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At least 433 records · Page 24

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DOE OSTI2022

Closing the loop between microstructure and charge transport in conjugated polymers by combining microscopy and simulation

A grand challenge in materials science is to identify the impact of molecular composition and structure across a range of length scales on macroscopic properties. We demonstrate a unified experimental–theoretical framework that coordinates experimental measurements of mesoscale structure with molecular-level physical modeling to bridge multiple scales of physical behavior. Here we apply this framework to understand charge transport in a semiconducting polymer. Spatially-resolved nanodiffraction in a transmission electron microscope is combined with a self-consistent framework of the polymer chain statistics to yield a detailed picture of the polymer microstructure ranging from the molecular to device relevant scale. Using these data as inputs for charge transport calculations, the combined multiscale approach highlights the underrepresented role of defects in existing transport models. Short-range transport is shown to be more chaotic than is often pictured, with the drift velocity accounting for a small portion of overall charge motion. Local transport is sensitive to the alignment and geometry of polymer chains. At longer length scales, large domains and gradual grain boundaries funnel charges preferentially to certain regions, creating inhomogeneous charge distributions. While alignment generally improves mobility, these funneling effects negatively impact mobility. The microstructure is modified in silico to explore possible design rules, showing chain stiffness and alignment to be beneficial while local homogeneity has no positive effect. This combined approach creates a flexible and extensible pipeline for analyzing multiscale functional properties and a general strategy for extending the accesible length scales of experimental and theoretical probes by harnessing their combined strengths.

42 ENGINEERING↗

DOE OSTI2023

Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning

Recent years have seen a rich literature of data-driven approaches designed for power grid applications. However, insufficient consideration of domain knowledge can impose a high risk to the practicality of the methods. Specifically, ignoring the grid-specific spatiotemporal patterns (in load, generation, and topology, etc.) can lead to outputting infeasible, unrealizable, or completely meaningless predictions on new inputs. To address this concern, this paper investigates real-world operational data to provide insights into power grid behavioral patterns, including the time-varying topology, load, and generation, as well as the spatial differences (in peak hours, diverse styles) between individual loads and generations. Then based on these observations, we evaluate the generalization risks in some existing ML works caused by ignoring these grid-specific patterns in model design and training.

Li, Shimiao↗

NASA NTRS1975

The effects of precipitation on radar target identification and imaging

The properties of precipitation which will influence radar system design are discussed. The spatial characteristics of rainfall and the sizes and shapes of raindrops are described. The dielectric behavior of water is combined with these characteristics to determine the effects of rain on electromagnetic waves. These effects include: absorption, scatter, noise emission, phase shift, and depolarization.

Hodge, D. B.↗

NASA NTRS1976

The effects of precipitation on radar target identification and imaging

Hodge, D. B.↗

NASA NTRS1986

Flight simulator requirements for airline transport pilot training - An evaluation of motion system design alternatives

The effect of motion platform systems on pilot behavior is considered with emphasis placed on civil aviation applications. A dynamic model for human spatial orientation based on the physiological structure and function of the human vestibular system is presented. Motion platform alternatives were evaluated on the basis of the following motion platform conditions: motion with six degrees-of-freedom required for Phase II simulators and two limited motion conditions. Consideration was given to engine flameout, airwork, and approach and landing scenarios.

Lee, A. T.↗

NASA NTRS1991

Spatiospectral and picosecond spatiotemporal properties of a broad area operating channeled-substrate-planar laser array

Spatiospectral and spatiotemporal properties of an eight-element channeled-substrate-planar laser array are investigated in both CW and pulsed operating conditions. The closely spaced CSP array with strong optical coupling between array elements is characterized by a broad area laserlike operation determined by its spatial mode spectra. The spatiotemporal evolution of the near and far field exhibits complex dynamic behavior in the picosecond to nanosecond domain. Operating parameters for the laser device have been experimentally determined. These results provide important information for the evaluation of the dynamic behavior of coherent semiconductor laser arrays.

