SEARCH · Engineering Papers

Results for “network alignment”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11

All sources NASA NTRS DOE OSTI

DOE OSTI2017

Reactive polymer fused deposition manufacturing

Methods and compositions for additive manufacturing that include reactive or thermosetting polymers, such as urethanes and epoxies. The polymers are melted, partially cross-linked prior to the depositing, deposited to form a component object, solidified, and fully cross-linked. These polymers form networks of chemical bonds that span the deposited layers. Application of a directional electromagnetic field can be applied to aromatic polymers after deposition to align the polymers for improved bonding between the deposited layers.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Bergman-type medium range order in amorphous Zr77Rh23 alloy studied by ab initio molecular dynamics simulations

In recent years, some arguments about the existence of medium-range order (MRO) in the Zr-Rh system have been put forward. However, research on the structural features of the Zr-Rh binary alloy at the atomic level is still lacking. This study uses ab initio molecular dynamics simulations to systematically study the local structures of Zr77Rh23 from the liquid to the glassy states. Pair correlation function (PCF), coordination number (CN), Honeycutt–Anderson(HA) index, bond-angle distribution functions, and the Voronoi tessellation method are used to reveal a clear icosahedral-like configuration in the amorphous Zr77Rh23 alloy. It is noteworthy that the splitting in the second peak of the partial PDF implies the existence of a medium range order (MRO) in the Zr77Rh23 system. We obtain the local order in three-dimensional atomic density distributions by using a new atomistic cluster alignment (ACA) method. Interestingly, a Bergman-type MRO is observed in the glassy Zr77Rh23. Furthermore, the spatial distribution and connections of icosahedral-like clusters are shown to further demonstrate the MRO network. Our findings shed light on the nature of atomic local structures of amorphous Zr77Rh23 alloy and have important implications to understanding the formation of various MROs in metallic glasses.

Guo, Y. R.↗

DOE OSTI2015

Erratum: Evolution of precipitate morphology during heat treatment and its implications for the superconductivity in K x F e 1.6 + y S e 2 single crystals [Phys. Rev. B 86 , 144507 (2012)]

In this article, we study the relationship between precipitate morphology and superconductivity in K x Fe 1.6+y Se 2 single crystals grown by self-flux method. Scanning electron microscopy (SEM) measurements revealed that the superconducting phase forms a network in the samples quenched above iron vacancy order-disorder transition temperature T s , whereas it aggregates into micrometer-sized rectangular bars and aligns as disconnected chains in the furnace-cooled samples.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Massive all-atom analysis of 2D materials with quantum properties (Final report)

Improvements in microscopy have enabled the acquisition of data at a scale that is difficult to process manually, making automated machine learning approaches to analyzing experimental images essential. In this project, we developed and applied machine learning (ML) workflows for atomic resolution scanning transmission electron microscopy (STEM) images. This development included improving both methodology as well as generating user-friendly codes. We developed machine learning architectures which, after training, automatically identify the location and types of defects throughout a material. We used these data to produce class-averaged images of 2D atomic coordinates with up to 0.3 pm precision, uncovering the structure and oscillations of long-range strain fields around point defects in WSe 2-2x Te 2x . We also resolved a long-standing problem in this field in the training of ML models, a lack of labeled experimental data, by developing a cycle-GAN that transformed simulated-generated labeled data into labeled data indistinguishable from experiment and therefore suitable for training. This removed the remaining parts of the ML data processing workflow where human intervention was still critical and therefore a bottleneck to working at scale. Codes have been developed and released for this full machine learning workflow. ML approaches to partially automate STEM acquisition were also developed. Finally we applied ML and other advanced data processing methods to several materials science problems in two-dimensional materials, including studying the evolution of hyperuniformity with defect concentration in WSe2, understanding phase transformations in transition metal dichalcogenides during in-situ heating in the STEM, and exploring how 2D interfaces transform from twisted into aligned structures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2010

CdSe Nanorods Dominate Photocurrent of Hybrid CdSe-P3HT Photovoltaic Cell

Photovoltaic devices based on organic semiconductors require charge-separating networks (bulk heterojunctions) for optimal performance. Here we report on the fabrication of organic-inorganic photovoltaic devices with tailored (n-type) CdSe nanorod arrays aligned perpendicularly to the substrate. The nanorod lengths varied from 58 ± 12 to 721 ± 15 nm, while the diameters and inter-rod spacings were kept constant at 89.5 ± 7.5 and 41.3 ± 9.9 nm, respectively. Short-circuit densities improved linearly with nanorod length, resulting in power conversion efficiencies of up to 1.38% for cells with nanorods 612 ± 46 nm long. Notably, the cell’s efficiency was dominated by exciton generation in the CdSe nanorods.

