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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Tuning the Mechanical Properties of Shape Memory Metallic Glass Composites with Brick and Mortar Designs

We use molecular dynamics simulations to characterize the tensile deformation and failure of shape memory alloy-bulk metallic glass composites (BMGCs). As the failure in BMGCs shifts from the MG matrix to the second phase different strategies are required to further improve their mechanical properties. Results show that a staggered brick BMGC displays enhanced ductility with mild strength loss following a homogeneously distributed plasticity. This demonstrates that judiciously designed BMGCs can effectively synergize the mechanical properties of metallic glasses and shape memory alloys by effectively distributing stress and preventing the generation of critical shear bands and premature failure while preserving strength.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Direct Structural Evidence for Interfacial Gradients in Asymmetric Polymer Nanocomposite Blends

Understanding the complex structure of polymer blends filled with nanoparticles (NPs) is key to design their macroscopic properties. In this work, the spatial distribution of hydrogenated (H) and deuterated (D) polymer chains asymmetric in mass is studied by small-angle neutron scattering. Depending on the chain mass, a qualitatively new large-scale organization of poly(vinyl acetate) chains beyond the random-phase approximation is evidenced in nanocomposites with attractive polymer–silica interactions. The silica is found to systematically induce bulk segregation. Only with long H-chains, a strong scattering signature is observed in the q range of the NP size: it is the sign of interfacial isotopic enrichment, that is, of contrasted polymer shells close to the NP surface. A quantitative model describing both the bulk segregation and the interfacial gradient (over ca. 10–20 nm depending on the NP size) is developed, showing that both are of comparable strength. In all cases, NP surfaces trap the polymer blend in a non-equilibrium state, with preferential adsorption around NPs only if the chain length and isotopic preference toward the surface combine their entropic and enthalpic driving forces. This structural evidence for interfacial polymer gradients will open the road for quantitative understanding of the dynamics of many-chain nanocomposite systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Faster Randomized Dynamical Decoupling

We present a randomized dynamical decoupling (DD) protocol that can substantially improve the performance of any given deterministic DD scheme for suppressing coherent noise by using no more than two additional pulses. Our construction is implemented by probabilistically applying sequences of pulses, which, when combined, effectively eliminate the error terms that scale linearly with the system-environment coupling strength. As a result, we show that a randomized protocol using a few pulses can outperform deterministic DD protocols that require considerably more pulses. Furthermore, we prove that the randomized protocol provides an improvement compared to deterministic DD sequences that aim to reduce the error in the system’s Hilbert space, such as Uhrig DD, which had been previously regarded to be optimal. To rigorously evaluate the performance, we introduce new analytical methods suitable for analyzing higher-order DD protocols that might be of independent interest. Here, we also present numerical simulations confirming the significant advantage of using randomized protocols compared to widely used deterministic protocols.

Quantum algorithms & computation↗
DOE OSTI2025

Elastic Screening of Pseudogauge Fields in Graphene

Lattice deformations in graphene couple to the low-energy electronic degrees of freedom as effective scalar and gauge fields. Using molecular dynamics simulations, we show that the optical component of the displacement field, i.e., the relative motion of different sublattices, contributes at equal order as the acoustic component and effectively screens the pseudogauge fields. In particular, we consider twisted bilayer graphene and corrugated monolayer graphene. In both cases, optical lattice displacements significantly reduce the overall magnitude of the pseudomagnetic fields. For corrugated graphene, optical contributions also reshape the pseudomagnetic field and significantly modify the electronic bands near charge neutrality. Previous studies based on continuum elasticity, which ignores this effect, have therefore systematically overestimated the strength of the strain-induced pseudomagnetic field. Furthermore, our results have important consequences for the interpretation of experiments and design of straintronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Comparative study of machine learning techniques for post-combustion carbon capture systems

