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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Science Federation Emulation Testbed: Demonstration of VFSIE Functionalities

The Virtual Federated Science Instrument Environment (VFSIE) is a digital twin of a federated infrastructure of multiple sites with science instruments and computing systems that are geographically dispersed. It emulates the federation using containers and virtual hosts (vhosts) connected over local and wide-area networks for the main purpose of supporting the development and testing of science workflows and software stacks, which will subsequently transition to the physical infrastructure. In this demo, we present VFSIE of a four sites scenario and illustrate the following use cases: (i) execution of federation stack components that expose resources to enable a science user to execute workflows, (ii) execution of remote control commands of beam-line instruments to collect measurements and position the sensor, and (iii) remote execution and access of Jupyter Notebook for tomographic image computations

Al Najjar, Anees↗
DOE OSTI2023

Highly multicolored light-emitting arrays for compressive spectroscopy

Miniaturized, multicolored light-emitting device arrays are promising for applications in sensing, imaging, computing, and more, but the range of emission colors achievable by a conventional light-emitting diode is limited by material or device constraints. In this work, we demonstrate a highly multicolored light-emitting array with 49 different, individually addressable colors on a single chip. The array consists of pulsed-driven metal-oxide-semiconductor capacitors, which generate electroluminescence from microdispensed materials spanning a diverse range of colors and spectral shapes, enabling facile generation of arbitrary light spectra across a broad wavelength range (400 to 1400 nm). When combined with compressive reconstruction algorithms, these arrays can be used to perform spectroscopic measurements in a compact manner without diffractive optics. As an example, we demonstrate microscale spectral imaging of samples using a multiplexed electroluminescent array in conjunction with a monochrome camera.

42 ENGINEERING↗
DOE OSTI2020

Hydrodynamic and Radiographic Toolbox (HART)

With a multi-lab and university team, we propose to develop new methods for a Hydrodynamic and Radiographic Toolbox (HART) that will enable a fuller and more extensive use of experimental radiographic data towards better characterizing and reducing uncertainties in predictive modeling of weapons performance. We will achieve this by leveraging recent developments in areas of computational imaging, statistical and machine learning, and reduced order modeling of hydrodynamics. In terms of software practices, by partnering with XCP (RISTRA Project) we will conform to recent XCP standards that are inline with modern software practices and standards and ensure compatibility and ease of inter-operability with existing codes. Three new activities under this proposal include (a) the development and use of deep learning-based surrogates to accelerate reconstruction and variational inference of density fields from radiographs of hydrotests, (b) a model-data fusion strategy that couples deep learning-based density reconstructions with fast hydrodynamics simulators to better constrain the reconstruction, and (c) a method for treating asymmetries using techniques adopted from limited view tomography. All three new activities will be based on improved treatment of scatter, noise, beam spot movement, detector blur, and flat fielding in the forward model, and a use of sophisticated priors to aid in the re construction. The improvements to the forward model and improved algorithmic design of the reconstruction when complete will be contained in the iterative reconstruction code SHIVA—a code project that we have recently initiated. The many ways in which machine learning can be used in the reconstruction work will be contained in a code HERMES that has been initiated with DTRA support. For example, the significant levels of acceleration that will likely be achieved by the use of machine learning techniques will permit us (and are required) to quantify uncertainties in density retrievals. Next, the two-way coupling between density reconstruction and model-based simulation of the hydrodynamics will be contained in code EREBUS, and will permit a fuller realization of the potential of the data to constrain the hydrodynamic model and better address issues related to asymmetries in the problem. Finally, we anticipate that the better consistency with physics achieved in our reconstructions will allow them to be used by X-Division more so than today.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Viewing is understanding: Graphite microstructure effects on infiltrated molten salt distribution revealed by 3D neutron tomography

Molten salt infiltration in the pore network of nuclear graphite may cause unwanted changes to graphite's local structure and mechanical and thermal properties. A detailed and comprehensive understanding of molten salt intrusion (distribution across sample cross section and penetration depth) is needed to assess its effects. Here, in this work, we report on an improved methodology for the use of neutron imaging (computed tomography) to evaluate salt penetration and distribution of a wide range of graphite grades with diverse microstructures. Neutron tomography data were acquired on the same graphite sample before and after salt intrusion; the 3D reconstructed volumes were digitally co-registered and subtracted. The difference in neutron attenuation coefficient represents direct visualization of FLiNaK (LiF–NaF–KF) salt distribution in the salt-impregnated graphite samples. This improved methodology was applied to investigate the effect of exposure times (12 h and 336 h) and of graphite microstructure when exposed to FLiNaK at 750 °C and 3 bar (gauge) pressure, starting from flowing argon at near atmospheric pressure. The results show that medium-grained and fine-grained graphites evolve to equilibrium at significantly different rates: fast salt uptake in medium-grained graphites produces salt deposits throughout the volume of graphite specimens, whereas salt infiltration in fine-grained graphites is much slower and limited to exposed surfaces.