14 SOLAR ENERGY↗

DOE OSTI2011

The effect of the target structure and composition on the ejection and transport of polymer molecules and carbon nanotubes in matrix-assisted pulsed laser evaporation

Matrix-assisted pulsed laser evaporation (MAPLE) is a prominent member of a broad and expanding class of laser-driven deposition techniques where a matrix of volatile molecules absorbs laser irradiation and provides the driving force for the ejection and transport of the material to be deposited. The mechanisms of MAPLE are investigated in coarse-grained molecular dynamic simulations focused on establishing the physical regimes and limits of the molecular transfer from targets with different structures and compositions. The systems considered in the simulations include dilute solutions of polymer molecules and individual carbon nanotubes (CNTs), as well as continuous networks of carbon nanotubes impregnated with solvent. The polymer molecules and nanotubes are found to be ejected only in the ablation regime and are incorporated into matrix-polymer droplets generated in the process of the explosive disintegration of the overheated matrix. The ejection and deposition of droplets explain the experimental observations of complex surface morphologies in films deposited by MAPLE. In simulations performed for MAPLE targets loaded with CNTs, the ejection of individual nanotubes, CNT bundles, and tangles with sizes comparable or even exceeding the laser penetration depth is observed. The ejected CNTs align along the flow direction in the matrix plume and tend to agglomerate into bundles at the initial stage of the ablation plume expansion. In a large-scale simulation performed for a target containing a network of interconnected CNT bundles, a large tangle of CNT bundles with the total mass of 50 MDa is separated from the continuous network and entrained with the matrix plume. No significant splitting and thinning of CNT bundles in the ejection process is observed in the simulations, suggesting that fragile structural elements or molecular agglomerates with complex secondary structures may be transferred and deposited to the substrate with the MAPLE technique.

Materials Science↗

DOE OSTI2020

Prototypical Region Proposal Networks for Few-Shot Localization and Classification

Recently proposed few-shot image classification methods have generally focused on use cases where the objects to be classified are the central subject of images. Despite success on benchmark vision datasets aligned with this use case, these methods typically fail on use cases involving densely-annotated, busy images: images common in the wild where objects of relevance are not the central subject, instead appearing potentially occluded, small, or among other incidental objects belonging to other classes of potential interest. To localize relevant objects, we employ a prototype-based few-shot segmentation model which compares the encoded features of unlabeled query images with support class centroids to produce region proposals indicating the presence and location of support set classes in a query image. These region proposals are then used as additional conditioning input to few-shot image classifiers. We develop a framework to unify the two stages (segmentation and classification) into an end-to-end classification model---PRoPnet---and empirically demonstrate that our methods improve accuracy on image datasets with natural scenes containing multiple object classes.

Skomski, Steven E.↗

DOE OSTI2025

DS-TIDE: Harnessing Dynamical Systems for Efficient Time-Independent Differential Equation Solving

