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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Machine learning and algebraic approaches towards complete matter spectra in 4d F-theory

Motivated by engineering vector-like (Higgs) pairs in the spectrum of 4d F-theory compactifications, we combine machine learning and algebraic geometry techniques to analyze line bundle cohomologies on families of holomorphic curves. To quantify jumps of these cohomologies, we first generate 1.8 million pairs of line bundles and curves embedded in dP 3 , for which we compute the cohomologies. A white-box machine learning approach trained on this data provides intuition for jumps due to curve splittings, which we use to construct additional vector-like Higgs-pairs in an F-Theory toy model. We also find that, in order to explain quantitatively the full dataset, further tools from algebraic geometry, in particular Brill-Noether theory, are required. Using these ingredients, we introduce a diagrammatic way to express cohomology jumps across the parameter space of each family of matter curves, which reflects a stratification of the F-theory complex structure moduli space in terms of the vector-like spectrum. Furthermore, these insights provide an algorithmically efficient way to estimate the possible cohomology dimensions across the entire parameter space.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Assessing parallel path cooling tower performance via artificial neural networks

Real-time monitoring of a research nuclear reactor, a system in which all generated power is dissipated to the environment, can be performed via analysis of the heat rejection from the cooling system. Given an inlet water temperature and flow rate, the reactor power can be well-approximated from the outlet water temperature; however, the instrumentation to measure outlet conditions may not be robust or accurate. If we know how a cooling tower performs from historical data, but cannot measure the outlet temperature, a mathematical representation of the system can be inverted to obtain the outlet water temperature that describes the cooling capacity. Unfortunately, model inversion processes are computationally expensive. To address this, an artificial neural network (ANN) is implemented to assess the performance of a multi-cell cooling tower for a nuclear reactor. This approach leverages the Merkel model to obtain an extensive data set describing performance of the cooling tower cells throughout a wide array of potential operating conditions. The Merkel model is expressed as a function of four parameters: the inlet and outlet water temperatures, inlet air wet bulb temperature, and ratio of liquid-to-gas mass flow rates (L/G), which together provide a non-dimensional number indicative of cooling tower performance, called the Merkel integral. Computing a 4-dimensional data structure that describes finite combinations of the Merkel integral, an inverse model is then generated using an ANN to determine the cell outlet water temperature from the other three model parameters along with the computed Merkel integral. Compared to traditional model inversion methods, the ANN reduces the computational time by approximately 4 orders of magnitude, with effectively no sacrifice to solution accuracy, and could be applied for different cooling towers in the event the performance curve is known. Finally, three use cases of the ANN are then reviewed: (1) determining the cell outlet water temperatures when gas flow at rated conditions (GFRC) is known, (2) performing the prior case without knowledge of the GRFC, and (3) assessing performance differences between the individual tower cells.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Deep learning ferroelectric polarization distributions from STEM data via with and without atom finding

Over the last decade, scanning transmission electron microscopy (STEM) has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision, opening the pathway toward exploring ferroelectric, ferroelastic, and chemical phenomena on the atomic scale. Analyses to date extracting a polarization signal from lattice coupled distortions in STEM imaging rely on discovery of atomic positions from intensity maxima/minima and subsequent calculation of polarization and other order parameter fields from the atomic displacements. Here, we explore the feasibility of polarization mapping directly from the analysis of STEM images using deep convolutional neural networks (DCNNs). In this approach, the DCNN is trained on the labeled part of the image (i.e., for human labelling), and the trained network is subsequently applied to other images. We explore the effects of the choice of the descriptors (centered on atomic columns and grid-based), the effects of observational bias, and whether the network trained on one composition can be applied to a different one. This analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Charge correlations suppress unconventional pairing in the Holstein model

In a recent work by Schrodi et al. [Phys. Rev. B. 104, L140506 (2021)], the authors find an unconventional superconducting state with a sign-changing order parameter using the Migdal-Eliashberg theory, including the first vertex correction. This unconventional solution arises despite using an isotropic bare electron-phonon coupling in the Hamiltonian. We examine this claim using hybrid quantum Monte Carlo for a single-band Holstein model with a cuprate-like noninteracting band structure and identical parameters to Schrodi et al.. Our Monte Carlo results for these parameters suggest that unconventional pairing correlations do not exceed their noninteracting values at any carrier concentration we have checked. Instead, strong charge-density-wave correlations persist at the lowest accessible temperatures for dilute and nearly half-filled bands. As a result, we present arguments for how vertex-corrected Migdal-Eliashberg calculation schemes can lead to uncontrolled results in the presence of Fermi surface nesting.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Phase diagram of CeSb 2 from magnetostriction and magnetization measurements: Evidence for ferrimagnetic and antiferromagnetic states