FLiNaK infiltration↗
DOE OSTI2025

Advances in relaxation and memory effects of magnetic nanoparticles for biomedical applications

Functionalized magnetic nanoparticles are pivotal in magnetic resonance imaging, computed tomography, controlled drug delivery, and hyperthermia treatments due to their exceptional magnetic relaxation and functional properties. The magnetic core composition and structure significantly affects the complex magnetic properties of these nanoparticles necessitating a thorough examination of magnetism fundamentals related to these systems. One important aspect is the ability of magnetic nanoparticles to retain previous magnetic state configurations known as memory effect, primarily governed by domain structure and magnetic anisotropy. Despite its relevance to advanced applications, comprehensive studies on magnetic relaxation and memory effects remain limited. Here, the present review aims to bridge this gap by investigating relaxation mechanisms, synthesis strategies, and applications, fostering further innovation. It investigates the memory effects and their dependence on particle composition and morphology along with key synthesis techniques for large-scale production in industrial adoption. Structured into focused sections on magnetic properties and their influence on biomedical and technological applications, this review provides essential insights into memory effects, magneto-relaxation mechanisms, influencing factors, and both experimental and theoretical methodologies. It also delves into computational modelling and AI-driven design, which are revolutionizing the prediction, discovery, and optimization of materials with tailored properties.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Anticipating decoherence in quantum systems

Large-scale quantum technologies require coherence across distant nodes, necessitating indistinguishable quantum states. However, environmental disorder, including dephasing, spectral diffusion, and spin-bath interactions, undermines coherence. Using statistical methods, we uncover correlations in decoherence channels induced by slowly varying environments. Spectral diffusion serves as a representative demonstration case that can be extended to other remote, disordered systems such as spins in nitrogen-vacancy centers and quantum-dot spin qubits, as well as flux noise in superconducting qubits. In this work, we employ replica-theory-inspired trajectory analysis to reveal predictable temporal structures in decoherence dynamics, and validate these through an anticipatory systems framework with internal prediction of unseen spectral dynamics in multiple quantum systems, showing that this framework could, if implemented, reduce spectral shift by average factors of approximately 2 to 19, depending on emitter stability, thereby enabling enhanced coherence and multi-node synchronization for scalable quantum communication, computation, imaging, and sensing.

Maan, Pranshu [Purdue University]↗
DOE OSTI2025

Ptychography at all wavelengths

Ptychography is a computational imaging technique that operates across multiple wavelength regimes, from electron (picometres) to X-ray (~0.1 nm), extreme ultraviolet (~10 nm) and visible light (micrometres). By reconstructing both amplitude and phase from diffraction patterns, ptychography enables high-resolution, quantitative imaging without conventional limitations imposed by lens-based optics. Ptychography has enabled advances across a range of scales: achieving deep-sub-angstrom resolution with electron microscopy, becoming an indispensable tool at X-ray synchrotron facilities worldwide and overcoming the trade-offs between resolution and field-of-view in optical imaging. This Primer provides a unified treatment of ptychography across these wavelength regimes. First, we discuss theoretical foundations, reconstruction algorithms, experimental considerations and wavelength-specific challenges. We then give examples of raw and processed data from various configurations and wavelengths. Next, we highlight key applications of ptychography in life sciences, materials science and industry. We also discuss data standards, open-source software implementations and best practices for ensuring reproducibility across different wavelength regimes. Finally, we consider limitations and future opportunities for ptychography. Together with accompanying datasets and code implementations, this Primer aims to serve newcomers and experienced practitioners in the field, facilitating broader adoption of ptychography across different disciplines.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

