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 163 records · Page 9

All sources NASA NTRS DOE OSTI

DOE OSTI2013

Survey and Alignment Reference Data Generation and Deviation Reporting

The data used by survey group can be categorized into design data and measured data based on how it’s obtained. Design data is a simple term which refers to the ideal beam orbit which is given by physics group in the form of a series of coordinates in lattice frame. Comparatively, measured data is a complicated term which includes the fiducialization data of each component, girder integration data, girder profiling data and control network data etc. There are a lot of steps and techniques to make sure a precise and successful alignment. However, from pure data perspective, making up right reference data and reporting misalignment correctly are 2 keys of survey and alignment. The note will give an overview regarding how survey data will be generated and reported during key steps. More details should be checked in the referred documents or discussed specifically.

36 MATERIALS SCIENCE↗

NASA NTRS2004

Examples of Current and Future Uses of Neural-Net Image Processing for Aerospace Applications

Feed forward artificial neural networks are very convenient for performing correlated interpolation of pairs of complex noisy data sets as well as detecting small changes in image data. Image-to-image, image-to-variable and image-to-index applications have been tested at Glenn. Early demonstration applications are summarized including image-directed alignment of optics, tomography, flow-visualization control of wind-tunnel operations and structural-model-trained neural networks. A practical application is reviewed that employs neural-net detection of structural damage from interference fringe patterns. Both sensor-based and optics-only calibration procedures are available for this technique. These accomplishments have generated the knowledge necessary to suggest some other applications for NASA and Government programs. A tomography application is discussed to support Glenn's Icing Research tomography effort. The self-regularizing capability of a neural net is shown to predict the expected performance of the tomography geometry and to augment fast data processing. Other potential applications involve the quantum technologies. It may be possible to use a neural net as an image-to-image controller of an optical tweezers being used for diagnostics of isolated nano structures. The image-to-image transformation properties also offer the potential for simulating quantum computing. Computer resources are detailed for implementing the black box calibration features of the neural nets.

Decker, Arthur J.↗

DOE OSTI2026

Comparison of structurally diverse simulation models for prediction of epidemic outcomes caused by a long-distance dispersed pathogen

Long-distance dispersal (LDD) pathogens pose substantial challenges for epidemic control due to their ability to generate new infection foci at great distances. While various modeling approaches have been developed to understand and manage such outbreaks, little work has compared how models of different structures behave under shared conditions. Here, in this study, we compare four structurally distinct epidemiological models — EPIMUL, GEMF, PoPS, and Warwick — each adapted to simulate the spread of wheat stripe rust (WSR), a wind-dispersed LDD pathogen, under identical epidemiological parameters and dispersal kernel. Using data from a controlled field experiment, we evaluate the ability of each model to replicate disease prevalence under nine intervention scenarios that vary in timing and culling area. While the models differ substantially in design — ranging from spatial grid-based to network-based and raster-based frameworks — the shared dispersal kernel allowed for close alignment in their predictions. All models accurately captured general epidemic trends, particularly the strong effect of early intervention on disease suppression. We qualitatively compared their behavioral responses across scenarios and also evaluated an ensemble prediction by averaging across model outputs. Our findings highlight how integrating shared epidemiological components into distinct modeling frameworks can improve consistency and accuracy, while reinforcing the importance of early culling in managing LDD pathogen outbreaks.

Dispersal kernel↗

DOE OSTI2018

Nanoscale structure, mechanics and growth of epidermal cell walls

This article briefly reviews recent advances in nano-scale and micro-scale assessments of primary cell wall structure, mechanical behaviors and expansive growth. Cellulose microfibrils have hydrophobic and hydrophilic faces which may selectively bind different matrix polysaccharides and adjacent microfibrils. Furthermore, these distinctive binding interactions may guide partially aligned cellulose microfibrils in primary cell walls to form a planar, load-bearing network within each lamella of polylamellate walls. Consideration of expansive growth of cross-lamellate walls leads to a surprising inference: side-by-side sliding of microfibrils may be a key rate-limiting physical step, potentially targeted by specific wall loosening agents. Atomic force microscopy shows different patterns of microfibril movement during force-driven extension versus enzymatic loosening. Consequently, simulations of cell growth as elastic deformation of isotropic cell walls may need to be augmented to incorporate the distinctive behavior of growing cell walls.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Learning an Algebriac Multrigrid Interpolation Operator Using a Modified GraphNet Architecture

