Search NASA⌕ Search

SEARCH · Search NASA

Results for “structure reconstruction”

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.

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

At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Quasiparticle Poisoning of Superconducting Qubits from Resonant Absorption of Pair-Breaking Photons

The ideal superconductor provides a pristine environment for the delicate states of a quantum computer: because there is an energy gap to excitations, there are no spurious modes with which the qubits can interact, causing irreversible decay of the quantum state. As a practical matter, however, there exists a high density of excitations out of the superconducting ground state even at ultralow temperature; these are known as quasiparticles. Observed quasiparticle densities are of order 1 μ m - 3 , tens of orders of magnitude greater than the equilibrium density expected from theory. Nonequilibrium quasiparticles extract energy from the qubit mode and can induce dephasing. Here we show that a dominant mechanism for quasiparticle poisoning is direct absorption of high-energy photons at the qubit junction. We use a Josephson junction-based photon source to controllably dose qubit circuits with millimeter-wave radiation, and we use an interferometric quantum gate sequence to reconstruct the charge parity of the qubit. We find that the structure of the qubit itself acts as a resonant antenna for millimeter-wave radiation, providing an efficient path for photons to generate quasiparticles. A deep understanding of this physics will pave the way to realization of next-generation superconducting qubits that are robust against quasiparticle poisoning.

Liu, Chuan-Hong↗
DOE OSTI2021

A Comparative Study of Straight-Strip and Zigzag-Interleaved Anode Patterns for MPGD Readouts

Due to their simplicity and versatility of design, straight strip or rectangular pad anode structures are frequently employed with micro-pattern gas detectors to reconstruct high precision space points for various tracking applications. The particle impact point is typically determined by interpolating the charge collected by several neighboring strips. However, to effectively extract the inherent positional information, the lateral spacing of the straight strips must be comparable to or preferably smaller than the full extent of the incident charge cloud. In contrast, highly interleaved anode patterns, such as zigzags, can adequately sample the incident charge with a pitch appreciably larger than the charge cloud.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Time resolved radiographs and fuel density of the stagnation phase of Inertial Confinement Fusion implosions (NSA report)

This report contains a figure and caption. Left: Compton radiographs of a 3-shock HDC THD layered implosion recorded 10ps before and 160ps after bang-time (BT) at the NIF, using >50keV point projection backlighters generated by irradiating a 25μm-diameter Au wire with 30ps-long ARC pulses. Right: Reconstructed density distributions of fuel and remaining ablator showing significant 3D structure and top-down asymmetry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

On the constitutive stress-strain relationships and evaluation of the softening coefficient in work hardening mechanisms

The constitutive stress-strain relationships for structural alloys, such as additively manufactured Ti- 6Al-4V, are reconstructed by assessing the form and mechanism of work hardening relationships. The stress-strain relationships are best fit using both the Hollomon and Voce expressions wherein the Voce expression well-reproduces the later stage(s) of work hardening whereas the Hollomon relationship provides a better fit just beyond the proportional limit.

36 MATERIALS SCIENCE↗
NASA NTRS1978

Coded aperture imaging - Predicted performance of uniformly redundant arrays

It is noted that uniformly redundant arrays (URAs) have autocorrelation functions with perfectly flat sidelobes. A generalized signal-to-noise equation has been developed to predict URA performance. The signal-to-noise value is formulated as a function of aperture transmission or density, the ratio of the intensity of a resolution element to the integrated source intensity, and the ratio of detector background noise to the integrated intensity. It is shown that the only two-dimensional URAs known have a transmission of one half. This is not a great limitation because a nonoptimum transmission of one half never reduces the signal-to-noise ratio more than 30%. The reconstructed URA image contains practically uniform noise, regardless of the object structure. URA's improvement over the single-pinhole camera is much larger for high-intensity points than for low-intensity points.