X-ray Computed Tomography Data of Dense Metallic Components

The data shared in here are X-ray computed tomography (XCT) scans of a hexagonal fuel nozzle in 3 sections with the Metrotom 800 system at the Manufacturing Demonstration Facility (MDF) at Oak Ridge National Laboratory. The data are used in the paper "Tomographic Sparse View Selection using the View Covariance Loss, by Lin et al. (doi:10.1109/TPAMI.2025.36000720), accepted to the international conference on computational imaging (ICCP 2025). Figures 4-7 in the paper describe the part/XCT scan. File name Descriptions: Bottom section: TCR- Single Channeled SRC L 2019-3-18 12-26-41.hdf5 Medium section: TCR- Single Channeled SRC M 2019-3-18 13-8-9.hdf5 Top section: TCR- Single Channeled SRC T 2019-3-18 13-45-39.hdf5 Each hdf5 file contains projection data, and all the relevant X-ray CT scan setting. The full list of included attributes: distance_unit: Units of all distances specified angle_unit : Units of the angles angles: Array of all angles used voxel_size_xy: Baseline recon (if any) has this voxel size in the in-plane direction voxel_size_z: Baseline recon (if any) has this voxel size in the cross-plane direction det_pixel_size_col: Size of the detector pixels in the column dimension det_pixel_size_row: Size of the detector pixels in the row dimension src_iso_dist: Source to iso-center distance iso_det_dist: Iso-center to detector distance det_angle: If the detector is rotated/tilted, this angle corresponds to that value det_row_offset: Center of rotation offset in the vertical direction det_col_offset: Center of rotation offset in the horizontal direction reconstruction: A baseline reconstruction stored as 3D array BHC params: Beam-hardening parameters - Van De Casteel Model - if it has been used to pre-process the projections We also provided a python script (hdf_io.py) that allows the user to read the relevant data from each hdf5 file.

Ziabari, Amir [Oak Ridge National Laboratory]↗
DOE OSTI2021

Light transport with weak angular dependence in fog

Random scattering and absorption of light by tiny particles in aerosols, like fog, reduce situational awareness and cause unacceptable down-time for critical systems or operations. Computationally efficient light transport models are desired for computational imaging to improve remote sensing capabilities in degraded optical environments. To this end, we have developed a model based on a weak angular dependence approximation to the Boltzmann or radiative transfer equation that appears to be applicable in both the moderate and highly scattering regimes, thereby covering the applicability domain of both the small angle and diffusion approximations. An analytic solution was derived and validated using experimental data acquired at the Sandia National Laboratory Fog Chamber facility. The evolution of the fog particle density and size distribution were measured and used to determine macroscopic absorption and scattering properties using Mie theory. A three-band (0.532, 1.55, and 9.68 µ m) transmissometer with lock-in amplifiers enabled changes in fog density of over an order of magnitude to be measured due to the increased transmission at higher wavelengths, covering both the moderate and highly scattering regimes. The meteorological optical range parameter is shown to be about 0.6 times the transport mean free path length, suggesting an improved physical interpretation of this parameter.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Incorporating the effects of objects in an approximate model of light transport in scattering media

A computationally efficient radiative transport model is presented that predicts a camera measurement and accounts for the light reflected and blocked by an object in a scattering medium. The model is in good agreement with experimental data acquired at the Sandia National Laboratory Fog Chamber Facility (SNLFC). The model is applicable in computational imaging to detect, localize, and image objects hidden in scattering media. Here, a statistical approach was implemented to study object detection limits in fog.

42 ENGINEERING↗
DOE OSTI2020

The road to atomically thin metasurface optics

Abstract The development of flat optics has taken the world by storm. The initial mission was to try and replace conventional optical elements by thinner, lightweight equivalents. However, while developing this technology and learning about its strengths and limitations, researchers have identified a myriad of exciting new opportunities. It is therefore a great moment to explore where flat optics can really make a difference and what materials and building blocks are needed to make further progress. Building on its strengths, flat optics is bound to impact computational imaging, active wavefront manipulation, ultrafast spatiotemporal control of light, quantum communications, thermal emission management, novel display technologies, and sensing. In parallel with the development of flat optics, we have witnessed an incredible progress in the large-area synthesis and physical understanding of atomically thin, two-dimensional (2D) quantum materials. Given that these materials bring a wealth of unique physical properties and feature the same dimensionality as planar optical elements, they appear to have exactly what it takes to develop the next generation of high-performance flat optics.