Yu, NU↗

DOE OSTI2026

DEM Modeling and Validation of Pebble Bed Packing Using Chrono::GPU

Accurate prediction of pebble packing structure is important for pebble bed reactors because the spatial distribution of void fraction directly affects coolant flow, pressure drop, heat transfer, and neutronic behavior. However, experimentally validated DEM studies that directly evaluate local void-fraction structure in reactor-relevant pebble beds remain limited. In this work, the pebble bed experiment conducted at Missouri University of Science and Technology is simulated using the graphics processing unit (GPU)-based discrete element method (DEM) code Chrono::GPU. The study focuses on evaluating the ability of Chrono::GPU to reproduce the packing arrangement and void-fraction distribution of a randomly packed spherical pebble bed. The DEM results are first verified against established radial void-fraction correlations, including the Mueller and Vortmeyer-Schuster models, to assess the predicted bulk porosity, near-wall behavior, and oscillatory packing structure. The simulation is then verified against reference DEM data and validated against gamma-ray computed tomography (CT) experimental data at three axial locations. The Chrono::GPU results reproduce the main features of the experimental packing, including the high void fraction near the wall, the first near-wall trough, and the damped oscillatory radial profile caused by wall-induced ordering. Quantitative comparison with DEM data and the CT-based radial profiles shows good agreement, with mean absolute errors on the order of 0.07 and root-mean-square errors below 0.09 for the averaged profiles. These results demonstrate that Chrono::GPU can accurately capture the void-fraction structure of spherical pebble beds and provides a reliable DEM framework for future pebble bed reactor packing, recycling, and thermal-hydraulic studies.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2025

High compressive energy absorption and shape recovery behavior of additively manufactured textile-inspired cylindrical braided metamaterials

Mechanical metamaterials (MMs) are engineered structures with unique mechanical properties that arise from their unique spatial arrangement or lattice-like structure. The most commonly designed MMs such as honeycomb and re-entrant auxetics are prone to failure at the sharp corners and weak joints due to the increased stress concentration under deformation. To mitigate this challenge, braided MM structures involving intertwining threads of nylon—forming curved unit cells—have been studied. These textile-inspired cylindrical braided metamaterials (CBMMs) with contrasting unit cells, namely diamond and regular CBMMs, were fabricated by 3D printing. The layer-by-layer deposited structure built by fused filament fabrication delivered an assembly of overlapped threads that are fused at the contact point. To understand deformation behavior of these MMs, finite element models were developed for various load scenarios including quasi-static compression, cyclic and creep loads at room temperature. Stress distribution, deformation mechanisms, and failure modes were analyzed and validated by experiments to analyze the geometries and associated performance. The diamond CBMMs showed stress softening at 30 % compressive strain, withstanding a load of ∼440 N, whereas the regular CBMMs at 50 % strain experienced ∼250 N. The diamond CBMMs delivered higher creep resistance under sustained load and better energy absorption under cyclic loading than the regular CBMMs. The latter, however, exhibited 94 % shape recovery in contrast to 88 % recovery in former prototype during their first cyclic load. In conclusion, this study helps design mechanical lightweight devices that endure significant sustained load and exhibit enhanced energy absorption and shape recovery characteristics in cyclic loading.

Creep↗

DOE OSTI2023

Surface and subsurface oceanographic features drive forage fish distributions and aggregations: Implications for prey availability to top predators in the US Northeast Shelf ecosystem

Abstract Forage fishes are a critical food web link in marine ecosystems, aggregating in a hierarchical patch structure over multiple spatial and temporal scales. Surface‐level forage fish aggregations (FFAs) represent a concentrated source of prey available to surface‐ and shallow‐foraging marine predators. Existing survey and analysis methods are often imperfect for studying forage fishes at scales appropriate to foraging predators, making it difficult to quantify predator–prey interactions. In many cases, general distributions of forage fish species are known; however, these may not represent surface‐level prey availability to predators. Likewise, we lack an understanding of the oceanographic drivers of spatial patterns of prey aggregation and availability or forage fish community patterns. Specifically, we applied Bayesian joint species distribution models to bottom trawl survey data to assess species‐ and community‐level forage fish distribution patterns across the US Northeast Continental Shelf (NES) ecosystem. Aerial digital surveys gathered data on surface FFAs at two project sites within the NES, which we used in a spatially explicit hierarchical Bayesian model to estimate the abundance and size of surface FFAs. We used these models to examine the oceanographic drivers of forage fish distributions and aggregations. Our results suggest that, in the NES, regions of high community species richness are spatially consistent with regions of high surface FFA abundance. Bathymetric depth drove both patterns, while subsurface features, such as mixed layer depth, primarily influenced aggregation behavior and surface features, such as sea surface temperature, sub‐mesoscale eddies, and fronts influenced forage fish diversity. In combination, these models help quantify the availability of forage fishes to marine predators and represent a novel application of spatial models to aerial digital survey data.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