Computational analysis of countercurrent flows in packed absorption columns, often used in solvent-based post-combustion carbon capture systems (CCSs), is challenging. Typically, computational fluid dynamics (CFD) approaches are used to simulate the interactions between a solvent, gas, and column's packing geometry while accounting for the thermodynamics, kinetics, heat, and mass transfer effects of the absorption process. These simulations can then be used explain a column's hydrodynamic characteristics and evaluate its CO 2 -capture efficiency. However, these approaches are computationally expensive, making it difficult to evaluate numerous designs and operating conditions to improve efficiency at industrial scales. In this work, we comprehensively explore the application of statistical ML methods, convolutional neural networks (CNNs), and graph neural networks (GNNs) to aid and accelerate the scale-up and design optimization of solvent-based post-combustion CCSs. We apply these methods to CFD datasets of countercurrent flows in absorption columns with structured packings characterized by several geometric parameters. We train models to use these parameters, inlet velocity conditions, and other model-specific representations of the column to estimate key determinants of CO 2 -capture efficiency without having to simulate additional CFD datasets. We also evaluate the impact of different input types on the accuracy and generalizability of each model. We discuss the strengths and limitations of each approach to further elucidate the role of CNNs, GNNs, and other machine learning approaches for CO 2 -capture property prediction and design optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Lattice Dynamics of Sb 2 Se 3 from Inelastic Neutron and X-Ray Scattering

The lattice dynamics of orthorhombic Sb 2 Se 3 is studied by a combination of inelastic neutron and 121 Sb nuclear inelastic scattering giving access to the total and Sb partial density of phonon states (DPS). The Se partial DPS is determined from the difference between the total and Sb partial DPS. The total DPS is determined at 39, 150, and 300 K, and an analysis of the temperature-induced mode shifts in combination with low-temperature powder diffraction data is provided. Using an earlier reported theoretical approach, the corresponding total and partial DPS of Sb 2 Se 3 are calculated by first-principles calculations. Herein, a detailed analysis of the Grüneisen parameter, element-specific and bulk Debye temperatures, and the mean force constants as derived from the experimental data and discrete Fourier transform calculations is provided. In general, the calculations underestimate the strength of the covalent Sb-Se bonds.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Regional biogeography versus intra-annual dynamics of the root and soil microbiome

Abstract Background Root and soil microbial communities constitute the below-ground plant microbiome, are drivers of nutrient cycling, and affect plant productivity. However, our understanding of their spatiotemporal patterns is confounded by exogenous factors that covary spatially, such as changes in host plant species, climate, and edaphic factors. These spatiotemporal patterns likely differ across microbiome domains (bacteria and fungi) and niches (root vs. soil). Results To capture spatial patterns at a regional scale, we sampled the below-ground microbiome of switchgrass monocultures of five sites spanning > 3 degrees of latitude within the Great Lakes region. To capture temporal patterns, we sampled the below-ground microbiome across the growing season within a single site. We compared the strength of spatiotemporal factors to nitrogen addition determining the major drivers in our perennial cropping system. All microbial communities were most strongly structured by sampling site, though collection date also had strong effects; in contrast, nitrogen addition had little to no effect on communities. Though all microbial communities were found to have significant spatiotemporal patterns, sampling site and collection date better explained bacterial than fungal community structure, which appeared more defined by stochastic processes. Root communities, especially bacterial, were more temporally structured than soil communities which were more spatially structured, both across and within sampling sites. Finally, we characterized a core set of taxa in the switchgrass microbiome that persists across space and time. These core taxa represented < 6% of total species richness but > 27% of relative abundance, with potential nitrogen fixing bacteria and fungal mutualists dominating the root community and saprotrophs dominating the soil community. Conclusions Our results highlight the dynamic variability of plant microbiome composition and assembly across space and time, even within a single variety of a plant species. Root and soil fungal community compositions appeared spatiotemporally paired, while root and soil bacterial communities showed a temporal lag in compositional similarity suggesting active recruitment of soil bacteria into the root niche throughout the growing season. A better understanding of the drivers of these differential responses to space and time may improve our ability to predict microbial community structure and function under novel conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Image Distinguishability Analysis Testing Through Principal Components and Its Application to Hot Spot Scale Invariance