Brongersma, Mark L.↗
DOE OSTI2025

AI-Driven Accelerated Inclusion Analysis for Energy Efficient Steelmaking (Final CRADA Report)

This was a collaborative effort between Lawrence Livermore National Security, LLC (LLNS) as manager and operator of Lawrence Livermore National Laboratory (LLNL) and ArcelorMittal USA Research LLC (“ArcelorMittal” as the Participant), to use scanning electron microscopy (SEM) images, computer vision and machine learning methods, and high-performance computing to accelerate the inclusion analysis process of liquid steel so that new methods can be used for near-real time process control on the shop floor.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantum graph learning and algorithms applied in quantum computer sciences and image classification

Graph and network theory play a fundamental role in quantum computer sciences, including quantum information and computation. Random graphs and complex network theory are pivotal in predicting novel quantum phenomena, where entangled links are represented by edges. Quantum algorithms have been developed to enhance solutions for various network problems, giving rise to quantum graph computing and quantum graph learning (QGL). Here, in this review, we explore graph theory and graph learning methods as powerful tools for quantum computers to generate efficient solutions to problems beyond the reach of classical systems. We delve into the development of quantum complex network theory and its applications in quantum computation, materials discovery, and research. We also discuss quantum machine learning (QML) methodologies for effective image classification using qubits, quantum gates, and quantum circuits. Additionally, the paper addresses the challenges of QGL and algorithms, emphasizing the steps needed to develop flexible QGL solvers. This review presents a comprehensive overview of the fields of QGL and QML, highlights recent advancements, and identifies opportunities for future research.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Star–Galaxy Image Separation with Computationally Efficient Gaussian Process Classification

Abstract We introduce a novel method for discerning optical telescope images of stars from those of galaxies using Gaussian processes (GPs). Although applications of GPs often struggle in high-dimensional data modalities such as optical image classification, we show that a low-dimensional embedding of images into a metric space defined by the principal components of the data suffices to produce high-quality predictions from real large-scale survey data. We develop a novel method of GP classification hyperparameter training that scales approximately linearly in the number of image observations, which allows for application of GP models to large-size Hyper Suprime-Cam Subaru Strategic Program data. In our experiments, we evaluate the performance of a principal component analysis embedded GP predictive model against other machine-learning algorithms, including a convolutional neural network and an image photometric morphology discriminator. Our analysis shows that our methods compare favorably with current methods in optical image classification while producing posterior distributions from the GP regression that can be used to quantify object classification uncertainty. We further describe how classification uncertainty can be used to efficiently parse large-scale survey imaging data to produce high-confidence object catalogs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Diffractive optical computing in free space

Abstract Structured optical materials create new computing paradigms using photons, with transformative impact on various fields, including machine learning, computer vision, imaging, telecommunications, and sensing. This Perspective sheds light on the potential of free-space optical systems based on engineered surfaces for advancing optical computing. Manipulating light in unprecedented ways, emerging structured surfaces enable all-optical implementation of various mathematical functions and machine learning tasks. Diffractive networks, in particular, bring deep-learning principles into the design and operation of free-space optical systems to create new functionalities. Metasurfaces consisting of deeply subwavelength units are achieving exotic optical responses that provide independent control over different properties of light and can bring major advances in computational throughput and data-transfer bandwidth of free-space optical processors. Unlike integrated photonics-based optoelectronic systems that demand preprocessed inputs, free-space optical processors have direct access to all the optical degrees of freedom that carry information about an input scene/object without needing digital recovery or preprocessing of information. To realize the full potential of free-space optical computing architectures, diffractive surfaces and metasurfaces need to advance symbiotically and co-evolve in their designs, 3D fabrication/integration, cascadability, and computing accuracy to serve the needs of next-generation machine vision, computational imaging, mathematical computing, and telecommunication technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Coupling of multiscale imaging analysis and computational modeling for understanding thick cathode degradation mechanisms

Here, using a thick NMC811 (LiNi 0.8 Mn 0.1 Co 0.1 O 2 ) electrode as an example, we present a macro- to nanoscale 2D and 3D imaging analysis approach coupled with 4D (space + time) computational modeling to probe its degradation mechanism in a lithium-ion battery cell. Particle cracking increases and contact loss between particles and carbon-binder domain are observed to correlate with the cell degradation. This study unravels that the reaction heterogeneity within the thick cathode caused by the unbalanced electron conduction is the main cause of the battery degradation over cycling. The increased heterogeneity in the system will entail more cathode regions where the degree of active material utilization is uneven, leading to higher probabilities of particle cracking. These findings shed light on the crucial role of the electronic and ionic transportation networks in the performance deterioration of the thick cathode. They also provide guidance for cathode architecture optimization and performance improvement.

25 ENERGY STORAGE↗