Cerium diantimonide (CeSb 2 ) is one of a family of rare-earth-based magnetic materials that exhibit metamagnetism, enabling control of the magnetic ground state through an applied magnetic field. At low temperatures, CeSb 2 hosts a rich phase diagram with multiple magnetically ordered phases for many of which the order parameter is only poorly understood. In this work, we report a study of its metamagnetic properties by scanning tunneling microscopy (STM) and magnetization measurements. We use STM measurements to characterize the sample magnetostriction with subpicometer resolution from magnetic field and temperature sweeps. This allows us to directly assess the bulk phase diagram as a function of field and temperature and relate spectroscopic features from tunneling spectroscopy to bulk phases. Our magnetostriction and magnetization measurements indicate that the low-temperature ground state at zero field is ferrimagnetic. Quasiparticle interference mapping shows evidence for a reconstruction of the electronic structure close to the Fermi energy upon entering the magnetically ordered phase.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

A reaction–diffusion model for grayscale digital light processing 3D printing

We report that digital light processing (DLP) 3D printing is an additive manufacturing process that utilizes light patterns to photopolymerize a liquid resin into a solid. Due to the accuracy of modern digital micromirror devices (DMD) and recent advances in resin chemistry, it is now possible to create functionally graded structures using different light intensity values, also known as grayscale DLP (g-DLP). Different intensities of light lead to differences in the polymer crosslinking density after curing, which ultimately produces a part with gradients of material properties. However, g-DLP is a complicated process. First, the DLP printing is a highly coupled chemical and physical process that involves light propagation, chemical reactions, species diffusion, heat transfer, volume shrinkage, and changes in mechanical behaviors of the curing resin. Second, in g-DLP, light gradients create strong in plane gradients of chemical species concentrations in the curing liquid resin due to the strong dependence of light intensity on the rate of monomer crosslinking. Furthermore, light gradients through the depth create concentration gradients due to the degree of cure dependent light absorption and the use of photoabsorbers. These complex physical features of the printing process must be understood in order to properly control printing parameters such as light exposure time, printing speed, and grayscale variations to achieve accurate mechanical properties. In this paper, a photopolymerization reaction–diffusion model is developed and used in conjunction with experiments to investigate the coupled effects of light propagation, chemical reaction rates, and species diffusion during g-DLP 3D printing. The model is implemented numerically utilizing the finite difference method and simulation results are compared to experimental findings of simple printed structures. The agreement between experimental and model predictions of simple quantities of interest, such as geometric feature sizes, shows that the model can capture the overcure due to free-radical and other species diffusion during printing when grayscale patterns are employed. This model lays the groundwork for future extensions that can incorporate more complex coupled physics such as heat transfer, volume shrinkage, and material property evolution, which are critically important in utilizing g-DLP 3D printing for the fabrication of high-performance parts which excellent geometric and material property tolerances.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Lagrangian conditional statistics and flow topology in edge plasma turbulence

Lagrangian statistics and particle transport in edge plasma turbulence are investigated using the Hasegawa–Wakatani model and its modified version. The latter shows the emergence of pronounced zonal flows. Different values of the adiabaticity parameter are considered. The main goal is to characterize the role of coherent structures, i.e., vortices and zonal flows, and their impact on the Lagrangian statistics of particles. Computationally intensive long time simulations following ensembles of test particles over hundreds of eddy turnover times are considered in statistically stationary turbulent flows. The flow topology is characterized using the Lagrangian Okubo–Weiss criterion in order to split the flow into topologically different domains. In elliptic and hyperbolic regions, the probability density functions (PDFs) of the residence time have self-similar algebraic decaying tails. However, in the intermediate regions, the PDFs exhibit exponentially decaying tails. Topologically conditioned PDFs of the Lagrangian velocity, and acceleration and density fluctuations are likewise computed. The differences between the classical Hasegawa–Wakatani system and its modified version are assessed, and the role of zonal flows is highlighted. Here, the density flux spectrum, which characterizes the contributions of different length scales, is studied, and its inertial scaling is found to be in agreement with predictions based on dimensional arguments. Analyzing the angular change of particle tracers at different time scales, corresponding to coarse grained curvature, completes the study, and these multiscale geometric statistics quantify the directional properties of the particle motion in different flow regimes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Instability and evolution of the magnetic ground state in metallic perovskites GdRh 3 C 1-x B x