Fenimore, E. E.↗
NASA NTRS2015

In-Flight Suppression of an Unstable F/A-18 Structural Mode Using the Space Launch System Adaptive Augmenting Control System

NASA's Space Launch System (SLS) Flight Control System (FCS) includes an Adaptive Augmenting Control (AAC) component which employs a multiplicative gain update law to enhance the performance and robustness of the baseline control system for extreme off-nominal scenarios. The SLS FCS algorithm including AAC has been flight tested utilizing a specially outfitted F/A-18 fighter jet in which the pitch axis control of the aircraft was performed by a Non-linear Dynamic Inversion (NDI) controller, SLS reference models, and the SLS flight software prototype. This paper describes test cases from the research flight campaign in which the fundamental F/A-18 airframe structural mode was identified using post-flight frequency-domain reconstruction, amplified to result in closed loop instability, and suppressed in-flight by the SLS adaptive control system.

VanZwieten, Tannen S.↗
NASA NTRS2015

Background-Oriented Schlieren for Large-Scale and High-Speed Aerodynamic Phenomena

Visualization of the flow field around a generic re-entry capsule in subsonic flow and shock wave visualization with cylindrical explosives have been conducted to demonstrate sensitivity and applicability of background-oriented schlieren (BOS) for field experiments. The wind tunnel experiment suggests that BOS with a fine-pixel imaging device has a density change detection sensitivity on the order of 10(sup -5) in subsonic flow. In a laboratory setup, the structure of the shock waves generated by explosives have been successfully reconstructed by a computed tomography method combined with BOS.

Mizukaki, Toshiharu↗
NASA NTRSDate not supplied

Modeling the Effective Elasticity of Anisotropic Porous Materials

The development and optimization of composite materials designed for thermal protection of NASA’s spacecraft require understanding their physical response to high-enthalpy environments. To predict their macro-scale properties and behavior, high-fidelity 3D simulations are performed at the microscale on realistic representations of these composites. The digital microstructures are generated either synthetically or through X-ray micro-computed tomography reconstructions. One of the main challenges in the prediction of the structural response of heatshield materials is the computation of the effective elasticity of the fibrous composite, as well as the understanding of the deformation and stresses generated at the microscale. These are driven by the fiber layout within the microstructure and the distribution of the infused matrix. In this effort, the micro-mechanical linear elastic behavior of fibrous ablators is modeled using a numerical method based on the Multi-Point Stress Approximation (MPSA) finite volume scheme, a generalization of the more commonly used Multi-Point Flux Approximation (MPFA) that was presented at the 10th Ablation Workshop. To predict the behavior of fibrous and woven architectures, algorithms that compute the local fiber orientation are used. The implementation of the MPSA was verified using analytical solutions, engineering test cases, and compared against legacy Finite Element Analysis (FEA) software. The stress analysis models were then applied to real geometries used by NASA in thermal protection systems such as fibrous preforms and woven materials and the results were compared to experimental data.

Elasticity↗
DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Regional tropical rainfall shifts under global warming: an energetic perspective

Abstract Future climate simulations feature pronounced spatial shifts in the structure of tropical rainfall. We apply a novel atmospheric energy flux analysis to diagnose late 21st century tropical rainfall shifts in a large ensemble of simulations of 21st century climate. The method reconstructs 2D spatial changes in rainfall based on horizontal shifts in the lines of zero meridional and zonal divergent energy flux, called the energy flux equator (EFE) and energy flux prime meridian (EFPM), respectively. Two main sources of future atmospheric energy flux changes, and hence rainfall shifts, are identified by the analysis: the high-latitude North Atlantic due to a weakened Atlantic Meridional Overturning Circulation that shifts tropical rainfall southwards over the greater Tropical Atlantic sector and eastern Pacific; and the eastern tropical Pacific due to a permanent El-Niño-like response that produces zonal shifts over the Maritime Continent and South America. To first order, the shifts in the EFE and EFPM mirror gross distributional changes in tropical precipitation, with a southward shift in rainfall over the tropical Atlantic, West Africa, and eastern tropical Pacific and an eastward shift over the Maritime Continent and western Pacific. When used to reconstruct future rainfall shifts in the tropical Atlantic and Sahel, the method reasonably represents the simulated meridional structure of rainfall shifts but does not do so for the zonal structures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Unbiased particle conformation extraction from scattering spectra using orthonormal basis expansions