Time-Independent Differential Equations (TIDEs) are central to modeling equilibrium behavior across a wide range of scientific and engineering domains, from electrostatics to porous media flow. Conventional numerical solvers offer reliable solutions but incur significant computational costs due to fine-grained discretization and iterative procedures. Machine learning-based approaches address this by replacing iterative solving processes with one-time inference; however, their sophisticated models require extensive training resources that often exceed those of traditional solvers. Consequently, designing a TIDE solver that achieves high accuracy, broad applicability, and exceptional computational efficiency remains a fundamental challenge. In this paper, we propose DS-TIDE, a novel hardware solver that is inspired by, and subsequently leverages, the intrinsic connection between Dynamical Systems (DS) and Differential Equations (DEs) to efficiently and accurately solve TIDEs. DS-TIDE employs a CMOS-compatible DS-based processor, whose physical states evolve under carefully designed DE-driven dynamics and naturally converge to equilibrium -- the solution of the target TIDE -- within ~1µs on a ~1-watt DS-TIDE processor. To enhance expressivity, DS-TIDE incorporates Heterogeneous Dynamics with Temporal Layering (HDTL), which solves TIDEs through a three-stage DS evolution -- conditioning, solving, and decoding -- each governed by specialized dynamics. The entire evolution process is analogous to an infinitely deep neural network temporally unrolled, offering the system the capability of representing complex equations. Furthermore, DS-TIDE is equipped with an on-device DS-DE Auto-Alignment mechanism that dynamically adapts intrinsic hardware dynamics within milliseconds, effectively aligning the system’s dynamics to diverse target DEs. Experimental results across TIDEs from a wide range of scientific and engineering domains demonstrate that DS-TIDE achieves ~10^3× speedup, ~10^5× energy savings, and competitive or superior accuracy compared to state-of-the-art numerical and ML-based solvers.

Liu, Chuan↗

DOE OSTI2024

Atomic-Scale Scanning of Domain Network in the Ferroelectric HfO 2 Thin Film

Ferroelectric HfO 2 -based thin films have attracted much interest in the utilization of ferroelectricity at the nanoscale for next-generation electronic devices. However, the structural origin and stabilization mechanism of the ferroelectric phase are not understood because the film is typically nanocrystalline with active yet stochastic ferroelectric domains. Here, in this study, electron microscopy is used to map the in-plane domain network structures of epitaxially grown ferroelectric Y:HfO 2 films in atomic resolution. The ferroelectricity is confirmed in free-standing Y:HfO 2 films, allowing for investigating the structural origin for their ferroelectricity by 4D-STEM, high-resolution STEM, and iDPC-STEM. At the grain boundaries of <111>-oriented Pca2 1 orthorhombic grains, a high-symmetry mixed-(R3m, Pnm2 1 ) phase is induced, exhibiting enhanced polarization due to in-plane compressive strain. Nanoscale Pca2 1 orthorhombic grains and their grain boundaries with mixed-(R3m, Pnm2 1 ) phases of higher symmetry cooperatively determine the ferroelectricity of the Y:HfO 2 film. It is also found that such ferroelectric domain networks emerge when the film thickness is beyond a finite value. Furthermore, in-plane mapping of oxygen positions overlaid on ferroelectric domains discloses that polarization is suppressed at vertical domain walls, while it is active when domains are aligned horizontally with subangstrom domain walls. In addition, randomly distributed 180° charged domain walls are confined by spacer layers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Cooling rate dependence of structural order in Al{sub 90}Sm{sub 10} metallic glass

The atomic structure of Al{sub 90}Sm{sub 10} metallic glass is studied using molecular dynamics simulations. By performing a long sub-T{sub g} annealing, we developed a glass model closer to the experiments than the models prepared by continuous cooling. Using the cluster alignment method, we found that “3661” cluster is the dominating short-range order in the glass samples. The connection and arrangement of “3661” clusters, which define the medium-range order in the system, are enhanced significantly in the sub-T{sub g} annealed sample as compared with the fast cooled glass samples. Unlike some strong binary glass formers such as Cu{sub 64.5}Zr{sub 35.5}, the clusters representing the short-range order do not form an interconnected interpenetrating network in Al{sub 90}Sm{sub 10,} which has only marginal glass formability.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Operating-Envelopes-Aware Decentralized Welfare Maximization for Energy Communities

We propose an operating-envelope-aware, prosumer-centric, and efficient energy community that aggregates individual and shared community distributed energy resources and transacts with a regulated distribution system operator (DSO) under a generalized net energy metering tariff design. To ensure safe network operation, the DSO imposes dynamic export and import limits, known as dynamic operating envelopes, on end-users' revenue meters. Given the operating envelopes, we propose an incentive-aligned community pricing mechanism under which the decentralized optimization of community members' benefit implies the optimization of overall community welfare. The proposed pricing mechanism satisfies the cost-causation principle and ensures the stability of the energy community in a coalition game setting. Numerical examples provide insights into the characteristics of the proposed pricing mechanism and quantitative measures of its performance.

