disk ratio; instead, the gas distribution may determine the star formation efficiency. Finally, the weak connection between galaxies’ specific star formation rates and M{sub H I}/M{sub ∗} contrasts with the well-known correlation between M{sub H I}/M{sub ∗} and color. We show that dust extinction can explain the H i–color trend, which may arise from the relationship between M{sub ∗}, M{sub H I}, and metallicity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

A lower bound on the number of cosmic ray events required to measure source catalogue correlations

Recent analyses of cosmic ray arrival directions have resulted in evidence for a positive correlation with active galactic nuclei positions that has weak significance against an isotropic source distribution. In this paper, we explore the sample size needed to measure a highly statistically significant correlation to a parent source catalogue. We compare several scenarios for the directional scattering of ultra-high energy cosmic rays given our current knowledge of the galactic and intergalactic magnetic fields. We find significant correlations are possible for a sample of >1000 cosmic ray protons with energies above 60 EeV.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

DCTRGAN: improving the precision of generative models with reweighting

Significant advances in deep learning have led to more widely used and precise neural network-based generative models such as Generative Adversarial Networks (Gans). We introduce a post-hoc correction to deep generative models to further improve their fidelity, based on the Deep neural networks using the Classification for Tuning and Reweighting (Dctr) protocol. The correction takes the form of a reweighting function that can be applied to generated examples when making predictions from the simulation. We illustrate this approach using Gans trained on standard multimodal probability densities as well as calorimeter simulations from high energy physics. We show that the weighted Gan examples significantly improve the accuracy of the generated samples without a large loss in statistical power. This approach could be applied to any generative model and is a promising refinement method for high energy physics applications and beyond.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

A composite likelihood approach for inference under photometric redshift uncertainty

ABSTRACT Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.

(cosmology:) large-scale structure of Universe↗
DOE OSTI2017

Optimized clustering estimators for BAO measurements accounting for significant redshift uncertainty

Here, we determine an optimized clustering statistic to be used for galaxy samples with significant redshift uncertainty, such as those that rely on photometric redshifts. To do so, we study the baryon acoustic oscillation (BAO) information content as a function of the orientation of galaxy clustering modes with respect to their angle to the line-of-sight (LOS). The clustering along the LOS, as observed in a redshift-space with significant redshift uncertainty, has contributions from clustering modes with a range of orientations with respect to the true LOS. For redshift uncertainty $\sigma_z \geq 0.02(1+z)$ we find that while the BAO information is confined to transverse clustering modes in the true space, it is spread nearly evenly in the observed space. Thus, measuring clustering in terms of the projected separation (regardless of the LOS) is an efficient and nearly lossless compression of the signal for $\sigma_z \geq 0.02(1+z)$. For reduced redshift uncertainty, a more careful consideration is required. We then use more than 1700 realizations (combining two separate sets) of galaxy simulations mimicking the Dark Energy Survey Year 1 sample to validate our analytic results and optimized analysis procedure. We find that using the correlation function binned in projected separation, we can achieve uncertainties that are within 10 per cent of those predicted by Fisher matrix forecasts. We predict that DES Y1 should achieve a 5 per cent distance measurement using our optimized methods. We expect the results presented here to be important for any future BAO measurements made using photometric redshift data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

First-principles calculation of the configurational energy density of states for a solid-state ion conductor with a variant of the Wang and Landau algorithm

In this work, a variant of the Wang and Landau algorithm for calculation of the configurational energy density of states is proposed. The algorithm was developed for the purpose of using first-principles simulations, such as density functional theory, to calculate the partition function of disordered sublattices in crystal materials. The expensive calculations of first-principles methods make a parallel algorithm necessary for a practical computation of the configurational energy density of states within a supercell approximation of a solid-state material. The algorithm developed in this work is tested with the two-dimensional (2d) Ising model to bench mark the algorithm and to help provide insight for implementation to a materials science application. Tests with the 2d Ising model revealed that the algorithm has good performance compared to the original Wang and Landau algorithm and the 1/$\textit{t}$ algorithm, in particular the short iteration performance. Further, a proof of convergence is presented within an adiabatic assumption, and the analysis is able to correctly predict the time dependence of the modification factor to the density of states. The algorithm was then applied to the lithium and lanthanum sublattice of the solid-state lithium ion conductor Li 0.5 La 0.5 TiO 3 . This was done to help understand the disordered nature of the lithium and lanthanum. The results find, overall, that the algorithm performs very well for the 2d Ising model and that the results for Li 0.5 La 0.5 TiO 3 are consistent with experiment while providing additional insight into the lithium and lanthanum ordering in the material. The primary result is that the lithium and lanthanum become more mixed between layers along the c axis for increasing temperature. In part, the simulation of the disordered Li 0.5 La 0.5 TiO 3 system serves as a benchmark for what size systems are currently and in the near future practical to calculate with density functional theory methods.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Nano-XRF Analysis of Metal Impurities Distribution at PL Active Grain Boundaries During mc-Silicon Solar Cell Processing

