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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Probing three-dimensional mesoscopic interfacial structures in a single view using multibeam X-ray coherent surface scattering and holography imaging

Abstract Visualizing surface-supported and buried planar mesoscale structures, such as nanoelectronics, ultrathin-film quantum dots, photovoltaics, and heterogeneous catalysts, often requires high-resolution X-ray imaging and scattering. Here, we discovered that multibeam scattering in grazing-incident reflection geometry is sensitive to three-dimensional (3D) structures in a single view, which is difficult in conventional scattering or imaging approaches. We developed a 3D finite-element-based multibeam-scattering analysis to decode the heterogeneous electric-field distribution and to faithfully reproduce the complex scattering and surface features. This approach further leads to the demonstration of hard-X-ray Lloyd’s mirror interference of scattering waves, resembling dark-field, high-contrast surface holography under the grazing-angle scattering conditions. A first-principles calculation of the single-view holographic images resolves the surface patterns’ 3D morphology with nanometer resolutions, which is critical for ultrafine nanocircuit metrology. The holographic method and simulations pave the way for single-shot structural characterization for visualizing irreversible and morphology-transforming physical and chemical processes in situ or operando .

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Three-dimensional hard X-ray ptychographic reflectometry imaging on extended mesoscopic surface structures

Many nanodevices and quantum devices, with their sizes often spanning from millimeters down to sub-nanometer, have intricate low-dimensional, non-uniform, or hierarchical structures on surfaces and interfaces. Since their functionalities are dependent on these structures, high-resolution surface-sensitive characterization becomes imperative to gain a comprehensive understanding of the function–structure relationship. We thus developed hard x-ray ptychographic reflectometry imaging, a new technique that merges the high-resolution two-dimensional imaging capabilities of hard x-ray ptychography for extended objects, with the high-resolution depth profiling capabilities of x-ray reflectivity for layered structures. The synergy of these two methods fully leverages both amplitude and phase information from ptychography reconstruction to not only reveal surface topography and localized structures, such as shapes and electron densities, but also yields statistical details, such as interfacial roughness that is not readily accessible through coherent imaging solely. The hard x-ray ptychographic reflectometry imaging is well-suited for three-dimensional imaging of mesoscopic samples, particularly those comprising planar or layered nanostructures on opaque supports, and could also offer a high-resolution surface metrology and defect analysis on semiconductor devices, such as integrated nanocircuits and lithographic photomasks for microchip fabrications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Investigation of kinetic inductance and microwave loss in thin-film TaC x N 1−x superconducting resonators

The promise of tantalum for realizing superconducting quantum devices has generated interest in its compound films, particularly nitrides. Among these, cubic-phase tantalum carbonitride (TaC x N 1−x ) offers reduced susceptibility to oxidation and a high critical temperature, yet its microwave properties remain largely unexplored. Here, in this study, we investigate plasma-enhanced atomic layer deposition of cubic-phase TaC x N 1−x thin films for superconducting microwave circuits. Structural and transport measurements reveal nanocrystalline morphology with sub-10 nm grains and superconductivity in the dirty limit. Coplanar waveguide resonators exhibit moderately high kinetic inductance (16.8 pH/sq for 30 nm films) with potential for enhancement through dimensional scaling. The films also support high internal quality factors exceeding 10 5 at 50 mK in the single-photon regime, comparable to granular aluminum. Loss analysis identifies the two-level systems as the dominant limiting mechanism, with potential for further reduction through interface engineering. These results establish atomic layer deposited TaC x N 1−x as a promising material for scalable, low-loss, high-inductance superconducting circuits.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

diffpy.mpdf : open-source software for magnetic pair distribution function analysis

The open-source Python package diffpy.mpdf , part of the DiffPy suite for diffraction and pair distribution function analysis, provides a user-friendly approach for performing magnetic pair distribution function (mPDF) analysis. The package builds on existing libraries in the DiffPy suite to allow users to create models of magnetic structures and calculate corresponding one- and three-dimensional mPDF patterns. diffpy.mpdf can be used to perform fits to mPDF data either in isolation or in combination with atomic pair distribution function data for joint refinement of the atomic and magnetic structure. Examples are given using MnO and MnTe as representative antiferromagnetic compounds and MnSb as a representative ferromagnet.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantifying the topology of magnetic skyrmions in three dimensions