Hot spots are spatial regions of intense energy localization that govern initiation of secondary high explosives. Studies that characterize or compare simulated hot spots are frequently either qualitatively descriptive or resort to quantitative distribution functions that neglect stochastic variations and spatial correlations—effects that are also neglected in common comparison tests like the Kolmogorov–Smirnov test. To this end, we develop an image distinguishability analysis (IDA) test based on principal component (PC) analysis that makes pixel-by-pixel comparisons between small, for example, O(<10), image data sets. The IDA test makes comparisons through a generalized distance metric in the PC space and a test statistic that is derived to calculate mathematical equation-values. Here, we derive a statistical distribution and criticality criterion to determine whether images are distinguishable from established baselines. We apply the IDA test on images generated from molecular dynamics simulations of hot spots from pore collapse in TATB to assess scale invariance in the complex patterns of hot spots that form in a representative high explosive crystal. The IDA test shows that TATB hot spot spatial temperature fields and their derived temperature histograms exhibit scale-invariant features over specific intervals of shock orientation, strength, and initial pore diameter. However, the IDA test also shows that qualitatively different conclusions regarding invariance can be reached depending on whether the hot spot is treated as a spatially correlated field as opposed to a distribution function that lacks spatial information.

organic↗
DOE OSTI2020

Heterogeneous data-processing optimization with CLARA’s adaptive workflow orchestrator

The hardware landscape used in HEP and NP is changing from homogeneous multi-core systems towards heterogeneous systems with many different computing units, each with their own characteristics. To achieve maximum performance with data processing, the main challenge is to place the right computing on the right hardware. In this paper, we discuss CLAS12 charge particle tracking workflow orchestration that allows us to utilize both CPU and GPU to improve the performance. The tracking application algorithm was decomposed into micro-services that are deployed on CPU and GPU processing units, where the best features of both are intelligently combined to achieve maximum performance. In this heterogeneous environment, CLARA aims to match the requirements of each micro-service to the strength of a CPU or a GPU architecture. A predefined execution of a micro-service on a CPU or a GPU may not be the most optimal solution due to the streaming data-quantum size and the data-quantum transfer latency between CPU and GPU. So, the CLARA workflow orchestrator is designed to dynamically assign micro-service execution to a CPU or a GPU, based on the online benchmark results analyzed for a period of real-time data-processing.

Gyurjyan, Vardan↗
DOE OSTI2023

Reflections on bubble walls

We discuss the dynamics of expanding bubble walls in the presence of massive dark photons whose mass changes as they cross the wall. For sufficiently thin walls, we show that there exists a transient kinematic regime characterized by a constant reflection probability of longitudinal — but not transverse — modes. This effect can have important implications for the dynamics of expanding vacuum bubbles in the early Universe. Most notably, it leads to a new source of pressure on the expanding interface, featuring a non-monotonic dependence on the γ-factor of the bubble walls and reaching a peak at intermediate γ-factors that we dub Maximum Dynamic Pressure. When this pressure is large enough to halt the acceleration of the bubble walls, the difference in vacuum energy densities goes into making a fraction of the dark photons relativistic, turning them into dark radiation. If the dark radiation remains relativistic until late times, an observable contribution to ΔNeff is possible for phase transitions with strength α ~ 10 -2 - 10 -1 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Atomistic modeling of tensile deformation and fracture of carbon fibers: Nanoscale stress redistribution, effect of local structural characteristics and nanovoids

The mechanisms of tensile deformation and fracture of carbon fibers are investigated in large-scale molecular dynamics simulations. The simulations performed for computational samples with different structural characteristics enable analysis of the structural sensitivity of the mechanical properties. It is found that the carbon fiber nanostructure can be represented by a fine (nanoscale) mixture of stiff and soft regions. Despite the nanoscale structural heterogeneity of the computational fibers, their fracture strain and tensile strength exceed the experimental values by almost an order of magnitude. Here, this observation is explained by the ability of the carbon fiber nanostructure, largely consisting of curved and folded layers of turbostratic carbon, to exhibit continuous nanoscale load redistribution during the deformation. The transient, fluid nature of a percolating cluster of stress-holding structural elements prevents stress localization at the early stage of the deformation and enables the fiber nanostructure to adapt to the increasing load. Overall, the results of the simulations suggest that the design of advanced carbon fiber manufacturing and post-processing methods focused on the elimination of critical mesoscopic structural defects (internal voids, foreign inclusions, and surface flaws) can yield a substantial increase in the strength and fracture strain of carbon fibers.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Grain growth of nanocrystalline aluminum under tensile deformation: A combined in situ TEM and atomistic study

