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DOE OSTI2020

Materials Data on CeSe2 by Materials Project

CeSe2 is High (Orthorhombic) Tridymite-like structured and crystallizes in the orthorhombic Fddd space group. The structure is three-dimensional. Ce4+ is bonded to four Se2- atoms to form corner-sharing CeSe4 tetrahedra. There are two shorter (2.71 Å) and two longer (2.72 Å) Ce–Se bond lengths. There are two inequivalent Se2- sites. In the first Se2- site, Se2- is bonded in a bent 150 degrees geometry to two equivalent Ce4+ atoms. In the second Se2- site, Se2- is bonded in a linear geometry to two equivalent Ce4+ atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on CrN2 by Materials Project

CrN2 is High (Orthorhombic) Tridymite-like structured and crystallizes in the triclinic P1 space group. The structure is three-dimensional. there are twenty-four inequivalent Cr6+ sites. In the first Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.68–1.81 Å. In the second Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.68–1.81 Å. In the third Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.68–1.80 Å. In the fourth Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.66–1.78 Å. In the fifth Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.65–1.78 Å. In the sixth Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.65–1.78 Å. In the seventh Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.66–1.78 Å. In the eighth Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.68–1.81 Å. In the ninth Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.65–1.78 Å. In the tenth Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.68–1.81 Å. In the eleventh Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.66–1.78 Å. In the twelfth Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.68–1.81 Å. In the thirteenth Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.65–1.78 Å. In the fourteenth Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.66–1.78 Å. In the fifteenth Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.66–1.78 Å. In the sixteenth Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.68–1.81 Å. In the seventeenth Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.68–1.80 Å. In the eighteenth Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.68–1.81 Å. In the nineteenth Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.65–1.78 Å. In the twentieth Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.68–1.80 Å. In the twenty-first Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.66–1.78 Å. In the twenty-second Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.68–1.81 Å. In the twenty-third Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.65–1.78 Å. In the twenty-fourth Cr6+ site, Cr6+ is bonded to four N3- atoms to form corner-sharing CrN4 tetrahedra. There are a spread of Cr–N bond distances ranging from 1.68–1.81 Å. There are forty-eight inequivalent N3- sites. In the first N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the second N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the third N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the fourth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the fifth N3- site, N3- is bonded in a bent 150 degrees geometry to two Cr6+ atoms. In the sixth N3- site, N3- is bonded in a bent 150 degrees geometry to two Cr6+ atoms. In the seventh N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the eighth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the ninth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the tenth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the eleventh N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the twelfth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the thirteenth N3- site, N3- is bonded in a bent 150 degrees geometry to two Cr6+ atoms. In the fourteenth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the fifteenth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the sixteenth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the seventeenth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the eighteenth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the nineteenth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the twentieth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the twenty-first N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the twenty-second N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the twenty-third N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the twenty-fourth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the twenty-fifth N3- site, N3- is bonded in a bent 150 degrees geometry to two Cr6+ atoms. In the twenty-sixth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the twenty-seventh N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the twenty-eighth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the twenty-ninth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the thirtieth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the thirty-first N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the thirty-second N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the thirty-third N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the thirty-fourth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the thirty-fifth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the thirty-sixth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the thirty-seventh N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the thirty-eighth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the thirty-ninth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the fortieth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the forty-first N3- site, N3- is bonded in a bent 150 degrees geometry to two Cr6+ atoms. In the forty-second N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the forty-third N3- site, N3- is bonded in a bent 150 degrees geometry to two Cr6+ atoms. In the forty-fourth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the forty-fifth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the forty-sixth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the forty-seventh N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms. In the forty-eighth N3- site, N3- is bonded in a linear geometry to two Cr6+ atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on CeSe2 by Materials Project

CeSe2 is high (beta) Cristobalite structured and crystallizes in the cubic Fd-3m space group. The structure is three-dimensional. Ce4+ is bonded to four equivalent Se2- atoms to form corner-sharing CeSe4 tetrahedra. All Ce–Se bond lengths are 2.71 Å. Se2- is bonded in a linear geometry to two equivalent Ce4+ atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on HgN2 by Materials Project

HgN2 is high (beta) Cristobalite structured and crystallizes in the cubic Fd-3m space group. The structure is three-dimensional. Hg2+ is bonded to four equivalent N1- atoms to form corner-sharing HgN4 tetrahedra. All Hg–N bond lengths are 2.21 Å. N1- is bonded in a linear geometry to two equivalent Hg2+ atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on Si2O by Materials Project