Magnetic skyrmions have so far been treated as two-dimensional spin structures characterized by a topological winding number. However, in real systems with the finite thickness of the device material being larger than the magnetic exchange length, the skyrmion spin texture extends into the third dimension and cannot be assumed as homogeneous. Using soft x-ray laminography, we reconstruct with about 20-nanometer spatial (voxel) size the full three-dimensional spin texture of a skyrmion in an 800-nanometer-diameter and 95-nanometer-thin disk patterned into a 30× [iridium/cobalt/platinum] multilayered film. A quantitative analysis finds that the evolution of the radial profile of the topological skyrmion number is nonuniform across the thickness of the disk. Estimates of the micromagnetic energy densities suggest that the changes in topological profile are related to nonuniform competing energetic interactions. Our results provide a foundation for nanoscale metrology for spintronics devices using topology as a design parameter.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Foundations of automatic feature extraction at LHC–point clouds and graphs

Abstract Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and accurate detector simulations to physics analysis probing possible deviations from the Standard Model. The game-changing feature of these new algorithms is the ability to extract relevant information from high-dimensional input spaces, often regarded as “replacing the expert” in designing physics-intuitive variables. While this may seem true at first glance, it is far from reality. Existing research shows that physics-inspired feature extractors have many advantages beyond improving the qualitative understanding of the extracted features. In this review, we systematically explore automatic feature extraction from a phenomenological viewpoint and the motivation for physics-inspired architectures. We also discuss how prior knowledge from physics results in the naturalness of the point cloud representation and discuss graph-based applications to LHC phenomenology.

Bhardwaj, Akanksha↗
DOE OSTI2022

Automated Stellar Spectra Classification with Ensemble Convolutional Neural Network

Large sky survey telescopes have produced a tremendous amount of astronomical data, including spectra. Machine learning methods must be employed to automatically process the spectral data obtained by these telescopes. Classification of stellar spectra by applying deep learning is an important research direction for the automatic classification of high-dimensional celestial spectra. In this paper, a robust ensemble convolutional neural network (ECNN) was designed and applied to improve the classification accuracy of massive stellar spectra from the Sloan digital sky survey. We designed six classifiers which consist six different convolutional neural networks (CNN), respectively, to recognize the spectra in DR16. Then, according the cross-entropy testing error of the spectra at different signal-to-noise ratios, we integrate the results of different classifiers in an ensemble learning way to improve the effect of classification. The experimental result proved that our one-dimensional ECNN strategy could achieve 95.0% accuracy in the classification task of the stellar spectra, a level of accuracy that exceeds that of the classical principal component analysis and support vector machine model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

A tutorial review of machine learning-based model predictive control methods

Abstract This tutorial review provides a comprehensive overview of machine learning (ML)-based model predictive control (MPC) methods, covering both theoretical and practical aspects. It provides a theoretical analysis of closed-loop stability based on the generalization error of ML models and addresses practical challenges such as data scarcity, data quality, the curse of dimensionality, model uncertainty, computational efficiency, and safety from both modeling and control perspectives. The application of these methods is demonstrated using a nonlinear chemical process example, with open-source code available on GitHub. The paper concludes with a discussion on future research directions in ML-based MPC.