distributed energy resources aggregation↗

DOE OSTI2014

Death and Transfiguration in Static Staphylococcus epidermidis Cultures

The overwhelming majority of bacteria live in slime embedded microbial communities termed biofilms, which are typically adherent to a surface. However, when several Staphylococcus epidermidis strains were cultivated in static liquid cultures, macroscopic aggregates were seen floating within the broth and also sedimented at the test tube bottom. Light- and electron microscopy revealed that early-stage aggregates consisted of bacteria and extracellular matrix, organized in sheet-like structures. Perpendicular under the sheets hung a network of periodically arranged, bacteria-associated strands. During the extended cultivation, the strands of a subpopulation of aggregates developed into cross-connected wall-like structures, in which aligned bacteria formed the walls. The resulting architecture had a compartmentalized appearance. In late-stage cultures, the wall-associated bacteria disintegrated so that, henceforth, the walls were made of the coalescing remnants of lysed bacteria, while the compartment-like organization remained intact. At the same time, the majority of strand containing aggregates with associated culturable bacteria continued to exist. These observations indicate that some strains of Staphylococcus epidermidis are able to build highly sophisticated structures, in which a subpopulation undergoes cell lysis, presumably to provide continued access to nutrients in a nutrient-limited environment, whilst maintaining structural integrity.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Machine-Learning-Based Multiscale Methods for 3D Modelling of Granular Materials by Incorporating History-Dependent State Variables

Over the past decades, the prevalence of machine learning (ML) methods has made the development of ML-based constitutive models for granular materials undoubtedly a popular subject. Numerous studies have been made to feature the loading path or history-dependent stress-strain response of granular media using neural networks. In this work, a novel finite element method (FEM)–ML multiscale approach was developed by incorporating internal variables to improve the simulation accuracy of 3D history-dependent granular materials for the first time. To this end, a surrogate constitutive model based on the single-step-based multi-layer perceptron (MLP) neural network was used to replace representative volume element (RVE) simulations conducted by the discrete element method (DEM) in the multiscale FEM–DEM approach. Although the prediction principle of the MLP aligns with the FEM algorithm, artificially added internal variables are required to differentiate the loading history. To address this issue, history variables associated with the Frobenius norm are proposed to be fed into the MLP coupled with the strain tensor to extract the history-dependent behaviour of granular assemblies. The developed FEM–ML approach was demonstrated in 3D conventional triaxial compression (CTC) simulations. Compared to the multiscale FEM–DEM approach, the proposed FEM–ML method exhibits a significantly improved computational efficiency.

granular materials↗

DOE OSTI2025

Hyperspectral segmentation of plants in fabricated ecosystems

Hyperspectral imaging provides a powerful tool for analyzing above-ground plant characteristics in fabricated ecosystems, offering rich spectral information across diverse wavelengths. This study presents an efficient workflow for hyperspectral data segmentation and subsequent data analytics, minimizing the need for user annotation through the use of ensembles of sparse mixed scale convolution neural networks. The segmentation process leverages the diversity of ensembles to achieve high accuracy with minimal labeled data, reducing labor-intensive annotation efforts. To further enhance robustness, we incorporate image alignment techniques to address spatial variability in the dataset. Downstream analysis focuses on using the segmented data for processing spectral data, enabling monitoring of plant health. This approach provides a scalable solution for spectral segmentation, and facilitates actionable insights into plant conditions in complex, controlled environments. Our results demonstrate the utility of combining advanced machine learning techniques with hyperspectral analytics for high-throughput plant monitoring.

Zwart, Petrus H.↗

DOE OSTI2021

A General Framework to Learn Tertiary Structure for Protein Sequence Characterization