SiOSi is high (beta) Cristobalite structured and crystallizes in the cubic Fd-3m space group. The structure is three-dimensional. Si is bonded in a linear geometry to two equivalent O atoms. Both Si–O bond lengths are 2.03 Å. O is bonded to four equivalent Si atoms to form corner-sharing OSi4 tetrahedra.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on CeSe2 by Materials Project

CeSe2 is High (Orthorhombic) Tridymite-like structured and crystallizes in the orthorhombic Pccn space group. The structure is three-dimensional. Ce4+ is bonded to four Se2- atoms to form corner-sharing CeSe4 tetrahedra. All Ce–Se bond lengths are 2.71 Å. There are three inequivalent Se2- sites. In the first Se2- site, Se2- is bonded in a distorted bent 150 degrees geometry to two equivalent Ce4+ atoms. In the second Se2- site, Se2- is bonded in a linear geometry to two equivalent Ce4+ atoms. In the third Se2- site, Se2- is bonded in a linear geometry to two equivalent Ce4+ atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Phase retrieval for Bragg coherent diffraction imaging at high x-ray energies

Coherent x-ray beams with energies ≥ 50 keV can potentially enable three-dimensional imaging of atomic lattice distortion fields within individual crystallites in bulk polycrystalline materials through Bragg coherent diffraction imaging (BCDI). However, the undersampling of the diffraction signal due to Fourier-space compression at high x-ray energies renders conventional phase-retrieval algorithms unsuitable for three-dimensional reconstruction. To address this problem, we utilize a phase-retrieval method with a Fourier constraint specifically tailored for undersampled diffraction data measured with coarse-pitched detector pixels that bin the underlying signal. With our approach, we show that it is possible to reconstruct three-dimensional strained crystallites from an undersampled Bragg diffraction data set subject to pixel-area integration without having to physically upsample the diffraction signal. Using simulations and experimental results, we demonstrate that explicit modeling of Fourier-space compression during phase retrieval provides a viable means by which to invert high-energy BCDI data, which is otherwise intractable.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

Load response of a floating wind turbine to turbulent atmospheric flow

The two turbulence-generation models [Kaimal Spectrum Exponential Coherence (KSEC) and Mann] specified in the international standard for wind turbine design assume neutral atmospheric conditions and are based on statistical and spectral methods. Mainly due to the lack of physics, the flow fields simulated with these models ultimately differ in their underlying structure, especially in terms of the spatial coherence of longitudinal velocity perturbations. While this may not be critical for smaller wind turbine rotors, it becomes important when rotor sizes increase. Furthermore, it might be especially important in the context of floating technologies as they are more sensitive to large turbulent coherent structures. Previous work found that these differences between KSEC and Mann can propagate to loads predictions and thereby affect the design space of the entire wind turbine system. It is therefore crucial to determine in which ways these two models are underperforming. Up until now, validation of these models had only been done in the vertical direction because it is extremely difficult to obtain atmospheric turbulence measurements separated laterally, and sampled at heights relevant to wind energy. In this work, we address the lack of measurements by using high-fidelity, high-resolution simulation data as a reference. We perform hour-long, large-eddy simulations of turbulent velocity fields that are stability-dependent and contain three-dimensional coherent structures. These flow fields are then used to investigate which stochastic model is a better predictor of loads on a realistic spar-system floating offshore wind turbine, and to quantify how the assumption of neutral stratification propagates to short-term load estimates. Both stochastic turbulence models are found to overpredict fatigue loading in high-wind scenarios (in some cases, by more than 25%) and underpredict it when the wind speed is low (by as much as 20%). The KSEC model matches the high-fidelity flow fields more closely than Mann at high wind speeds, and the opposite is true at low wind speeds. Finally, turbine loading is found to be sensitive to atmospheric stability even when the turbulence intensity remains fairly constant. This sensitivity is most pronounced at low wind speeds, when fatigue load estimates on the spar system can differ by 40%.