A strategy is outlined for quantitatively evaluating the particle density profiles from small-angle scattering spectra of dilute solutions. The approach employs an orthonormal basis function expansion method, enabling the determination of characteristic mass distributions in self-assembled structures without the need for a specific structural model. Through computational benchmarking, the efficacy of this approach is validated by effectively reconstructing the density profile of soft-ball systems with varying fuzziness from their scattering signatures. Further, the feasibility of the method is demonstrated by fitting small-angle neutron scattering data obtained from Pluronic L64 micelles at different temperatures. This proposed approach is both simple and analytical, eliminating the requirement for a presumptive structural model in scattering analysis. The new method could therefore facilitate quantitative descriptions of complex nanoscopic structures inherent to numerous soft-matter systems using small-angle scattering techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Heterogeneous techniques for rescaling energy deposits in the CMS Phase-2 endcap calorimeter

We present the porting to heterogeneous architectures of the algorithm used for applying linear transformations of raw energy deposits in the CMS High Granularity Calorimeter (HGCAL). This is the first heterogeneous algorithm to be fully integrated with HGCAL’s reconstruction chain. After introducing the latter and giving a brief description of the structural components of HGCAL relevant for this work, the role of the linear transformations in the calibration is reviewed. The many ways in which parallelization is achieved are described, and the successful validation of the heterogeneous algorithm is covered. Detailed performance measurements are presented, including throughput and execution time for both CPU and GPU algorithms, therefore establishing the corresponding speedup. We finally discuss the interplay between this work and the porting of other algorithms in the existing reconstruction chain, as well as integrating algorithms previously ported but not yet integrated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Extracting dark-matter velocities from halo masses: A reconstruction conjecture

Increasing attention has recently focused on nontraditional dark-matter production mechanisms which result in primordial dark-matter velocity distributions with highly nonthermal shapes. Here, in this paper, we undertake an assessment of how the detailed shape of a general dark-matter velocity distribution impacts structure formation in the nonlinear regime. In particular, we investigate the impact on the halo-mass and subhalo-mass functions, as well as on astrophysical observables such as satellite and cluster-number counts. We find that many of the standard expectations no longer hold in situations in which this velocity distribution takes a highly nontrivial, even multimodal shape. For example, we find that the nominal free-streaming scale alone becomes insufficient to characterize the effect of free-streaming on structure formation. In addition, we propose a simple one-line conjecture which can be used to “reconstruct” the primordial dark-matter velocity distribution directly from the shape of the halo-mass function. Although our conjecture is completely heuristic, we show that it successfully reproduces the salient features of the underlying dark-matter velocity distribution even for nontrivial distributions which are highly nonthermal and/or multimodal, such as might occur for nonminimal dark sectors. Moreover, since our approach relies only on the halo-mass function, our conjecture provides a method of probing dark-matter properties even for scenarios in which the dark and visible sectors interact only gravitationally.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A Gigaparsec-scale Hydrodynamic Volume Reconstructed with Deep Learning

The next generation of spectroscopic surveys will map the large-scale structure of the Universe at high redshifts (2 ≤ z ≤ 5) using millions of quasar spectra, enabling major advances in constraining both the standard cosmological model and its extensions. Robust cosmological analyses of these data sets require numerical simulations that both cover gigaparsec volumes and resolve features on ∼10 kpc scales and smaller. However, running such large-volume, high-resolution hydrodynamic simulations is computationally prohibitive. We present a generative deep learning model that enhances a low-resolution, gigaparsec-scale (960 h −1 Mpc) hydrodynamic simulation using a smaller (80 h −1 Mpc) high-resolution input hydrodynamic simulation as training data. The resulting enhanced simulation reproduces the line-of-sight power spectrum to within ∼10% and the three-dimensional power spectrum at the ∼20% level at intermediate to small scales (k ≲ 2 h Mpc −1 ). Our method shows strong promise for producing realistic simulations for cosmological analyses with current surveys such as the Dark Energy Spectroscopic Instrument and upcoming next-generation experiments, but further improvements are needed to accurately recover the large-scale modes. We publicly release the enhanced hydrodynamic simulation, along with a halo catalog from a companion N-body dark matter simulation to support the calibration of data analysis pipelines for these large-scale surveys.