This work, building on previous efforts, develops a suite of new graph neural network machine learning architectures that generate data-driven prolongators for use in Algebraic Multigrid (AMG). Algebraic Multigrid is a powerful and common technique for solving large, sparse linear systems. Its effectiveness is problem dependent and heavily depends on the choice of the prolongation operator, which interpolates the coarse mesh results onto a finer mesh. Previous work has used recent developments in graph neural networks to learn a prolongation operator from a given coefficient matrix. In this paper, we expand on previous work by exploring architectural enhancements of graph neural networks. A new method for generating a training set is developed which more closely aligns to the test set. Asymptotic error reduction factors are compared on a test suite of 3-dimensional Poisson problems with varying degrees of element stretching. Results show modest improvements in asymptotic error factor over both commonly chosen baselines and learning methods from previous work.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1999

Advanced Communications Technology Satellite Now Operating in an Inclined Orbit

The Advanced Communications Technology Satellite (ACTS) system has been modified to support operation in an inclined orbit that is virtually transparent to users, and plans are to continue this final phase of its operation through September 2000. The next 2 years of ACTS will provide a new opportunity for using the technologies that this system brought online over 5 years ago and that are still being used to resolve the technical issues that face NASA and the satellite industry in the area of seamless networking and interoperability with terrestrial systems. New goals for ACTS have been defined that align the program with recent changes in NASA and industry. ACTS will be used as a testbed to: Show how NASA and other Government agencies can use commercial systems for 1. future support of their operations Test, characterize, and resolve technical issues in using advanced communications 2. protocols such as asynchronous transfer mode (ATM) and transmission control protocol/Internet protocol (TCP/IP) over long latency links as found when interoperating satellites with terrestrial systems Evaluate narrow-spot-beam Ka-band satellite operation in an inclined orbit 3. Verify Ka-band satellite technologies since no other Ka-band system is yet 4. available in the United States

Bauer, Robert A.↗

DOE OSTI2025

Introducing a multiscale feature integration network for inpainting with applications to enhanced CMB map reconstruction

We introduce a novel neural network, SkyReconNet, which combines the expanded receptive fields of dilated convolutional layers along with standard convolutions, to capture both the global and local features for reconstructing the missing information in an image. We implement our network to inpaint the masked regions in a full-sky cosmic microwave background (CMB) map. Inpainting CMB maps is a particularly formidable challenge when dealing with extensive and irregular masks, such as galactic masks which can obscure substantial fractions of the sky. The hybrid design of SkyReconNet leverages the strengths of standard and dilated convolutions to accurately predict CMB fluctuations in the masked regions by effectively utilizing the information from surrounding unmasked areas. During training, the network optimizes its weights by minimizing a composite loss function that combines the structural similarity index measure (SSIM) and mean squared error (MSE). SSIM preserves the essential structural features of the CMB, ensuring an accurate and coherent reconstruction of the missing CMB fluctuations, while MSE minimizes the pixelwise deviations, thus enhancing the overall accuracy of the predictions. The predicted CMB maps and their corresponding angular power spectra align closely with the targets, achieving the performance limited only by the fundamental uncertainty of cosmic variance. The network’s generic architecture enables application to other physics-based challenges involving data with missing or defective pixels, systematic artifacts, etc. In conclusion, our results demonstrate its effectiveness in addressing the challenges posed by large irregular masks, offering a significant inpainting tool not only for CMB analyses but also for image-based experiments across disciplines where such data imperfections are prevalent.