Wu, Zhe [Department of Chemical and Biomolecular E↗
DOE OSTI2022

A Novel Process for Converting Coal to High-Value Polyurethane Products

Battelle has demonstrated a patented process for making high-value, polyurethane (PU) foam from coal, based on preparing liquefied coal via direct liquefaction, converting it to polyols as an intermediate via ozonation, and then making PU foams from these polyols. This process represents a breakthrough in innovative utilization of U.S. coals, and is applicable to bituminous as well as sub-bituminous coals. The resulting PU foam products are projected to have an extremely high value (i.e., over $\$5,000$ /ton), with nearly 100% of carbon utilization from coalderived liquid feedstock, and 31.5% to 43.5% of the carbon in the PU foam polyol product being bio-based. The targeted products represent an extremely large (i.e., over $80 billion/year), existing PU foam market, which could expand into making coatings and adhesives. The process can further help reduce petroleum imports, while improving the economics of PU foam production. This work was completed with funding from the National Energy Technology Laboratory (NETL), with cost share from the State of Ohio’s Ohio Development Services Agency (ODSA) and others, and has advanced the process to 10 kg/day continuous scale and thus to Technology Readiness Level (TRL) 5. A total of 48 coal-based polyols were prepared and evaluated. The initial 28 polyols focused on range finding for ideal conditions. The later 20 polyols were produced as part of process optimizations. These optimizations were targeted around a continuous ozonolysis process to evaluate extended time reactions and to create the necessary intermediate for production of 1-gallon samples of polyol. The most unique attribute of Battelle’s polyol is in the utilization of coal’s aromaticity to gain final foam rigidity. Typically, polyols depend on the isocyanate fraction and cross-linking to gain rigidity. By utilizing coal, we were able to maintain rigidity while reducing the overall hydroxyl value of the polyol. This is important as lower hydroxyl value leads to greater percent weight of the coal-based polyol because less isocyanate is required for foaming. This leads to greater foam cost savings. This report provides the details, process, and process cost models of the conversion of coal to polyols and further to PU foams. Battelle’s process begins with coal liquids. These liquids can be obtained by two processes: coal coking or pyrolysis to produce coal tar, and Battelle’s biobased coal-to-liquids (CTL) process to produce heavy syncrude after liquifying >85% coal. After liquification, Battelle utilizes ozonolysis to create functionalization on the polyaromatic coal structure. The functionalization is then converted to the final polyester polyol through transesterification, or to hydroxyamide polyol through amidification. Equivalent or better standard properties have been obtained for 2 lb/ft 3 density rigid, water and freon-alternative blown foams, including compressive strength, density, R-value, and dimensional stability. Target applications for these foams are insulation, packaging, and energy-absorbing foams. Some exploratory testing also showed promise for adhesives applications. A detailed economic analysis showed that Battelle’s polyol process is economical, at a 140 metric tons per day (MTD) polyol production scale. An attractive return on investment (ROI) at competitive pricing validates the process is ready for a pilot-plant demonstration. A scale-up plan is provided.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Emergent quasi-one-dimensional antiferromagnetism in the distorted kagome magnet CePtPb

CePtPb hosts a distorted kagome lattice of Ce 3+ ions, providing a clean platform to investigate how reduced local symmetry and strong spin-orbit coupling reshape frustrated magnetism. Magnetization, specific heat, and magnetocaloric effect measurements, combined with a symmetry analysis of the single-ion anisotropy, demonstrate that the local 𝑚⁢2⁢𝑚 site symmetry selects a nearly Ising-like Kramers doublet with easy axes lying within the 𝑎⁢𝑏 plane. This results in three distinct in-plane Ising directions and an overall easy-plane anisotropy. The low-energy magnetic response is well captured by a three-sublattice Ising model, which quantitatively reproduces the saturation magnetization for arbitrary in-plane field orientations, including $[110]$ and $[1\bar{⁢1}⁢0]$, as well as the ratio of the field-induced critical fields. For 𝐵∥$[110]$, the phase diagram exhibits two quantum critical points at 𝐵 c⁢1 = 0.25T and 𝐵 c⁢2 = 0.55T, arising from the sequential polarization of the three Ising sublattices. In conclusion, these results reveal that the system develops quasi-one-dimensional spin chains along the 𝑐 axis, emerging from the nominally three-dimensional crystal structure composed of stacked kagome layers, and illustrate how reduced local symmetry can drive effective dimensional reduction in rare-earth Ising magnets.

Li, Fangli [Southern University of Science and Tec↗
DOE OSTI2021

The antesonic condition for the explosion of core-collapse supernovae – II. Rotation and turbulence

In the problem of steady free fall on to a standing shockwave around a central mass, the ‘antesonic’ condition limits the regime of stable accretion to $c_T^2/v_\mathrm{esc}^2\le 3/16$, where cT is the isothermal sound speed in the subsonic post-shock flow, and vesc is the escape velocity at the shock radius. Above this limit, it is impossible to satisfy both the Euler equation and the shock jump conditions, and the system transitions to a wind. This physics explains the existence of a critical neutrino luminosity in steady-state models of accretion in the context of core-collapse supernovae. Here, we extend the antesonic condition to flows with rotation and turbulence using a simple one-dimensional formalism. Both effects decrease the critical post-shock sound speed required for explosion. While quite rapid rotation is required for a significant change to the critical condition, we show that the level of turbulence typically achieved in supernova simulations can greatly impact the critical value of $c_T^2/v_\mathrm{esc}^2$. A core angular velocity corresponding to a millisecond rotation period after contraction of the proto-neutron star results in only a ~5 percent reduction of the critical curve. In contrast, near-sonic turbulence with specific turbulent kinetic energy $K/c_T^2=0.5-1$, leads to a decrease in the critical value of $c_T^2/v_{\rm esc}^2$ by ~20 to 40 percent. This analysis provides a framework for understanding the role of post-shock turbulence in instigating explosions in models that would otherwise fail and helps explain why multidimensional simulations explode more easily than their one-dimensional counterparts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Layer-dependent topological phase in a two-dimensional quasicrystal and approximant

