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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 OSTI2019

Cosmic distance inference from purely geometric BAO methods: Linear point standard ruler and correlation function model fitting

Leveraging the baryon acoustic oscillations (BAO) feature present in clustering 2-point statistics, we aim to measure cosmological distances independently of the underlying background cosmological model. However this inference is complicated by late-time nonlinearities that introduce model and tracer dependencies in the clustering correlation function and power spectrum, which must be properly accounted for. With this in mind, we introduce the “purely geometric-BAO,” which provides a rigorous tool to measure cosmological distances without assuming a specific background cosmology. We focus on the 2-point clustering correlation function monopole, and show how to implement such an inference scheme employing two different methodologies: the linear point standard ruler (LP) and correlation-function model-fitting (CF-MF). For the first time we demonstrate how, by means of the CF-MF, we can measure very precisely the sound-horizon/isotropic-volume-distance ratio, r d /DV(z¯), while correctly propagating all the uncertainties. Using synthetic data, we compare the outcomes of the two methodologies, and find that the LP provides up to 50% more precise measurements than the CF-MF. Finally, we test a procedure widely employed in BAO analyses: fitting the 2-point function while fixing the cosmological and the non-linear-damping parameters at fiducial values. We find that this underestimates the distance errors by nearly a factor of 2. Furthermore, we recommend that this practice be reconsidered, whether for parameter determination or model selection.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Radiation-induced Swelling and Microcracking in SiC Cladding for LWRs

After the Fukushima accident in 2011, SiC has attracted considerable attention as a potential material for fuel cladding that can offer increased accident tolerance in light water reactors (LWRs) compared to zircaloy cladding. Existing measurements on nuclear grade SiC/SiC composites made of high-purity, stoichiometric SiC have already shown very good radiation tolerance in various harsh environments. However, there are a number of challenges that need to be addressed before SiC composites can be successfully used in LWR technologies. One of them is irradiation-induced swelling, which can lead to development of significant stresses in the matrix, followed by microcracking and subsequent release of fission products. Models of irradiation-induced swelling had been largely empirical and therefore they cannot be used to predict the microstructural dependence of microcracking. Existing models are also limited by the lack of understanding of defects that form due to irradiation in the range of temperatures relevant to fuel cladding in LWRs (<1000°C). Many of the defects in this regime of temperature are too small to be detected with traditional transmission electron microscopy (TEM) techniques. In fact the defects observed in traditional TEM account only for 10-45% of the swelling measured in irradiated SiC. Here, we developed a multi-scale simulation methodology combined with state of the art experimental imaging techniques based on aberration corrected scanning transmission electron microscopy (STEM) to provide a scientific basis for predictive models of radiation swelling and microcracking in SiC. Specifically, we have developed a phase field fracture model that predicts microcrack initiation and growth in polycrystalline SiC. The model accounts for single crystal anisotropy, including anisotropy in the elastic constants and anisotropic surface energy. It also considers the reduced fracture strength of grain boundaries. The model was implemented using the MARMOT mesoscale nuclear materials tool. We have parameterized the model for SiC using molecular dynamics simulation results. The model has been verified using single crystal, bicrystal, and polycrystal examples. Swelling model has also been developed based on cluster dynamics simulations of defect evolution in SiC. We found that point defects and small defect clusters have a significant contribution to swelling. This finding is important because point defects and very small defect clusters are not visible in experiments (e.g., in TEM). Combined modeling and experiments can bring critical insights into how swelling depends on irradiation conditions and the microstructure. In situ nanoindentation tests and ex situ nanoindentation tests were performed on assynthesized 3C-SiC and 3C-SiC irradiated under different radiation conditions. Without radiation, bend contour movement and residual bend contours after indentation demonstrate some room temperature plastic flow and residual plastic deformation. Plastic deformation recovery is also observed on unirradiated samples during unloading. Radiation-induced embrittlement is demonstrated by differences in crack geometry in in situ tests, and is consistent with ex situ test results. Failure by single, straight, cracks indicates decreased fracture toughness due to irradiation in 3C-SiC. Future work includes coupling of the swelling and fracture models and development of a master equation that can be used in the BISON code.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1985

The optical structure of X-ray globular clusters

CTIO 4-m prime-focus plates of eight globular clusters containing bright X-ray sources have been obtained. Plates in several colors (principally U and B) of each cluster have been digitized. A maximum symmetry method for determining the center of a globular cluster image is described and applied to each cluster in order to determine the cluster center with an accuracy of above 1.0 arc sec. Surface brightness profiles have been derived for seven clusters and fitted with King (1966, 1980) models to yield core radii within about 10 percent. These determinations of the cluster centers and core radii are sufficiently accurate to allow Grindlay et al. (1984) to statistically measure the mass of the globular cluster X-ray sources. Two clusters (NGC 6624 and M15) show significant excesses of light in their cores over the best-fit King model, in agreement with the results of Djorgovski and King (1984). The significance of these excesses, as well as the lack of any dependence of globular cluster structural parameters on color, is discussed.