In this paper, we report investigations of the structural, magnetic, electrical transport and thermal properties of five compositions of the metallic perovskite GdRh 3 C 1–x B x (0.00 ≤ x ≤ 1.00). Our results show that all the five compositions undergo magnetic ordering at low temperatures, but the nature of the ordered state is significantly different in the carbon- and the boron-rich compositions, where the former shows signatures of an amplitude-modulated magnetic structure and the latter exhibits evidences of an equal-moment incommensurate antiferromagnetic ordering. We also observe a remarkable field-dependent evolution of conduction carrier polarization in the compositionally disordered compounds. The outcomes indicate that this system is energetically situated in proximity to a magnetic instability where small variations in the control parameter(s), such as lattice constant and/or electron density, lead to considerably different ground states.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A unified hp-HDG framework for Friedrichs' PDE systems

Here, this work proposes a unified hp-adaptivity framework for hybridized discontinuous Galerkin (HDG) method for a large class of partial differential equations (PDEs) of Friedrichs' type. In particular, we present unified hp-HDG formulations for abstract one-field and two-field structures and prove their well-posedness. In order to handle non-conforming interfaces we simply take advantage of HDG built-in mortar structures. With split-type mortars and the approximation space of trace, a numerical flux can be derived via Godunov approach and be naturally employed without any additional treatment. As a consequence, the proposed formulations are parameter-free. We perform several numerical experiments for time-independent and linear PDEs including elliptic, hyperbolic, and mixed-type to verify the proposed unified hp-formulations and demonstrate the effectiveness of hp-adaptation. Two adaptivity criteria are considered: one is based on a simple and fast error indicator, while the other is rigorous but more expensive using an adjoint-based error estimate. The numerical results show that these two approaches are comparable in terms of convergence rate even for problems with strong gradients, discontinuities, and singularities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Oxygen Deficiency and Migration-Mediated Electric Polarization in Magnetic Fe,Co-Substituted SrTiO3−δ

We use density functional theory (DFT) calculations to show that oxygen vacancies (vO) and mobility induce noncentrosymmetric polar structures in SrTi1−x−yFexCoyO3−δ (STFC, x=y=0.125) with δ={0.125,0.25}, enhance the saturation magnetization, and give rise to large changes in the electric polarization |ΔP|. We present an intuitive set of rules to describe the properties of STFC, which are based on the interplay between (Co/Fe)-vO defects, magnetic cation coordination, and topological vacancy disorder. STFC structures consist of layered crystals with sheets of linearly organized O4,5,6-coordinated Fe–Co pairs, sandwiched with layers of O5-coordinated Ti. (Co/Fe)-vO defects are the source of crystal distortions, cation off-centering and bending of the oxygen octahedra which, considering the charge redistribution mediated by vO and the cations’ electronegativity and valence states, triggers an effective electric polarization. Oxygen migration for δ=0.125 leads to |ΔP|>∼10 µC/cm2 due to quantum-of-polarization differences between δ=0.125 structures. Increasing the oxygen deficiency to δ=0.25 yields |ΔP|, the O migration of which resolved polarization for δ=0.25 is >∼3 µC/cm2. Magnetism is dominated by the Fe,Co spin states for δ=0.125, and there is a contribution from Ti magnetic moments (∼1 μB) for δ=0.25. Magnetic and electric order parameters change for variations of δ or oxygen migration for a given oxygen deficiency. Our results capture characteristics observed in the end members of the series SrTi(Co,Fe)O3, and suggest the existence of a broader set of rules for oxygen-deficient multiferroic oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Revisiting superconductivity in the extended one-band Hubbard model: Pairing via spin and charge fluctuations