The electronic and topological properties of materials are derived from the interplay between crystalline symmetry and dimensionality. Simultaneously introducing “forbidden” symmetries via quasiperiodic ordering with low dimensionality into a material system promises the emergence of new physical phenomena. Here, we isolate a two-dimensional (2D) chalcogenide quasicrystal and approximant, and investigate their electronic and topological properties. The 2D layers of the materials with a composition close to Ta 1.6 Te, derived from a layered transition metal dichalcogenide, are isolated with standard exfoliation techniques, and investigated with electron diffraction and atomic resolution scanning transmission electron microscopy. Density functional theory calculations and symmetry analysis of the large unit cell crystalline approximant of the quasicrystal, Ta 21 Te 13 , reveal the presence of symmetry-protected nodal crossings in the quasicrystalline and approximant phases, whose presence is tunable by layer number. Our study provides a platform for the exploration of physics in quasicrystalline, low-dimensional materials and the interconnected nature of topology, dimensionality, and symmetry in electronic systems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Space-Split Algorithm for Sensitivity Analysis of Discrete Chaotic Systems With Multidimensional Unstable Manifolds

Accurate approximations of the change of a system's output and its statistics with respect to the input are highly desired in computational dynamics. Ruelle's linear response theory provides breakthrough mathematical machinery for computing the linear response of chaotic dynamical systems. In this paper, we propose an algorithm for sensitivity analysis of discrete chaos with an arbitrary number of positive Lyapunov exponents. We combine the concept of perturbation space-splitting, which regularizes Ruelle's original expression, together with measure-based parameterization of the expanding subspace. We use these tools to rigorously derive trajectory-following recursive relations that converge exponentially fast, and construct a memory-efficient Monte Carlo scheme for derivatives of the output statistics. Thanks to the regularization and lack of simplifying assumptions on the system's behavior, our method is immune to the common problems of other popular methods such as the exploding tangent solutions and unphysical shadowing directions. Here, we provide a ready-to-use algorithm, analyze its complexity, and demonstrate several numerical examples of sensitivity computation using physically-inspired low-dimensional systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

K 3 Ir 2 O 6 and K 16.3 Ir 8 O 30 , Low-Dimensional Iridates with Infinite IrO 6 Chains

A previously unreported 1D iridate, K 3 Ir 2 O 6 , has been grown by a flux method in O 2 -rich environment and its crystal structure determined via single crystal structural analysis. It exhibits straight chains of face-sharing [IrO 6 ] octahedra, which are arranged along the crystallographic c axis, separated by nonmagnetic K ions. No magnetic transitions are observed during measured range, and the material is electrically insulating. Potentially interesting electronic behavior for K 3 Ir 2 O 6 is supported by electronic structure calculations. A structurally related material, K 16.3 Ir 8 O 30 , which displays similar fundamental geometric units but in a different spatial arrangement – zigzag chains based on edge and face sharing [IrO 6 ] octahedra, is also reported. Both materials are of interest for probing the properties of a 1D system with strong spin-orbit coupling

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Structuring Nutrient Yields throughout Mississippi/Atchafalaya River Basin Using Machine Learning Approaches

To minimize the eutrophication pressure along the Gulf of Mexico or reduce the size of the hypoxic zone in the Gulf of Mexico, it is important to understand the underlying temporal and spatial variations and correlations in excess nutrient loads, which are strongly associated with the formation of hypoxia. This study’s objective was to reveal and visualize structures in high-dimensional datasets of nutrient yield distributions throughout the Mississippi/Atchafalaya River Basin (MARB). For this purpose, the annual mean nutrient concentrations were collected from thirty-three US Geological Survey (USGS) water stations scattered in the upper and lower MARB from 1996 to 2020. Eight surface water quality indicators were selected to make comparisons among water stations along the MARB over the past two decades. Principal component analysis (PCA) was used to comprehensively evaluate the nutrient yields across thirty-three USGS monitoring stations and identify the major contributing nutrient loads. The results showed that all samples could be analyzed using two main components, which accounted for 81.6% of the total variance. The PCA results showed that yields of orthophosphate (OP), silica (SI), nitrate–nitrites (NO 3 -NO 2 ), and total suspended sediment (TSS) are major contributors to nutrient yields. It also showed that land-planted crops, density of population, domestic and industrial discharges, and precipitation are fundamental causes of excess nutrient loads in MARB. These factors are of great significance for the excess nutrient load management and pollution control of the Mississippi River. It was found that the average nutrient yields were stable within the sub-MARB area, but the large nitrogen yields in the upper MARB and the large phosphorus yields in the lower MARB were of great concern. t-distributed stochastic neighbor embedding (t-SNE) revealed interesting nonlinear and local structures in nutrient yield distributions. Clustering analysis (CA) showed the detailed development of similarities in the nutrient yield distribution. Moreover, PCA, t-SNE, and CA showed consistent clustering results. This study demonstrated that the integration of dimension reduction techniques, PCA, and t-SNE with CA techniques in machine learning are effective tools for the visualization of the structures of the correlations in high-dimensional datasets of nutrient yields and provide a comprehensive understanding of the correlations in the distributions of nutrient loads across the MARB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