Hertz, P.↗
NASA NTRS1993

Globular cluster systems as clues to galaxy evolution

We investigate the properties of systems of globular clusters in light of the hypothesis that galaxy mergers play a major role in galaxy evolution. In a previous paper, we presented a model in which the formation of globular clusters occurs during galaxy interactions and mergers. We discussed several predictions of the model, including the existence of young globular clusters in currently merging galaxies and the presence of two or more metallicity peaks in the globular clusters systems of normal elliptical galaxies. Here, we present recent observational evidence which supports both of these predictions and suggests that mergers may have a significant influence on the formation and evolution of galaxies and their globular clusters.

Zepf, Stephen E.↗
DOE OSTI2016

Lieb-Liniger-like model of quantum solvation in CO-{sup 4}He{sub N} clusters

Small {sup 4}He clusters doped with various molecules allow for the study of “quantum solvation” as a function of cluster size. A peculiarity of quantum solvation is that, as the number of {sup 4}He atoms is increased from N = 1, the solvent appears to decouple from the molecule which, in turn, appears to undergo free rotation. This is generally taken to signify the onset of “microscopic superfluidity.” Currently, little is known about the quantum mechanics of the decoupling mechanism, mainly because the system is a quantum (N + 1)-body problem in three dimensions which makes computations difficult. Here, a one-dimensional model is studied in which the {sup 4}He atoms are confined to revolve on a ring and encircle a rotating CO molecule. The Lanczos algorithm is used to investigate the eigenvalue spectrum as the number of {sup 4}He atoms is varied. Substantial solvent decoupling is observed for as few as N = 5 {sup 4}He atoms. Examination of the Hamiltonian matrix, which has an almost block diagonal structure, reveals increasingly weak inter-block (solvent-molecule) coupling as the number of {sup 4}He atoms is increased. In the absence of a dopant molecule the system is similar to a Lieb-Liniger (LL) gas and we find a relatively rapid transition to the LL limit as N is increased. In essence, the molecule initially—for very small N—provides a central, if relatively weak, attraction to organize the cluster; as more {sup 4}He atoms are added, the repulsive interactions between the identical bosons start to dominate as the solvation ring (shell) becomes more crowded which causes the molecule to start to decouple. For low N, the molecule pins the atoms in place relative to itself; as N increases the atom-atom repulsion starts to dominate the Hamiltonian and the molecule decouples. We conclude that, while the notion of superfluidity is a useful and correct description of the decoupling process, a molecular viewpoint provides complementary insights into the quantum mechanism of the transition from a molecular cluster to a quantum solvated molecule.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Radiation damage effects in beryllium for next generation neutrino beam targetry (Final Technical Report)