The leading superconducting instabilities of the two-dimensional extended repulsive one-band Hubbard model within spin-fluctuation pairing theory depend sensitively on electron density, band, and interaction parameters. We map out the phase diagrams within a random-phase-approximation spin- and charge-fluctuation approach, and find that while B 1g (d x 2 –y 2 ) and B 2g (d xy ) pairing dominates in the absence of repulsive longer-range Coulomb interactions V NN , the latter induces pairing in other symmetry channels, including, e.g., A 2g (g-wave), nodal A 1g (extended s-wave), or nodal E u (p-wave) spin-triplet superconductivity. At the lowest temperatures, transition boundaries in the phase diagrams between symmetry-distinct spin-singlet orders generate complex time-reversal symmetry broken superpositions. By contrast, we find that boundaries between singlet and triplet regions are characterized by first-order transitions. Finally, motivated by recent photoemission experiments, we have determined the influence of an additional explicitly attractive nearest-neighbor interaction, V NN < 0, on the superconducting gap structure. Furthermore, depending on the electronic filling, such an attraction boosts E u (p-wave) spin-triplet or B 1g (d x 2 –y 2 ) spin-singlet ordering.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Strongly coupled $\mathrm{QFT}$ dynamics via $\mathrm{TQFT}$ coupling

We consider a class of quantum field theories and quantum mechanics, which we couple to $\mathbb{Z}$ N topological QFTs, in order to classify non-perturbative effects in the original theory. The $\mathbb{Z}$ N TQFT structure arises naturally from turning on a classical background field for a $\mathbb{Z}$ N 0- or 1-form global symmetry. In SU(N) Yang-Mills theory coupled to $\mathbb{Z}$ N TQFT, the non-perturbative expansion parameter is exp[-S I /N] = exp[-8π 2 /g 2 N] both in the semi-classical weak coupling domain and strong coupling domain, corresponding to a fractional topological charge configurations. To classify the non-perturbative effects in original SU(N) theory, we must use PSU(N) bundle and lift configurations (critical points at infinity) for which there is no obstruction back to SU(N). These provide a refinement of instanton sums: integer topological charge, but crucially fractional action configurations contribute, providing a TQFT protected generalization of resurgent semi-classical expansion to strong coupling. Monopole-instantons (or fractional instantons) on T 3 x $S^1_L$ can be interpreted as tunneling events in the ’t Hooft flux background in the PSU(N) bundle. The construction provides a new perspective to the strong coupling regime of QFTs and resolves a number of old standing issues, especially, fixes the conflicts between the large-N and instanton analysis. We derive the mass gap at θ = 0 and gaplessness at θ = π in $\mathbb{CP}$ 1 model, and mass gap for arbitrary θ in $\mathbb{CP}$ N-1 , N ≥ 3 on $\mathbb{R}$ 2 .

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Correlation approach for quality assurance of additive manufactured parts based on optical metrology

Surface topography and surface finish are two significant factors for evaluating the quality of products in additive manufacturing (AM). AM parts are fabricated layer by layer, which is quite different from traditional formative or subtractive methods. Despite rapid progress in additive manufacturing and associated optical metrology for quality control and in-situ monitoring, limited research has been conducted to investigate the reliability of 3D surface measurement data. The surface topologies scanned by multiple optical systems demonstrated significant differences due to varying sampling mechanisms, resolutions, system noises, etc. The 3D datasets should be trustworthy in order to extract parameters for quality assurance or feedback control from 3D surface measurements. In this paper, we set up new standards to evaluate the reliability of 3D surface measurement data and analyze the variation in the topographical profile. In this study, two non-contact optical methods based on Focus Variation Microscopy (FVM) and Structured Light System (SLS) were adopted to measure the surface topography of the target components. The two optical metrology systems generated two entirely different point cloud datasets. Statistical methods were applied to test the difference between the data obtained from the two systems. By using a data analytics approach for comparison, it was found that the surface roughness estimated from the point cloud data sets of FVM and SLS has no significant difference, though the point cloud data sets were completely different. This paper provides standard validation approach to evaluate the plausibility of metrology data from in-situ real-time surface analysis for process planning of AM.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Tuning of electronic properties in highly lattice-mismatched epitaxial SmN