Nanocrystalline Al thin films have been strained in situ in a transmission electron microscope using two separate nanomechanical techniques involving a push-to-pull device and a microelectromechanical system (MEMS) device. Deformation-induced grain growth was observed to occur via stress-assisted grain boundary migration with extensive grain growth occurring in the necked region, indicating that the increase in local stress drives the boundary migration. Under applied tensile stresses close to the ultimate tensile strength of 450 MPa for a nanocrystalline Al specimen, measured boundary migration speeds are 0.2 – 0.7 nm s -1 for grains outside necked region and increases to 2.5 nm s -1 for grains within the necked region where the local estimated tensile stresses are elevated to around 630 MPa. By tracking grain boundary motion over time, molecular dynamics simulations showed qualitative agreement in terms of pronounced grain boundary migration with the experimental observations. The combined in situ observation and molecular dynamics simulation results underscore the important role of stress-driven grain growth in plastically deforming nanocrystalline metals, leading to intergranular fracture through predominant grain boundary sliding in regions with large localized deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Origin of Mechanical Enhancement in Polymer Nanoparticle (NP) Composites with Ultrahigh NP Loading

Polymer nanoparticle composites (PNCs) with ultrahigh loading of nanoparticles (NPs) (>50%) have been shown to exhibit markedly improved strength, stiffness, and toughness simultaneously compared to the neat systems of their components. Recent experimental studies on the effect of polymer fill fraction in these highly loaded PNCs reveal that even at low polymer fill fractions, hardness and modulus increase significantly. In this work, we aim to understand the origin of these performance enhancements by examining the dynamics of both polymer and NPs under tensile deformation. We perform molecular dynamics simulations of coarse-grained, glassy polymer in random-close-packed NP packings with a varying polymer fill fraction. We characterize the mechanical properties of the PNC systems, compare the NP rearrangement behavior, and study the polymer segmental and chain-level dynamics during deformation below the polymer glass transition. Finally, our simulation results confirm the experimentally observed increase in modulus at low polymer fill fractions, and we provide evidence that the source of mechanical enhancement is the polymer bridging effect.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Quantum Geometric Oscillations in Two-Dimensional Flat-Band Solids

Two-dimensional van der Waals heterostructures can be engineered into artificial superlattices that host flat bands with significant Berry curvature and provide a favorable environment for the emergence of novel electron dynamics. In particular, the Berry curvature can induce an oscillating trajectory of an electron wave packet transverse to an applied static electric field. Though analogous to Bloch oscillations, this novel oscillatory behavior is driven entirely by quantum geometry in momentum space instead of band dispersion. While the current from Bloch oscillations can be localized by increasing field strength, the current from the geometric orbits saturates to a nonzero plateau in the strong-field limit. Finally, in nonmagnetic materials, the geometric oscillations are even under inversion of the applied field, whereas the Bloch oscillations are odd, a property that can be used to distinguish these two coexisting effects.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Reformulating a Gurson-based dynamic damage model and demonstrating improved predictive power and numerical robustness

The original Tepla (TEnsile PLAsticity) ductile damage model, based on the Gurson yield surface, has long been used to model damage evolution and material failure under dynamic loading. Unfortunately, Tepla suffered from mesh sensitivity, numerical instability, and limited predictive capability. Here, we theoretically reformulate Tepla to address these issues. We especially focus on the prediction of porosity, which is the key state variable used for modeling ductile damage, as compared to more easily measured surface velocities, which are at best an indirect measure of damage. Key model changes include separating the viscosity during volumetric void growth from underlying shear strength behavior and switching to an iterative bisection solver. The new Tepla is then calibrated on incipient spall experiments on half-hard copper and tantalum, which demonstrate its ability to simultaneously fit the model to recovered porosity distributions and measured surface velocities, a stringent test. Improved numerical behavior, such as greatly reduced mesh sensitivity, is also shown in those simulations. Finally, the new Tepla model is applied to several high-explosive loaded, sweeping wave experiments, showing the ability of the model to predict behavior on tests with significantly different loading conditions and histories than the calibration data.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Charge-density-wave melting in the one-dimensional Holstein model