Current and future high-power accelerators put severe requirements on materials used for target and beam windows and target facilities have been recognized as a critical challenge in development of future particle accelerators. In accelerators, window and target materials are exposed to extreme conditions, which include bombardment with very high energy protons (1- 100 GeV) and thermomechanical shock waves. Radiation can cause direct damage in the material, and it leads to production of transmutation products (especially helium), both phenomena having a potential adverse effect on the stability and durability of the target/window material. At high enough temperatures, He can aggregate to form gas bubbles, which in turn cause significant dimensional changes (swelling), enable easy crack propagation, and eventually cause failure by fracture. On the other hand, if the temperature is too low, radiation damage accumulates in the form of internal defects (e.g., dislocations), leading to hardening and a decreased ductility of the material. In this project, we will focus on beryllium since it is considered to be one of the candidate materials for beam windows and targets in the next-generation proton accelerators, e.g., the Long Baseline Neutrino Facility (LBNF). Radiation effects in Be have been studied in the context of nuclear fusion reactor applications. However, key differences exist between reactor and accelerator conditions, including neutron vs. proton irradiation, continuous vs. pulsed beam flux, much higher energies of bombarding particles in accelerators, and higher operating temperatures for typical reactors. For example, the impact of beam pulsing on the radiation damage and the He bubble kinetics is largely unknown. While results obtained on Be from fusion research might not be directly transferrable to understanding target materials, there is an opportunity to bring state-of-the-art tools from materials research in nuclear reactors to aid design of target and beam window materials in high-power accelerators. To this end, the overarching goal of this project are to develop an experimentally-validated computational framework capable of predicting radiation damage evolution in beryllium relevant to beam window and target conditions, focusing on He bubble formation and growth as a function of irradiation temperature. Our model will be based on the cluster dynamics formalism, where size distribution of defects and He bubbles is simulated as a function of time, temperature, and radiation dose. Parameters for the model will be taken from published experiments and from high-fidelity atomistic simulations proposed in this project. In addition, we will carry out a series of targeted ex-situ and in-situ dual-beam experiments using low-energy protons to provide critical data for validation of the model on the effects of radiation on He clustering, He bubble distribution, and dislocation loop density/size in proton irradiated Be.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Clusterization and phase-transitions in atomic nuclei

The question of phases and phase-transitions of cluster states is reviewed. First some features of the algebraic models are recalled. Then we address the question, what the experimental spectrum of {sup 20}Ne suggests on the phase of the {sup 16}O+{alpha} system.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Accelerated, scalable and reproducible AI-driven gravitational wave detection

The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics. Here we develop a workflow that connects the Data and Learning Hub for Science, a repository for publishing AI models, with the Hardware-Accelerated Learning (HAL) cluster, using funcX as a universal distributed computing service. Using this workflow, an ensemble of four openly available AI models can be run on HAL to process an entire month's worth (August 2017) of advanced Laser Interferometer Gravitational-Wave Observatory data in just seven minutes, identifying all four binary black hole mergers previously identified in this dataset and reporting no misclassifications. This approach combines advances in AI, distributed computing and scientific data infrastructure to open new pathways to conduct reproducible, accelerated, data-driven discovery. By combining a repository for artificial intelligence models and a supercomputing cluster, an entire month's worth of advanced LIGO data is analysed in just 7 min, finding all binary black hole mergers previously identified in this dataset and reporting no misclassifications.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Improving Cosmological Constraints from Galaxy Cluster Number Counts with CMB-cluster-lensing Data: Results from the SPT-SZ Survey and Forecasts for the Future

Abstract We show the improvement to cosmological constraints from galaxy cluster surveys with the addition of cosmic microwave background (CMB)-cluster lensing data. We explore the cosmological implications of adding mass information from the 3.1 σ detection of gravitational lensing of the CMB by galaxy clusters to the Sunyaev–Zel’dovich (SZ) selected galaxy cluster sample from the 2500 deg 2 SPT-SZ survey and targeted optical and X-ray follow-up data. In the ΛCDM model, the combination of the cluster sample with the Planck power spectrum measurements prefers σ 8 Ω m / 0.3 0.5 = 0.831 ± 0.020 . Adding the cluster data reduces the uncertainty on this quantity by a factor of 1.4, which is unchanged whether the 3.1 σ CMB-cluster lensing measurement is included or not. We then forecast the impact of CMB-cluster lensing measurements with future cluster catalogs. Adding CMB-cluster lensing measurements to the SZ cluster catalog of the ongoing SPT-3G survey is expected to improve the expected constraint on the dark energy equation of state w by a factor of 1.3 to σ ( w ) = 0.19. We find the largest improvements from CMB-cluster lensing measurements to be for σ 8 , where adding CMB-cluster lensing data to the cluster number counts reduces the expected uncertainty on σ 8 by respective factors of 2.4 and 3.6 for SPT-3G and CMB-S4.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Constraining Galaxy-Halo connection using machine learning