A network approach for multiscale catchment classification using traits

Abstract. The classification of river catchments into groups with similar biophysical characteristics is useful to understand and predict their hydrological behavior. The increasing availability of remote sensing and other large-scale geospatial datasets has enabled the use of advanced data-driven approaches to classify catchments using traits such as topography, geology, climate, land cover, land use, and human influence. Unsupervised clustering algorithms based on the Euclidean distance are commonly used for trait-based classification but are not suitable for highly dimensional data. In this study we present a new network-based method for multi-scale catchment classification, which can be applied to large datasets and used to determine the traits associated with different catchment groups. In this framework, two networks are analyzed in parallel: the first being where the nodes are traits and the second being where the nodes are catchments. In both cases, edges represent pairwise similarity, and a network cluster detection algorithm is used for the classification. The trait network is used to investigate redundancy in the trait data and to condense this information into a small number of interpretable categories. The catchments network is used to classify the catchments into clusters and to identify representative catchments for the different groups using the degree centrality metric. We apply this method to classify 9067 river catchments across the contiguous United States at both regional and continental scales using 274 non-categorical traits. At the continental scale, we identify 25 interpretable trait categories and 34 catchment clusters of sizes greater than 50. We find that catchments with similar trait categories are typically located in the same region, with different spatial patterns emerging among clusters dominated by natural and anthropogenic traits. We also find that the catchment clusters exhibit distinct hydrological behavior based on an analysis of streamflow indices. This network approach provides several advantages over traditional means of classification, including better separation of clusters, the use of alternate similarity metrics that are more suitable for highly dimensional data, and reducing redundancy in the trait information. The paired catchment–trait networks enable analysis of hydrological behavior using the dominant trait categories for each catchment cluster. The approach can be used at multiple spatial scales since the network topologies adjust automatically to reflect the trait patterns at the scale of investigation. Finally, the representative catchments identified as hub nodes in the network can be used to guide transferable observational and modeling strategies. The method is broadly applicable beyond hydrology for classification of other complex systems that utilize different types of trait datasets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Critical length scales for chemical heterogeneity at Cu/Nb 3D interfaces by atom probe tomography

Cu/Nb nanocomposites containing sharp, two-dimensional (2D) interfaces have outstanding strength but limited deformability. In contrast, Cu/Nb with three dimensional (3D) biphase interfaces exhibiting crystallographic, topological, and chemical variations in all spatial dimensions overcomes this limitation by simultaneously enhancing material strength and deformability. Here, while structural characterization of 3D interfaces has been performed to understand their mechanical behavior, three dimensional chemical characterization of such interfaces is lacking. In this work we quantify the local chemistry of 3D interfaces in Cu/Nb nanocomposites using atom probe tomography (APT). Our analysis demonstrates chemical heterogeneities along all spatial dimensions in 3D interfaces, establishes the length scale of such features, and quantifies the morphology of 3D interfaces. 3D interface heterogeneities form by surface diffusion during physical vapor deposition (PVD), suggesting that deposition parameters can be used to control interface structure and provide unique ways to explore processing-structure-property relationships in interface-dominated nanocomposites.

36 MATERIALS SCIENCE↗

DOE OSTI2023

The Role of SnO2 Processing on Ionic Distribution in Double-Cation-Double Halide Perovskites

Moving toward a future of efficient, accessible, and less carbon-reliant energy devices has been at the forefront of energy research innovations for the past 30 years. Metal-halide perovskite (MHP) thin films have gained significant attention due to their flexibility of device applications and tunable capabilities for improving power conversion efficiency. Serving as a gateway to optimize device performance, consideration must be given to chemical synthesis processing techniques. Therefore, how does common substrate processing techniques influence the behavior of MHP phenomena such as ion migration and strain? Here, we demonstrate how a hybrid approach of chemical bath deposition (CBD) and nanoparticle SnO2 substrate processing significantly improves the performance of (FAPbI3)0.97(MAPbBr3)0.03 by reducing micro-strain in the SnO2 lattice, allowing distribution of K+ from K-Cl treatment of substrates to passivate defects formed at the interface and produce higher current in light and dark environments. X-ray diffraction reveals differences in lattice strain behavior with respect to SnO2 substrate processing methods. Through use of conductive atomic force microscopy (c-AFM), conductivity is measured spatially with MHP morphology, showing higher generation of current in both light and dark conditions for films with hybrid processing. Additionally, time-of-flight secondary ionization mass spectrometry (ToF-SIMS) observed the distribution of K+ at the perovskite/SnO2 interface, indicating K+ passivation of defects to improve the power conversion efficiency (PCE) and device stability. We show how understanding the role of ion distribution at the SnO2 and perovskite interface can help reduce the creating of defects and promote a more efficient MHP device.