Here, we establish the relationship between native N vacancies, introduced through varying growth parameters, and the structural and transport properties in SmN thin films grown via molecular beam epitaxy grown on MgO(001). The varying levels of N vacancies introduced by varying the ratio of Sm to N atoms during deposition creates excess carriers that radically transform the electrical behavior of the film, over a range of five orders of magnitude, from highly resistive to highly conductive, and unlocking a phase transition evidenced by the presence of a ferromagnetic feature in resistivity. X-ray photoelectron spectroscopy results show that this effect is much less pronounced when varying the available nitrogen species. These samples retain a highly crystal quality despite being grown on a substrate with a lattice mismatch of 20%, alleviating the strain by forming a highly strained SmO oxide layer. The integration between SmN and several transition metal nitride compounds has the potential to unlock new architectures for Josephson junction devices.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Magnetic dynamics in NiTiO 3 honeycomb antiferromagnet using neutron scattering

The ilmenite NiTiO 3 consists of a buckled honeycomb lattice, with the Ni spins aligned ferromagnetically in-plane and antiferromagnetically out-of-plane. Using neutron spectroscopy, the magnetic structure and the dynamics were investigated as a function of temperature. Dispersive acoustic bands and nearly dispersionless optical bands at ≈ 3.7 meV are described by a highly anisotropy Heisenberg model with stronger antiferromagnetic (AFM) out-of-plane, weaker ferromagnetic (FM) in-plane interactions and an anisotropy gap of 0.95 meV. Furthermore, the order parameter yields a critical exponent between the Heisenberg and two-dimensional Ising models, consistent with highly anisotropic Heisenberg systems. The frustration parameter ≈ 2 supports a weakly frustrated system.

Antiferromagnets↗
DOE OSTI2020

Efficient Low-Order Refined Preconditioners for High-Order Matrix-Free Continuous and Discontinuous Galerkin Methods

In this paper, we design preconditioners for the matrix-free solution of high-order continuous and discontinuous Galerkin discretizations of elliptic problems based on finite element method--spectral element method (FEM-SEM) equivalence and additive Schwarz methods. The high-order operators are applied without forming the system matrix, making use of sum factorization for efficient evaluation. The system is preconditioned using a spectrally equivalent low-order (p = 1) finite element operator discretization on a refined mesh. The low-order refined mesh is anisotropic and not shape regular in the polynomial degree of the high-order operator, requiring specialized solvers to treat the anisotropy. We make use of an element-structured, geometric multigrid V-cycle with ordered ILU(0) smoothing. The preconditioner is parallelized through an overlapping additive Schwarz method that is robust in h and p. The method is extended to interior penalty and Bassi and Rebay (BR2) discontinuous Galerkin discretizations, for which it is also robust in the size of the penalty parameter. Finally, numerical results are presented on a variety of examples, verifying the uniformity of the preconditioner.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Bayesian Learning of Adatom Interactions from Atomically Resolved Imaging Data

Atomic structures and adatom geometries of surfaces encode information about the thermodynamics and kinetics of the processes that lead to their formation, and which can be captured by a generative physical model. In this work, we develop a workflow based on a machine-learning-based analysis of scanning tunneling microscopy images to reconstruct the atomic and adatom positions, and a Bayesian optimization procedure to minimize statistical distance between the chosen physical models and experimental observations. We optimize the parameters of a 2- and 3-parameter Ising model describing surface ordering and use the derived generative model to make predictions across the parameter space. For concentration dependence, we compare the predicted morphologies at different adatom concentrations with the dissimilar regions on the sample surfaces that serendipitously had different adatom concentrations. The proposed workflow can be used to reconstruct the thermodynamic models and associated uncertainties from the experimental observations of materials microstructures. The code used in the manuscript is available at https://github.com/saimani5/Adatom_interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Ab initio property predictions of quinary solid solutions using small binary cells

The Set of Small Ordered Structures (SSOS) approach is an ab initio technique for modelling random solid solutions in which many small structures are averaged so that their correlation functions match those of a desired composition. SSOS has been shown to be effective in reducing the cost of density functional theory calculations relative to other well-known techniques such as cluster expansions and special quasirandom structures for modelling solid solutions. Here in this work, we demonstrate that SSOS’s can be constructed using cells with only a subset of elements while still accurately modelling multi-component systems. Specifically, we show that small binary cells can effectively model two quinary high entropy alloys – NbTaTiHfZr and MoNbTaVW – accurately capturing properties such as formation energy, lattice parameters, elastic constants, and root-mean-square atomic displacements. Overall, this insight is useful for those looking to construct databases of such small structures for predicting the properties of multi-component solid solutions, as it greatly decreases the number of structures that needs to be considered.

36 MATERIALS SCIENCE↗