We investigate the potential of machine learning (ML) methods to model small-scale galaxy clustering for constraining Halo Occupation Distribution (HOD) parameters. Our analysis reveals that while many ML algorithms report good statistical fits, they often yield likelihood contours that are significantly biased in both mean values and variances relative to the true model parameters. This highlights the importance of careful data processing and algorithm selection in ML applications for galaxy clustering, as even seemingly robust methods can lead to biased results if not applied correctly. ML tools offer a promising approach to exploring the HOD parameter space with significantly reduced computational costs compared to traditional brute-force methods if their robustness is established. Using our ANN-based pipeline, we successfully recreate some standard results from recent literature. Properly restricting the HOD parameter space, transforming the training data, and carefully selecting ML algorithms are essential for achieving unbiased and robust predictions. Among the methods tested, artificial neural networks (ANNs) outperform random forests (RF) and ridge regression in predicting clustering statistics, when the HOD prior space is appropriately restricted. We demonstrate these findings using the projected two-point correlation function (w p (r p )), angular multipoles of the correlation function (ξ ℓ (r)), and the void probability function (VPF) of Luminous Red Galaxies from Dark Energy Spectroscopic Instrument mocks. Our results show that while combining w p (r p ) and VPF improves parameter constraints, adding the multipoles ξ 0 , ξ 2 , and ξ 4 to w p (r p ) does not significantly improve the constraints.

cosmology↗
DOE OSTI2013

A consistent approach to falsifying ΛCDM with rare galaxy clusters

We consider methods with which to answer the question ''is any observed galaxy cluster too unusual for ΛCDM?'' After emphasising that many previous attempts to answer this question will overestimate the confidence level at which ΛCDM can be ruled out, we outline a consistent approach to these rare clusters, which allows the question to be answered. We define three statistical measures, each of which are sensitive to changes in cluster populations arising from different modifications to the cosmological model. We also use these properties to define the ''equivalent mass at redshift zero'' for a cluster — the mass of an equally unusual cluster today. This quantity is independent of the observational survey in which the cluster was found, which makes it an ideal proxy for ranking the relative unusualness of clusters detected by different surveys. These methods are then used on a comprehensive sample of observed galaxy clusters and we confirm that all are less than 2σ deviations from the ΛCDM expectation. Whereas we have only applied our method to galaxy clusters, it is applicable to any isolated, collapsed, halo. As motivation for future surveys, we also calculate where in the mass redshift plane the rarest halo is most likely to be found, giving information as to which objects might be the most fruitful in the search for new physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

Molecular arrangement in water: random but not quite.

Water defines life on Earth from the cellular to the terrestrial level. Yet the molecular level arrangement in water is not well understood, posing problems in comprehending its very special chemical, physical and biological properties. Here we present high-resolution x-ray diffraction data for water clearly showing that its molecular arrangement exhibits specific correlations that are consistent with the presence of rings of H{sub 2}O molecules linked together by hydrogen bonds into tetrahedral-like units from a continuous network. This level of molecular arrangement complexity is beyond what a simple 'two-state' model of water (Bernal and Fowler 1933 J. Chem. Phys.1 515-48) could explain. It may not be explained by the recently put forward 'chains-clusters of completely uncorrelated molecules' model (Wernet et al 2004 Science 304 995-9) either. Rather it indicates that water is homogeneous down to the molecular level where different water molecules form tetrahedral units of different perfection and/or participate in rings of different sizes, thus experiencing different local environments. The local diversity of this tetrahedral network coupled to the flexibility of the hydrogen bonds that hold it together may explain well the rich phase diagram of water and why it responds non-uniformly to external stimuli such as, for example, temperature and pressure.

08 HYDROGEN↗
NASA NTRS1993

Numerical Model for Isothermal and Non-Isothermal Crystallization of Liquids and Glasses

A new numerical model of isothermal and non-isothermal first order phase transformations, such as the crystallization of liquids and glasses, is presented. This model computes directly the volume fraction transformed, taking into account time-dependent nucleation rates and cluster-size-dependent growth velocities. The model is applied to the crystallization of lithium disilicate glass, using the appropriate kinetic and thermodynamic parameters. The model is used (1) to determine the validity of common methods for computing the volume fraction transformed as a function of time in isothermal experiments when a time-dependent nucleation rate is expected, (2) to simulate non-isothermal differential scanning calorimetry (DSC) studies of glass devitrification as a function of scan rate, and (3) to compute the effect of preannealing on the DSC peak parameters. A novel behavior of the nucleation rate with scan rate is predicted, arising because the relaxation of the cluster distribution cannot be described by a single relaxation time. Comparisons of the calculations with experimental data on this glass demonstrate the validity of the model.