During the past five years, deep-learning algorithms have enabled ground-breaking progress towards the prediction of tertiary structure from a protein sequence. Very recently, we developed SAdLSA, a new computational algorithm for protein sequence comparison via deep-learning of protein structural alignments. SAdLSA shows significant improvement over established sequence alignment methods. In this contribution, we show that SAdLSA provides a general machine-learning framework for structurally characterizing protein sequences. By aligning a protein sequence against itself, SAdLSA generates a fold distogram for the input sequence, including challenging cases whose structural folds were not present in the training set. About 70% of the predicted distograms are statistically significant. Although at present the accuracy of the intra-sequence distogram predicted by SAdLSA self-alignment is not as good as deep-learning algorithms specifically trained for distogram prediction, it is remarkable that the prediction of single protein structures is encoded by an algorithm that learns ensembles of pairwise structural comparisons, without being explicitly trained to recognize individual structural folds. As such, SAdLSA can not only predict protein folds for individual sequences, but also detects subtle, yet significant, structural relationships between multiple protein sequences using the same deep-learning neural network. The former reduces to a special case in this general framework for protein sequence annotation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Multilayered Network Models for Security: Enhancing System Security Engineering with Orchestration

Security engineering approaches can often focus on a particular domain—physical security, cyber security, or personnel security, for example. Yet, security systems engineering consistently faces challenges requiring socio-technical solutions to address evolving and dynamic complexity. While some drivers of this complexity stem from complex risk environments, innovative adversaries, and disruptive technologies, other drivers are endogenous and emerge from the interactions across security engineering approaches. In response, INCOSE's Systems Security Working Group identified the need to better coordinate “disparate security solutions [that] operate independently” as one of eleven key concepts in their IS21 FuSE Security Roadmap. From this perspective, this need for “security orchestration” aligns with the perspective that security is a property that emerges from interactions within complex systems. Current efforts at Sandia National Laboratories are developing a systems security engineering approach that describes high consequence facility (HCF) security as a multidomain set of interacting layers. The result is a multilayered network (MLN)-based approach that captures the interactions between infrastructure, physical components, digital components, and humans in nuclear security systems. This article will summarize the MLN-based approach to HCF security and describe two preliminary results demonstrating potential benefits from incorporating interactions across disparate security solutions. Here, leveraging the logical structure of networks, this MLN model-based approach provides an example of how security orchestration provides enhanced systems security engineering solutions.

42 ENGINEERING↗

DOE OSTI2022

Anisotropic Thermally Conductive Composites Enabled by Acoustophoresis and Stereolithography

Opportunities to improve thermal management in electronic devices are currently hindered by processing constraints that limit thermal conductivity in polymer-matrix composites. Active patterning of filler particles is a promising route to improve conductivity while retaining processability by improving particle contact density and directing heat along optimized pathways. Here, we employ acoustic patterning to align and compact filler particles into stripes during stereolithographic 3D printing. This approach produces polymer-based composite materials with highly efficient embedded heat transport pathways which reach 95 vol% particle utilization (relative to the parallel conduction upper limit). These composites exhibit anisotropic thermal conductivity up to 300% higher than unpatterned composites, with in-plane anisotropy ratios of up to 350%. Combining this high conductivity with 3D printing enables materials with engineered heat networks that optimize transport from hot spots to heatsinks while maintaining low viscosity for fast particle patterning and for infiltration around electronic components. Finally, numerical simulations of acoustic assembly of particles with varied geometry, when compared to experimentally characterized particle packing, illuminate pathways for further improving conductivity by optimizing particle geometry for alignment and stacking of particles with maximum contact surface area.

36 MATERIALS SCIENCE↗

DOE OSTI2014

Preliminary study of slow and fast ultrasonic waves using MR images of trabecular bone phantom

Cancellous bone is a complex tissue that performs physiological and biomechanical functions in all vertebrates. It is made up of trabeculae that, from a simplified structural viewpoint, can be considered as plates and beams in a hyperstatic structure that change with time leading to osteoporosis. Several methods has been developed to study the trabecular bone microstructure among them is the Biot’s model which predicts the existence of two longitudinal waves in porous media; the slow and the fast waves, that can be related to porosity of the media. This paper is focused on the experimental detection of the two Biot’s waves of a trabecular bone phantom, consisting of a trabecular network of inorganic hydroxyapatite. Experimental measurements of both waves were performed using through transmission ultrasound. Results had shown clearly that the propagation of two waves propagation is transversal to the trabecular alignment. Otherwise the waves are overlapped and a single wave seems to be propagated. To validate these results, magnetic resonance images were acquired to assess the trabecular direction, and to assure that the pulses correspond to the slow and fast waves. This approach offers a methodology for non-invasive studies of trabecular bones.

60 APPLIED LIFE SCIENCES↗