Metal impurities are known to hinder the performance of commercial Si-based solar cells by inducing bulk recombination, increasing leakage current, and causing direct shunting. Recently, a set of photoluminescence (PL) images of neighboring multicrystalline silicon wafers taken from a cell production line at different processing stages has been acquired. Both band-to-band PL and sub-bandgap PL (subPL) images showed various regions with different PL signal intensity. Interestingly, in several of these regions a reversal of the subPL intensity was observed right after the deposition of the antireflective coating. In this paper, we present the results of the synchrotron-based nano-X-ray fluorescence imaging performed in areas characterized by the subPL reversal to evaluate the possible role of metal decoration in this uncommon behavior. Furthermore, the acquisition of a statistically meaningful set of data for samples taken at different stages of the solar cell manufacturing allows us to shine a light on the precipitation and rediffusion mechanisms of metal impurities at these grain boundaries.

14 SOLAR ENERGY↗
DOE OSTI2020

TuckerMPI: A Parallel C++/MPI Software Package for Large-scale Data Compression via the Tucker Tensor Decomposition

With this study, our goal is compression of massive-scale grid-structured data, such as the multi-terabyte output of a high-fidelity computational simulation. For such data sets, we have developed a new software package called TuckerMPI, a parallel C++/MPI software package for compressing distributed data. The approach is based on treating the data as a tensor, i.e., a multidimensional array, and computing its truncated Tucker decomposition, a higher-order analogue to the truncated singular value decomposition of a matrix. The result is a low-rank approximation of the original tensor-structured data. Compression efficiency is achieved by detecting latent global structure within the data, which we contrast to most compression methods that are focused on local structure. In this work, we describe TuckerMPI, our implementation of the truncated Tucker decomposition, including details of the data distribution and in-memory layouts, the parallel and serial implementations of the key kernels, and analysis of the storage, communication, and computational costs. We test the software on 4.5 and 6.7 terabyte data sets distributed across 100 s of nodes (1,000 s of MPI processes), achieving compression ratios between 100 and 200,000×, which equates to 99--99.999% compression (depending on the desired accuracy) in substantially less time than it would take to even read the same dataset from a parallel file system. Moreover, we show that our method also allows for reconstruction of partial or down-sampled data on a single node, without a parallel computer so long as the reconstructed portion is small enough to fit on a single machine, e.g., in the instance of reconstructing/visualizing a single down-sampled time step or computing summary statistics. The code is available at https://gitlab.com/tensors/TuckerMPI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Polynomial Chaos–Based Bayesian Inference of K -Profile Parameterization in a General Circulation Model of the Tropical Pacific

The authors present a polynomial chaos (PC)–based Bayesian inference method for quantifying the uncertainties of the K-profile parameterization (KPP) within the MIT general circulation model (MITgcm) of the tropical Pacific. The inference of the uncertain parameters is based on a Markov chain Monte Carlo (MCMC) scheme that utilizes a newly formulated test statistic taking into account the different components representing the structures of turbulent mixing on both daily and seasonal time scales in addition to the data quality, and filters for the effects of parameter perturbations over those as a result of changes in the wind. To avoid the prohibitive computational cost of integrating the MITgcm model at each MCMC iteration, a surrogate model for the test statistic using the PC method is built. Because of the noise in the model predictions, a basis-pursuit-denoising (BPDN) compressed sensing approach is employed to determine the PC coefficients of a representative surrogate model. The PC surrogate is then used to evaluate the test statistic in the MCMC step for sampling the posterior of the uncertain parameters. Results of the posteriors indicate good agreement with the default values for two parameters of the KPP model, namely the critical bulk and gradient Richardson numbers; while the posteriors of the remaining parameters were barely informative.

54 ENVIRONMENTAL SCIENCES↗