We study the Holstein model of spinless fermions, which at half filling exhibits a quantum phase transition from a metallic Tomonaga-Luttinger liquid phase to an insulating charge-density-wave (CDW) phase at a critical electron-phonon coupling strength. In our work, we focus on the real-time evolution starting from two different types of initial states that are CDW ordered: (i) ideal CDW states with and without additional phonons in the system and (ii) correlated ground states in the CDW phase. We identify the mechanism for CDW melting in the ensuing real-time dynamics and show that it strongly depends on the type of initial state. We focus on the far-from-equilibrium regime and emphasize the role of electron-phonon coupling rather than dominant electronic correlations, thus complementing a previous study of photoinduced CDW melting [H. Hashimoto and S. Ishihara, Phys. Rev. B 96, 035154 (2017)]. The numerical simulations are performed by means of matrix-product-state based methods with a local basis optimization (LBO). Within these techniques, one rotates the local (bosonic) Hilbert spaces adaptively into an optimized basis that can then be truncated while still maintaining a high precision. In this work, we extend the time-evolving block decimation (TEBD) algorithm with LBO, previously applied to single-polaron dynamics, to a half-filled system. We demonstrate that in some parameter regimes, a conventional TEBD method without LBO would fail. Furthermore, we introduce and use a ground-state density-matrix renormalization group method for electron-phonon systems using local basis optimization. In our examples, we account for up to M ph =40 bare phonons per site by working with O(10) optimal phonon modes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Phenomenology of Spectral Functions in Disordered Spin Chains at Infinite Temperature

Studies of disordered spin chains have recently experienced a renewed interest, inspired by the question to which extent the exact numerical calculations comply with the existence of a many-body localization phase transition. For the paradigmatic random field Heisenberg spin chains, many intriguing features were observed when the disorder is considerable compared to the spin interaction strength. Here, we introduce a phenomenological theory that may explain some of those features. The theory is based on the proximity to the noninteracting limit, in which the system is an Anderson insulator. Taking the spin imbalance as an exemplary observable, we demonstrate that the proximity to the local integrals of motion of the Anderson insulator determines the dynamics of the observable at infinite temperature. In finite interacting systems our theory quantitatively describes its integrated spectral function for a wide range of disorders.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Climatic Controls on Soil Carbon Accumulation and Loss in a Dryland Ecosystems

Arid and semiarid ecosystems drive year-to-year variability in the strength of the terrestrial carbon (C) sink, yet there is uncertainty about how soil C gains and losses contribute to this variation. For this work, to address this knowledge gap, we embedded C-depleted soil mesocosms, containing litter or biocrust C inputs, within an in situ dryland ecosystem warming experiment. Over the course of one year, changes in microbial biomass and total soil organic C pools were monitored alongside hourly measurements of soil CO 2 flux. We also developed a biogeochemical model to explore the mechanisms that gave rise to observed soil C dynamics. Field data and model simulations demonstrated that water exerted much stronger control on soil biogeochemistry than temperature, with precipitation events triggering large CO 2 pulses and transport of litter- and biocrust-derived C into the soil profile. We expected leaching of organic matter would result in steady accumulation of C within the mineral soil over time. Instead, the size of the total organic C pool fluctuated throughout the year, largely in response to microbial growth: increases in the size of microbial biomass were negatively correlated with the quantity of C residing in the top 2 cm, where most biogeochemical changes were observed. Our data and models suggest that microbial responses to precipitation events trigger rapid metabolism of dissolved organic C inputs, which strongly limit accumulation of autotroph-derived C belowground. Accordingly, changes in the magnitude and/or frequency of precipitation events in this dryland ecosystem could have profound impacts on the strength of the soil C sink.

54 ENVIRONMENTAL SCIENCES↗