17 WIND ENERGY↗
NASA NTRS1995

On-line estimation of error covariance parameters for atmospheric data assimilation

A simple scheme is presented for on-line estimation of covariance parameters in statistical data assimilation systems. The scheme is based on a maximum-likelihood approach in which estimates are produced on the basis of a single batch of simultaneous observations. Simple-sample covariance estimation is reasonable as long as the number of available observations exceeds the number of tunable parameters by two or three orders of magnitude. Not much is known at present about model error associated with actual forecast systems. Our scheme can be used to estimate some important statistical model error parameters such as regionally averaged variances or characteristic correlation length scales. The advantage of the single-sample approach is that it does not rely on any assumptions about the temporal behavior of the covariance parameters: time-dependent parameter estimates can be continuously adjusted on the basis of current observations. This is of practical importance since it is likely to be the case that both model error and observation error strongly depend on the actual state of the atmosphere. The single-sample estimation scheme can be incorporated into any four-dimensional statistical data assimilation system that involves explicit calculation of forecast error covariances, including optimal interpolation (OI) and the simplified Kalman filter (SKF). The computational cost of the scheme is high but not prohibitive; on-line estimation of one or two covariance parameters in each analysis box of an operational bozed-OI system is currently feasible. A number of numerical experiments performed with an adaptive SKF and an adaptive version of OI, using a linear two-dimensional shallow-water model and artificially generated model error are described. The performance of the nonadaptive versions of these methods turns out to depend rather strongly on correct specification of model error parameters. These parameters are estimated under a variety of conditions, including uniformly distributed model error and time-dependent model error statistics.

Dee, Dick P.↗
DOE OSTI2025

Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.

Chemistry↗
DOE OSTI2026

Biopolymer-Templated Titania Film Formation for Nanostructured Coatings Revealed by Machine Learning-Supported Time-Resolved Analysis

This study presents a machine learning approach to derive the film formation of biopolymer-templated titania nanostructures during spray deposition, in combination with in situ grazing-incidence small-angle X-ray scattering (GISAXS). A neural network trained on synthetic GISAXS data directly predicts domain-size distributions from experimental two-dimensional scattering patterns, capturing the full kinetics of nanostructure evolution with high temporal resolution. The predictions reveal hierarchical size distributions and periodic growth features, consistent with layer-by-layer spray deposition and validated by complementary scanning electron microscopy (SEM) imaging. Quantitative comparison with conventional parametric GISAXS fits shows good qualitative agreement, with systematic differences explained by domain-shape assumptions and resolved by applying a geometric scaling factor. Simulated SEM-like surfaces derived from neural network outputs reproduce the porous, foam-like nanoscale morphology observed experimentally, reinforcing the method’s credibility. This integrated approach enables real-time, nondestructive, statistically averaged monitoring of bulk nanostructure development in functional coatings, offering a scalable methodology to accelerate the characterization and process control of sustainably manufactured nanostructured titania films for energy-related applications such as photocatalysis and photovoltaics.

Heger, JulianEliah↗
DOE OSTI2013

Improved laser-to-proton conversion efficiency in isolated reduced mass targets

We present experimental results of laser-to-proton conversion efficiency as a function of lateral confinement of the refluxing electrons. Experiments were carried out using the T-Cubed laser at the Center for Ultrafast Optical Science, University of Michigan. We demonstrate that the laser-to-proton conversion efficiency increases by 50% with increased confinement of the target from surroundings with respect to a flat target of the same thickness. Three-dimensional hybrid particle-in-cell simulations using LSP code agree with the experimental data. The adopted target design is suitable for high repetition rate operation as well as for Inertial Confinement Fusion applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Molecular hydrodynamics: Vortex formation and sound wave propagation

In the present study, quantitative feasibility tests of the hydrodynamic description of a two-dimensional fluid at the molecular level are performed, both with respect to length and time scales. Using high-resolution fluid velocity data obtained from extensive molecular dynamics simulations, we computed the transverse and longitudinal components of the velocity field by the Helmholtz decomposition and compared them with those obtained from the linearized Navier-Stokes (LNS) equations with time-dependent transport coefficients. By investigating the vortex dynamics and the sound wave propagation in terms of these field components, we confirm the validity of the LNS description for times comparable to or larger than several mean collision times. The LNS description still reproduces the transverse velocity field accurately at smaller times, but it fails to predict characteristic patterns of molecular origin visible in the longitudinal velocity field. Based on these observations, we validate the main assumptions of the mode-coupling approach. The assumption that the velocity autocorrelation function can be expressed in terms of the fluid velocity field and the tagged particle distribution is found to be remarkably accurate even for times comparable to or smaller than the mean collision time. This suggests that the hydrodynamic-mode description remains valid down to the molecular scale.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation

Many material and biological samples in scientific imaging are characterized by nonlocal repeating structures. These are studied using scanning electron microscopy and electron tomography. Sparse sampling of individual pixels in a two-dimensional image acquisition geometry, or sparse sampling of projection images with large tilt increments in a tomography experiment, can enable high speed data acquisition and minimize sample damage caused by the electron beam. In this paper, we present an algorithm for electron tomographic reconstruction and sparse image interpolation that exploits the nonlocal redundancy in images. We adapt a framework, termed plug-and-play priors, to solve these imaging problems in a regularized inversion setting. The power of the plug-and-play approach is that it allows a wide array of modern denoising algorithms to be used as a “prior model” for tomography and image interpolation. We also present sufficient mathematical conditions that ensure convergence of the plug-and-play approach, and we use these insights to design a new nonlocal means denoising algorithm. Finally, we demonstrate that the algorithm produces higher quality reconstructions on both simulated and real electron microscope data, along with improved convergence properties compared to other methods.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Accuracy of Some Approximate Gaussian Filters for the Navier--Stokes Equation in the Presence of Model Error

Bayesian state estimation of a dynamical system from a stream of noisy measurements is important in many geophysical and engineering applications where high dimensionality of the state space, sparse observations, and model error pose key challenges. In this work, three computationally feasible, approximate Gaussian data assimilation/filtering algorithms are considered in various regimes of turbulent 2D Navier--Stokes dynamics in the presence of model error. The first source of error arises from the necessary use of reduced models for the forward dynamics of the filters, while a particular type of representation error arises from the finite resolution of observations which mix up information about resolved and unresolved dynamics. Two stochastically parameterized filtering algorithms, referred to as cSPEKF and GCF, are compared with 3DVAR---a prototypical time-sequential algorithm known to be accurate for filtering dissipative systems for a suitably inflated “background” covariance. We provide the first evidence that the stochastically parameterized algorithms, which do not rely on detailed knowledge of the underlying dynamics and do not require covariance inflation, can compete with or outperform an optimally tuned 3DVAR algorithm, and they can overcome competing sources of error in a range of dynamical scenarios.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

High-resolution time-resolved experiments on mixing and entrainment of buoyant jets in stratified environments

Fluid jets interacting with a stratified layer play an important role in the safety of several reactor designs. In the containment of nuclear power plants, fluid jets dominate the transport and mixing of gaseous species and consequent hydrogen distribution in case of a severe accident. The mixing phenomena in the containment are driven by buoyant high-momentum injections (jets) and low momentum injection plumes. Mixing near the postulated break is initially dominated by high flow velocities. Plumes with moderate flow velocities are instead relevant in the break compartment during the long-term pressurization phase, or in any of the apertures between two connected compartments if the mass flows are sufficiently high and the density differences between efflux and ambient are sufficiently low. Phenomena of interest include free plumes (as produced by the efflux from the break compartment in a larger room or directly from a break flow), wall plumes (such those produced by low mass flows through inter-compartment apertures), and propagating stratification fronts in the ambient (for any stably stratified conditions). These phenomena have been highly ranked about nuclear reactor design, especially regarding of safety protocols. During a Pressurized Thermal Shock (PTS) scenario, the interaction between the cold ECCS injection plume and the stratified fluid present in the cold (or hot) leg is important in order to determine the temperature at the time-dependent temperature at the inlet of the reactor pressure vessel (RPV) and the potential to cause a thermal shock on the RPV wall. In sodium-cooled fast reactors (SFRs), core channels are typically hydro-dynamically isolated so that there exists a considerable temperature variation at the exit of adjacent fuel assemblies. All the above phenomena are characterized by the interaction of buoyant jets with the stratified flow. In stratified layers baroclinic forces create significant redistribution of turbulent kinetic energy and scales, which leads to anisotropy. This important physical phenomenon is highly three dimensional and is challenging to capture even with high-fidelity CFD simulations, due in part to lack of sufficiently resolved validation data. Furthermore, the experimental data available in the open literature do not feature the level of fidelity needed for an extensive validation of turbulence models in lower order CFD. To shed new lights into the crucial phenomena object of the present research project, it was proposed to conduct coordinated experiments and simulations at the University of Michigan and the George Washington University. The project has resulted in an experimental database of high-resolution time-resolved measurements of jets in uniform and stratified environments. The novel experimental data will be used to validate computational fluid dynamic (CFD) codes, including both Large Eddy Simulations (LES) and unsteady Reynolds-averaged Navier-Stokes equations (URANS) methodologies. In the Experimental and Multiphase flow (ECMF) laboratory at Univerisity of Michigan, we built two experimental facilities to investigate also the effect of scaling. The first facility, DESTROJER (DEnsity Stratified Turbulent ROund free Jet ExpeRiment), featuring a contoured jet nozzle with a diameter of D=12.7mm and a 1m×1m×1m cubic tank, which is made of acrylic glass for optical access. The ratio between the tank width and the nozzle diameter is equal to 78, which ensures that there is no direct interaction between the jet and the side walls. A second, modular experimental facility, features three different tank sizes of size 10×10×30, 20×20×30, 30×30×30 cm3 respectively (all tanks have the same height of 30 cm), and a jet diameter of 2mm. For the modular facility, tank-to-nozzle ratio of 50, 100, and 150 are obtained respectively. Experiments with different-density jet impactions and sharp interface with a density difference of 3.16% In the Laboratory at George Washington University, complementary experiments have been carried out at a facility featuring the same dimensions as DESTROYER. Experiments with different jet diameters have been carried out as well. High-resolution time-resolved measurements have been performed at all facilities using high-speed particle image velocimetry (PIV) and planar laser-induced fluorescence (PLIF). In this way, simultaneous measurements of velocity and density fields have been carried out. At the University of Michigan, wire-mesh sensors have been designed and built to gather high-resolution, time-dependent data of the stratified front in the tank. These measurements are important to provide accurate initial conditions for the CFD simulations. An important outcome of the project has been the development of novel techniques to achieve refractive index matching (RIM) of acqueous solutions with high density differences. RIM is crucial if optical techniques such as PIV are used to measure velocity fields. Previous to this project, experiments up to 3% density difference had been reported in the literature. With the methodology developed in this project, we have been able to push the envelop to density differences up to 9%. The experiments have been used to validate Reynolds Averaged Navier-Stokes (RANS) turbulence models and Large Eddy Simulations (LES) models. Both NEK5000 and the commercial CFD code STAR-CCM+ have been used. The experimental data will be made available to the community through the NE-KAMS databasehave been successfully conducted in the scaled facility.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Precision Measurements of Electron-Proton Elastic Scattering Cross Sections at Large Q2