Kelton, K. F.↗
DOE OSTI2020

Three dimensional cluster analysis for atom probe tomography using Ripley’s K-function and machine learning

The size and structure of spatial molecular and atomic clustering can significantly impact material properties and is therefore important to accurately quantify. Ripley’s K-function (K(r)), a measure of spatial correlation, can be used to perform such quantification when the material system of interest can be represented as a marked point pattern. This work demonstrates how machine learning models based on K (r)-derived metrics can accurately estimate cluster size and intra-cluster density in simulated three dimensional (3D) point patterns containing spherical clusters of varying size; over 90% of model estimates for cluster size and intra-cluster density fall within 11% and 18% error of the true values, respectively. These K (r)-based size and density estimates are then applied to an experimental APT reconstruction to characterize MgZn clusters in a 7000 series aluminum alloy. Here we find that the estimates are more accurate, consistent, and robust to user interaction than estimates from the popular maximum separation algorithm. Using K (r) and machine learning to measure clustering is an accurate and repeatable way to quantify this important material attribute.

36 MATERIALS SCIENCE↗
DOE OSTI2014

The dark side of galaxy colour: evidence from new SDSS measurements of galaxy clustering and lensing

The age matching model has recently been shown to predict correctly the luminosity L and g-r color of galaxies residing within dark matter halos. The central tenet of the model is intuitive: older halos tend to host galaxies with older stellar populations. In this paper, we demonstrate that age matching also correctly predicts the g-r color trends exhibited in a wide variety of statistics of the galaxy distribution for stellar mass M* threshold samples. In particular, we present new measurements of the galaxy two-point correlation function and the galaxy-galaxy lensing signal as a function of M* and g-r color from the Sloan Digital Sky Survey, and show that age matching exhibits remarkable agreement with these and other statistics of low-redshift galaxies. In so doing, we also demonstrate good agreement between the galaxy-galaxy lensing observed by SDSS and the signal predicted by abundance matching, a new success of this model. We describe how age matching is a specific example of a larger class of Conditional Abundance Matching models (CAM), a theoretical framework we introduce here for the first time. CAM provides a general formalism to study correlations at fixed mass between any galaxy property and any halo property. The striking success of our simple implementation of CAM provides compelling evidence that this technique has the potential to describe the same set of data as alternative models, but with a dramatic reduction in the required number of parameters. CAM achieves this reduction by exploiting the capability of contemporary N-body simulations to determine dark matter halo properties other than mass alone, which distinguishes our model from conventional approaches to the galaxy-halo connection.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data

Hyperspectral image analysis has benefited from an array of methods that take advantage of the increased spectral depth compared to multispectral sensors; however, the focus of these developments has been on supervised classification methods. Lack of a priori knowledge regarding land cover characteristics can make unsupervised classification methods preferable under certain circumstances. An unsupervised classification technique is presented in this paper that utilizes physically relevant basis functions to model the reflectance spectra. These fit parameters used to generate the basis functions allow clustering based on spectral characteristics rather than spectral channels and provide both noise and data reduction. Histogram splitting of the fit parameters is then used as a means of producing an unsupervised classification. Unlike current unsupervised classification techniques that rely primarily on Euclidian distance measures to determine similarity, the unsupervised classification technique uses the natural splitting of the fit parameters associated with the basis functions creating clusters that are similar in terms of physical parameters. The data set used in this work utilizes the publicly available data collected at Indian Pines, Indiana. This data set provides reference data allowing for comparisons of the efficacy of different unsupervised data analysis. The unsupervised histogram splitting technique presented in this paper is shown to be better than the standard unsupervised ISODATA clustering technique with an overall accuracy of 34.3/19.0% before merging and 40.9/39.2% after merging. Finally, this improvement is also seen as an improvement of kappa before/after merging of 24.8/30.5 for the histogram splitting technique compared to 15.8/28.5 for ISODATA.

58 GEOSCIENCES↗
NASA NTRS1978

Effect of grazing flow on the acoustic impedance of Helmholtz resonators consisting of single and clustered orifices

A semiempirical fluid mechanical model is presented which predicts impedance of a Helmholtz resonator consisting of a single cavity-backed orifice as a function of grazing flow speed, boundary-layer thickness, incident sound amplitude and frequency, and resonator geometry. The incident and cavity sound fields are connected in terms of an orifice discharge coefficient. The effect of multiple orifices was studied experimentally. Interaction between orifices is important only for orifices aligned parallel to the grazing flow. Resistance was virtually independent of both orifice relative spacing and number. Reactance was found to be quite dependent upon orifice spacing but insensitive to the number of orifices.

Hersh, A. S.↗