FSVPy: A python-based package for fluorescent streak velocimetry (FSV)

Predictive constitutive equations that connect easy-to-measure transport properties (e.g., viscosity and conductivity) with system performance variables (e.g., power consumption and efficiency) are needed to design advanced thermal and electrical systems. In this work, we explore the use of fluorescent particle-streak analysis to directly measure the local velocity field of a pressure-driven flow, introducing a new Python package (FSVPy) to perform the analysis. Fluorescent streak velocimetry combines high-speed imaging with highly fluorescent particles to produce images that contain fluorescent streaks, whose length and intensity can be related to the local flow velocity. By capturing images throughout the sample volume, the three-dimensional velocity field can be quantified and reconstructed. We demonstrate this technique by characterizing the channel flow profiles of several non-Newtonian fluids: micellar Cetylpyridinium Chloride solution, Carbopol 940, and Polyethylene Glycol. We then explore more complex flows, where significant acceleration is created due to microscale features encountered within the flow. We demonstrate the ability of FSVPy to process streaks of various shapes and use the variable intensity along the streak to extract position-specific velocity measurements from individual images. Thus, we demonstrate that FSVPy is a flexible tool that can be used to extract local velocimetry measurements from a wide variety of fluids and flow conditions.

Mechanics↗
DOE OSTI2020

Construction of a Code Verification Matrix for Heat Conduction With Finite Element Code Applications

When establishing the pedigree of a simulation tool, code verification is used to ensure that the implemented numerical algorithm is a faithful representation of its underlying mathematical model. During this process, numerical results on various meshes are systematically compared to a reference analytic solution. The selection of analytic solutions can be a laborious process, as it is difficult to establish adequate code confidence without performing redundant work. In this report we address this issue by applying a physics-based process that establishes a set of reference problems. In this process, code simulation options are categorized and systematically tested, which ensures that gaps in testing are easily identified and addressed. The resulting problems are primarily intended for code verification analysis but may also be useful for comparison to other simulation codes, troubleshooting activities, or training exercises. The process is used to select fifteen code verification problems relevant for the one-dimensional steady-state heat conduction equation. These problems are applicable to a wide variety of simulation tools, but, in this work, a demonstration is performed using the finite element-based nuclear fuel performance code BISON. Convergence to the analytic solution at the theoretical rate is quantified for a selection of the problems, which establishes a baseline pedigree for the code. Not only can this standard set of conduction solutions be used for verification of other codes, but also the physics-based process for selecting problems can be utilized to quantify and expand testing for any simulation tool.

42 ENGINEERING↗
DOE OSTI2020

Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN

Design of materials with desired properties is currently laborious and heavily relies on intuition of researchers through a trial-and-error process. To tackle this challenge, in this work we propose a novel regressional and conditional generative adversarial network (RCGAN) for inverse design of representative two-dimensional materials, the graphene and boron-nitride (BN) hybrids. RCGAN incorporates a supervised regressor network, thus overcoming the common technical barrier in the traditional unsupervised GANs, which cannot generate data when fed with continuous and quantitative labels. RCGAN can autonomously generate graphene/BN hybrids given any target bandgap values. These structures are distinguished from the ones used for training and exhibit high diversity for a given bandgap. Moreover, they exhibit high fidelity, yielding bandgaps within ~10% MAE F of the desired bandgaps as validated by density functional theory (DFT) calculations. Analysis by the principle component analysis (PCA) and modified locally linear embedding (MLLE) reveals that the generator has successfully generated structures following the statistical distribution of the real structures. It implies the possibility of the RCGAN in recognizing physical rules hidden in the high-dimensional data. The novel strategy for designing regressional GAN architecture together with the successful application to inverse design of materials would inspire further exploration in research fields beyond materials.

36 MATERIALS SCIENCE↗