conductive atomic force microscopy↗

DOE OSTI2021

In-situ X-ray and visual observation of foam morphology and behavior at the batch-melt interface during melting of simulated waste glass

We report to attain a basic understanding of the primary foam structure and behavior, which affects the heat and mass transfer and the efficiency of the glass melting process, we investigated the primary foam layer under the glass batch floating on molten glass. The primary foam affects mass transfer during batch melting, in turn affecting the melting process. The recently performed direct in-situ three-dimensional X-ray computed tomography of the batch melting in a laboratory-scale melter vessel allowed us to visualize the features of the reacting batch layer and the foam that develops at its bottom, though with an insufficient resolution of images. In this study, we obtained better temporal and spatial resolution using the two-dimensional X-ray radiography and visual observation of the structure and behavior of transient primary foam as it formed and decayed. As soon as the batch was charged onto the melt surface, foam bubbles began to evolve, grow, and coalesce, forming a primary foam layer, 5-10 mm thick, within tens of seconds. This foam layer was sustained by ongoing gas evolving reactions counterbalanced by bubble coalescence into cavities that moved sideways and escaped to the atmosphere. Eventually, the entire remaining batch turned into foam that gradually decayed at the melt surface. The decay rate agreed with literature observations of surface foam produced by secondary foaming.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Spatially resolved measurements of plasma ion velocity distributions in a dipole magnetic field

The equilibrium flows of a plasma discharge in a dipole magnetic field are a topic of interest in low temperature plasma physics. Experimentalists typically rely on probe-based and line-integrated diagnostic techniques in these environments to describe plasma behavior. Presented here are measurements of argon ion dynamics with laser induced fluorescence techniques to provide insight into plasma dynamics in dipole magnetic fields with nonperturbative, spatially localized measurements. Simulation results from a Lagrangian approach to track particle orbits are compared to measured density profiles and provide evidence to support the mechanism distinguishing experimental configurations is the initial approach of particles. Applying a negative DC bias to the magnet induces strong E×B flows around the magnet, even exceeding the ion acoustic speed as measured far from the magnet. A strong enough bias also produces two distinct ion populations and provides a method for controlling the density gradient on the equator.

McLaughlin, Jacob W. (ORCID:0000000152661888)↗

DOE OSTI2023

Assessing individual movement, habitat use, and behavior of non-breeding marine birds in relation to prey availability in the US Atlantic

Resource availability is a key factor driving marine bird movements and distributions, but direct information on prey availability is difficult to obtain at relevant scales. We present novel methods for describing multi-scale trophic associations, combining movement analyses of marine birds with estimates of forage fish surface aggregations from digital aerial survey data and species occupancy from bottom trawl survey data. We analyzed satellite telemetry data from northern gannets Morus bassanus , red-throated loons Gavia stellata , and long-tailed ducks Clangula hyemalis in the US Atlantic during the non-breeding period. Using discrete-time hidden Markov models to distinguish area-restricted (i.e. putative foraging) from transit movements, we examined how environmental factors influence movement, and how forage fish species distributions and surface aggregations influence habitat use by gannets and loons that have greater dietary reliance. Our results suggest that chlorophyll a concentration significantly affected movement behavior across species, highlighting the importance of higher-productivity areas around estuaries during colder months when regional productivity is low. Though variable across species and seasons, spatial cross-correlation analysis revealed that herring species (Family Clupeidae), including Atlantic menhaden Brevoortia tyrannus , may be important resources; it also showed positive spatial correlations with forage fish aggregations. This suggests that prey patch dynamics and factors driving aggregation formation may be as important as species composition. However, spatial patterns were generally low (<0.3), suggesting a mismatch in spatiotemporal resolution, exemplifying the challenges in quantifying trophic relationships in marine systems. Disentangling predator-prey relationships is critical to understanding the mechanisms driving marine bird behavior in rapidly changing marine systems.