Cosmic microwave background↗

DOE OSTI2026

Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy

Training machine learning models for radioisotope identification using gamma spectroscopy remains an elusive challenge for many practical applications, largely stemming from the difficulty of acquiring and labeling large, diverse experimental datasets. Simulations can mitigate this challenge, but the accuracy of models trained on simulated data can deteriorate substantially when deployed to an out-of-distribution operational environment. In this study, we demonstrate that unsupervised domain adaptation (UDA) can improve the ability of a model trained on synthetic data to generalize to a new testing domain, provided unlabeled data from the target domain are available. Conventional supervised techniques are unable to utilize this data because the absence of isotope labels precludes defining a supervised classification loss. Instead, we first pretrain a spectral classifier using labeled synthetic data and subsequently leverage unlabeled target data to align the learned feature representations between the source and target domains. We compare a range of different UDA techniques, finding that minimizing the maximum mean discrepancy (MMD) between source and target feature vectors yields the most consistent improvement to testing scores. For instance, using a custom transformer-based neural network, we achieved a testing accuracy of $0.904 \pm 0.022$ on an experimental LaBr test set after performing unsupervised feature alignment via MMD minimization, compared to $0.754 \pm 0.014$ before alignment. Overall, our results highlight the potential of using UDA to adapt a radioisotope classifier trained on synthetic data for real-world deployment.

Lalor, Peter W.↗

DOE OSTI2023

Utah FORGE: 2023 Large Upscaled Discrete Fracture Network Models

This dataset includes the data and a report on the large upscaled discrete fracture network modeling done for the Utah FORGE project in 2023. The FORGE modeling team is making five discrete fracture network (DFN) realizations of a large reservoir model available to researchers. These models have been upscaled to a continuum mesh or grid at resolutions of 10 meters and 20 meters providing reservoir properties for fracture porosity, permeability, and compressibility. The models are available in both the reference global coordinate frame and a local coordinate frame aligned with principal stress directions.

15 GEOTHERMAL ENERGY↗

DOE OSTI2017

Community detection in sequence similarity networks based on attribute clustering

Networks are powerful tools for the presentation and analysis of interactions in multi-component systems. A commonly studied mesoscopic feature of networks is their community structure, which arises from grouping together similar nodes into one community and dissimilar nodes into separate communities. Here in this paper, the community structure of protein sequence similarity networks is determined with a new method: Attribute Clustering Dependent Communities (ACDC). Sequence similarity has hitherto typically been quantified by the alignment score or its expectation value. However, pair alignments with the same score or expectation value cannot thus be differentiated. To overcome this deficiency, the method constructs, for pair alignments, an extended alignment metric, the link attribute vector, which includes the score and other alignment characteristics. Rescaling components of the attribute vectors qualitatively identifies a systematic variation of sequence similarity within protein superfamilies. The problem of community detection is then mapped to clustering the link attribute vectors, selection of an optimal subset of links and community structure refinement based on the partition density of the network. ACDC-predicted communities are found to be in good agreement with gold standard sequence databases for which the "ground truth" community structures (or families) are known. ACDC is therefore a community detection method for sequence similarity networks based entirely on pair similarity information. A serial implementation of ACDC is available from https://cmb.ornl.gov/resources/developments

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Tribal Colleges and Universities/Advanced Manufacturing Network Initiative Phase II Sandia Technical Assistance (Q2 FY2021 Progress Report)

The National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) to 1) align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA’s enduring technical workforce at the laboratories and production plants, and 2) to enhance research and education at under-represented colleges and universities. Out of this effort, MSIPP launched a new consortium in early FY17 focused on Tribal Colleges and Universities (TCUs) known as the Advanced Manufacturing Network Initiative (AMNI). This consortium has been extended for FY20 and FY21. The following report summarizes the status update during this quarter.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Tribal Colleges and Universities/Advanced Manufacturing Network Initiative Phase II Sandia Technical Assistance FY21 Q3 Progress Report

42 ENGINEERING↗

DOE OSTI2021

Tribal Colleges and Universities/Advanced Manufacturing Network Initiative: Phase II Sandia Technical Assistance (FY21 Q4 Progress Report)

The National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) 1) to align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA’s enduring technical workforce at the laboratories and production plants, and 2) to enhance research and education at under-represented colleges and universities. Out of this effort, MSIPP launched a new consortium in early FY17 focused on Tribal Colleges and Universities (TCUs) known as the Advanced Manufacturing Network Initiative (AMNI). This consortium has been extended for FY20 and FY21. The following report summarizes the status update during this quarter.