Convolutional neural networks↗
DOE OSTI2020

Three-dimensional bubble reconstruction in high burnup UO 2

In light water reactor (LWR) UO 2 fuels, the evolution of volatile fission products is one of the critical areas of fuel behavior that is yet to be fully understood. In UO 2 irradiated to high burnups, it is well known that most released fission gases come from the central region of the fuel as opposed to the highly porous high burnup structure (HBS) on the periphery of the pellets. However, fuels with and without interconnected bubble networks at the fuel center showed high to moderate release fractions, which conceals the mechanisms responsible for the gas release in the latter scenario. In this work, focused ion beam tomography was used to investigate the three-dimensional bubble structure in an irradiated LWR UO 2 fuel pellet with high degree of fission gas retention so that the degree of bubble interconnection could be assessed. Six radial locations with different burnups and temperatures were serially sectioned and imaged to reconstruct the three-dimensional bubble structure. As expected, the highest porosity was observed at the periphery of the fuel (HBS). Here, the porosity then decreased towards the pellet center, except for the centermost location. This location had a slightly higher porosity than its adjacent mid-radial location, which was attributed to the temperature difference between the two locations. This study provides a first-time volumetric evaluation of the porosity at different radial locations on a UO 2 fuel pellet. During this investigation, no significant bubble interconnection was noted at any of the six radial locations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Quantitative Structure Determination from Experimental Four-Dimensional Scanning Transmission Electron Microscopy via the Scattering Matrix

Considerable inroads have recently been made on algorithms to determine the sample potential from four-dimensional scanning transmission electron microscopy data from thick samples where multiple scattering cannot be neglected. This paper further develops the scattering matrix approach to such structure determination. Through simulation, we demonstrate how this approach can be modified to better handle partial spatial coherence, unknown probe defocus, and information from the dark field region. By combining these developments we reconstruct the electrostatic potential of a monolithic SrTiO 3 crystal showing good quantitative agreement with the expected structure.

4D STEM↗
DOE OSTI2026

Deep Learning and Photogrammetric Reconstruction for Automated Crack Detection and Dimensional Measurement in Mining Operations

Surface crack detection and dimensional measurement at active mining sites present significant safety and operational challenges. Manual inspection methods are labor-intensive, spatially incomplete, and expose personnel to hazardous environments, while existing automated approaches have been developed primarily for concrete civil infrastructure and have not been validated on the complex, variable surfaces characteristic of mining environments. This dissertation presents an automated pipeline that integrates deep learning semantic segmentation with Structure-from-Motion photogrammetry to detect surface cracks and measure their aperture, length, and vertical displacement from standard RGB imagery acquired during routine Uncrewed Aerial Vehicle (UAV) survey operations, without requiring additional sensor hardware or manual measurement. The pipeline combines a U-Net architecture with an EfficientNet-B0 encoder, pretrained on the SDNET2018 concrete crack dataset and fine-tuned on a mining-specific dataset spanning laboratory concrete specimens, coal refuse impoundment embankments, and post-blast limestone quarry benches. Photogrammetric reconstruction is performed using COLMAP Structure-from-Motion and Multi-View Stereo, with crack segmentation masks projected into the reconstructed point cloud to enable three-dimensional vertical displacement measurement through local plane fitting and bimodal surface detection. The pipeline was validated across 36 controlled laboratory specimens at three imaging distances and four vertical displacement levels, achieving aperture measurement RMSE of 0.047 cm and R² of 0.954, and vertical displacement RMSE of 0.140 cm and R² of 0.966, against independent caliper measurements. Field application at a coal refuse impoundment in southwestern Pennsylvania detected 71 crack components across the embankment crest, with a dominant longitudinal crack exhibiting aperture values reaching 28 cm and a 95th percentile vertical displacement of 35.53 cm, consistent in magnitude and spatial distribution with simultaneously acquired LiDAR-derived estimates. Application across four post-blast limestone quarry bench datasets in California successfully characterized blast-induced fracture networks at ground sampling distances ranging from 0.59 to 1.23 cm/pixel, with detected crack geometries physically consistent with observable surface conditions at each site. The results demonstrate that deep learning-based crack detection and photogrammetric measurement can be integrated into routine UAV inspection workflows at mining sites, providing repeatable, scalable, and quantitative crack characterization across surface types, crack scales, and displacement magnitudes not previously addressed in the literature. The pipeline requires no dedicated surveying equipment beyond the UAV platforms already deployed at mine sites for survey and monitoring purposes, supporting practical adoption within existing operational workflows.

Crack detection, Dimensional Measurement↗