Environmental Sciences & Ecology↗

NASA NTRSDate not supplied

Aeroacoustic Study of a Subscale Large Civil Transport (STAR) Model – Part 1: Simulations

Results from a computational aeroacoustic study of the Subsonic Transport Aeroacoustic Research (STAR) model are presented. The model, a 26%-scale semispan Boeing 777-200 aircraft, was an inceptive configuration in a comprehensive simulation campaign to accurately predict the airframe noise of a full-scale, large civil transport during landing. The lengthy process of obtaining a high-fidelity, CFD-ready, vetted, digital representation of the model is described in detail. The simulations were performed with the STAR model installed in the NASA Ames 40- by 80-foot full-scale wind tunnel to enable direct comparisons with aerodynamic and acoustic measurements of the model, presented separately in Part 2 of the study. Two versions of the wind tunnel test section were considered. The entire test section was used to simulate the aerodynamic behavior of the model; an open-wall representation of the section was adopted during the acoustic simulations to eliminate wall reflections. To preserve the aerodynamic equivalency of the model for the two simulated tunnel configurations, the open-wall computations were executed at an angle-of-attack one degree higher than that of the corresponding closed-wall tunnel. The effect on aerodynamic behavior of several minor geometry alterations necessary to con-form the simulated to the tested model was assessed – the modifications considerably altered the strength and extent of a flow separation zone on the inboard segment of the flap. Also evaluated was the impact of spatial resolution on aerodynamic and acoustic characteristics of the model – in general, medium resolution was sufficient to establish convergence trends for averaged surface pressure behavior and proper localization of noise sources. After the aeroacoustic characteristics of the baseline configuration were predicted, a toboggan-shaped fairing designed to reduce main landing gear noise was evaluated computationally. Far-field noise spectra for the baseline and toboggan-equipped STAR model were calculated via a Ffowcs-Williams and Hawkings integral approach, with flow quantities on a permeable data surface enclosing the source regions used as input. Results from the simulations indicated that, while the fairing reduced gear noise in the near-field, it had a negligible effect on the far-field noise signature of the model. This trend agreed with previously published experimental results obtained during sub-scale, isolated gear tests and full-scale flight tests

airframe noise↗

NASA NTRS1986

Heat flow control and segregation in directional solidification: Development of an experimental and theoretical basis for Bridgman-type growth experiments in a microgravity environment

Within the framework of the proposed research, emphasis was placed on application of magnetic fields to semiconductor growth systems. It was found that magnetic fields up to 3 kGauss do not affect the growth behavior nor the macro-segregation behavior in the system Ge(Ga). Applied fields are found to significantlty alter the radial dopant distribution, which is attributed to alterations in the spatial orientation of convective cells. Increasing the magnetic field to 30 kGauss is found to have a fundamental effect on dopant segregation. Emphasis is also placed on the potential of KC-135 flights for preliminary studies on the effects of reduced gravity environments on the wetting behavior of semiconductor systems in growth configuration. The limited number of experiments conducted does not allow any conclusions on the merits of KC-135 flights for semiconductor processing research.

Witt, A. F.↗

DOE OSTI2025

Critical impact of experimentally-driven strut level anisotropic material models in advanced stress analysis of additively manufactured lattice structures

The rapid acceleration in materials discovery may overshadow the importance of thoroughly understanding the mechanical performance of newly developed materials in demanding environments. The recent interest in combining parametric studies with machine learning techniques to explore how changes in specific processing parameters or model inputs affect the overall behavior of a material system can only be truly beneficial if the governing constitutive relations describing material behavior are accurately established. In this study, we demonstrate the critical impact of accurately representing strut-level anisotropic material behavior in advanced stress analysis of additively manufactured lattice structures (AMLS). We introduce a systematic experimental and modeling approach for developing strut-level anisotropic elastoplastic material models that account for the influence of microstructural features such as porosity, texture, and surface roughness on the development of local anisotropic mechanical properties, which vary with strut orientation relative to the build direction (BD). As a result the presented material model captures and relates the statistics of spatially varying struts’ microstructural features to the local stress distribution. Our findings suggest that incorporating strut-level anisotropic material behavior into unit cell analysis significantly influences the load distribution and evolution of local stresses within the structure. Therefore, accounting for this anisotropy is critical for developing an understanding of unit cell behavior and performance, including subsequent topology/component design optimization based on this analysis.

Sahoo, Subhadip [University of Arizona]↗