The electromagnetic form factors are fundamental quantities characterizing the internal structure of the nucleon. Their measurements have provided significant insight into the spatial distribution and interaction of quarks inside the proton. The knowledge of high Q2 form factors proves essential in understanding the properties of quantum chromodynamics in the transition region from non-perturbative to perturbative behavior. It also provides important links to generalized parton distributions, which describe the three-dimensional structure of hadrons at the parton level. In view of the significant theoretical research activities in this field, high quality experimental data are crucial for providing stringent tests and benchmarks to guide and test different models. The form factors can be accessed in experiments by measuring elastic scattering of electrons off a hydrogen target. Experiment E12-07-108, which took place at the Thomas Jefferson National Accelerator Facility, conducted precise measurements of the unpolarized e-p elastic scattering cross section over a Q2 range of 0.6-16.5 GeV2 . This thesis presents the results for 7 kinematic settings with total uncertainties that are 1.5 times smaller than those of the existing data at large Q2 . The proton magnetic form factors were extracted using a parameterization of the form factor ratio obtained from recent polarized e-p scattering experiments. Comparisons to existing global and phenomenological fits are presented.

Ou, Longwu↗
DOE OSTI2022

Rheo-Structural Spectroscopy: Fingerprinting the In Situ Response of Fluids to Arbitrary Flow Fields

The objectives of this project were to develop new sample environments, measurement methodologies and associated modeling tools for characterizing the structural response to arbitrarily complex processing flows using small angle scattering, and to apply these new tools for understanding the fundamental physics governing the structuring of anisotropic particulate and polymeric materials under flow histories and conditions relevant to industrial processing flows. The research resulted in the development and implementation of a new sample environment, the fluidic four roll mill (FFoRM), for in situ small angle neutron and X-ray scattering (SANS/SAXS) measurements. These measurements are capable of generating large data sets that “fingerprint” how a complex fluid responds to a wide range of flow histories involving time variations in deformation type and rate. New modeling tools were developed to extract detailed microstructural information from such data sets, including orientation distribution functions and interparticle correlation functions, as well as reduced-order parametric descriptors of these high-dimensional functions that can be used to readily map, visualize and interpret a fluid’s structural response to its flow history. These new tools were applied to a range of model materials involving elongated particle suspensions in order to provide new insights into the physics of how flow couples with orientational and structural order in complex flows, particularly under non-dilute conditions for which no accurate theories currently exist. Using these investigations, we elucidated a number of new insights into the fundamental phenomena driving such process-structure-property relationships. These findings provide guidance for the further development of rheological models, and ultimately can inform the rational and model-based design of flow processes to achieve optimized orientational ordering that is key to the properties and function of a wide range of energy-relevant materials.

36 MATERIALS SCIENCE↗