42 ENGINEERING↗

DOE OSTI2013

Self-assembly of a new type of periodic surface structure in a copolymer by excimer laser irradiation above the ablation threshold

We report self-assembly of periodic surface structures in a commercial block copolymer (BCP) (Filofocon A) upon irradiation with a few tens of excimer laser pulses (20 ns, 193 nm) at fluences above the ablation threshold. This new type of structures is characterized by much larger periods than those characteristic for Laser-Induced Periodic Surface Structures (LIPSS) and features nanochains instead of ripples. We find a period of 790 nm at 400 mJ/cm{sup 2}, scaling linearly with laser fluence up to a maximum of 1.0 μm. While an entangled random network of nanochains is produced for normal-incidence and non-polarized light, nanochain alignment can be achieved either by irradiation at an angle or by using linearly polarized light, forming a lamella-like structure. In both cases, the nanochains are aligned parallel to the penetrating polarization orientation and their period does not show a dependence on the angle of incidence, as opposed to the general behavior of standard LIPSS. Also, our results show that the chains are not formed by frozen capillary waves. In contrast, we show analogies of the nanochains produced to lamellar structures fabricated on a smaller scale in other BCP. We discuss the origin of the self-assembly process in terms of a combination of chemical (BCP), optical (surface scattering), and thermal (melting, coarsening, and ablation) effects.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Ferromagnetic resonance in single vertices and 2D lattices macro-dipoles of elongated nanoelements: measurements and simulations

We report broadband ferromagnetic resonance measurements of the in-plane magnetic field response of three- and four-fold symmetric vertices formed by non-contacting permalloy nano-ellipses together with extended lattices constructed from them. Complementing the experimental data with simulations, we are able to show that, as far as the most intense FMR responses are concerned, the spectra of vertices and lattices can largely be interpreted in terms of a superposition of the underlying hysteretic responses of the individual ellipses, as elemental building blocks of the system. Here, this property suggest that it is possible to understand the orientation of the individual magnetic dipole moments in a dipole network in terms of dynamic measurements alone, thereby offering a powerful tool to analyze the alignment statistics in frustrated systems that are exposed to various magnetic histories.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Automating Traffic Microsimulation from SYNCHRO UTDF to SUMO

Modern transportation research relies on seamlessly integrating traffic signal data with robust network representation and simulation tools. This study presents utdf2gmns, an open-source Python tool that automates conversion of the Universal Traffic Data Format, including network representation, signalized intersections, and turning volumes into the General Modeling Network Specification (GMNS) Standard. The resulting GMNS-compliant network can be converted for microsimulation in SUMO. By automatically extracting intersection control parameters and aligning them with GMNS conventions, utdf2gmns minimizes manual preprocessing and data loss. utdf2gmns also integrates with the Sigma-X engine to extract and visualize key traffic control metrics, such as phasing diagrams, turning volumes, volume-tocapacity ratios, and control delays. This streamlined workflow enables efficient scenario testing, accurate model building, and consistent data management. Validated through case studies, utdf2gmns reliably models complex urban corridors, promoting reproducibility and standardization. Documentation is available on GitHub and PyPI, supporting easy integration and community engagement.

Luo, Roy [ORNL] (ORCID:0009000312909983)↗

DOE OSTI2018

The NSLS-II Components Relative Alignment Deviation Estimate

The alignment of NSLS-II components will be conducted under the constraint of survey control network. The control network system is categorized as outside, primary and secondary control networks, and all of them have random errors due to measurement random errors. In the error propagation point of view, they all will contribute to the final positioning accuracy of each component in global sense. However, as the global positioning requirement is relative loose (i.e. ± 3mm for storage ring), relative positioning accuracy is what we really concern. In this situation, only secondary control network will contribute as only secondary monuments will be employed to direct the adjustment of components. Other error sources include instrument setup error, adjustment residuals and slab movement etc. This article will analyze the maximum relative deviation of 3 positions in one cell’s region, which is a simulation of 1 ID and 2 Vacuum chamber positions.

43 PARTICLE ACCELERATORS↗

DOE OSTI2013

The NSLS-II Components Relative Alignment Deviation Estimate

36 MATERIALS SCIENCE↗