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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Quantitative Holdup Determinations Using Coded-Aperture Gamma-Ray Imaging

Passive gamma-ray imaging can be used to create images of distributed nuclear materials. Inherently quantitative, the images provide a means to directly determine the amount of nuclear material in the imager’s field of view. However, to take advantage of quantitative imaging, one must correct for changes to the imager’s efficiency across the image. This paper presents work on detailed calibration of a coded-aperture imager based on a portable, mechanically cooled, high-purity germanium detector. This enables the use of gamma-ray imaging as a tool to determine nuclear holdup. By applying the calibrations to images of small amounts of distributed 235U, quantity estimates within a few percent of the actual amounts present were obtained, validating the approach.

Ziock, Klaus-Peter↗
DOE OSTI2020

Examination of synthetic gas puff imaging diagnostic data from a gyrokinetic turbulence code

A synthetic gas puff imaging (GPI) diagnostic has been developed for the purpose of validating the three-dimensional gyrokinetic turbulence code XGC. The synthetic diagnostic is described and applied to XGC simulations of two Alcator C-Mod discharges. The turbulence characteristics deduced from the resulting simulated GPI frames, using analysis techniques similar to those applied to experimental data, are compared with locally derived characteristics extracted directly from the XGC output. The comparison of the two is shown to be potentially impacted by misalignment between the GPI view and the magnetic field, the dependence of the light emission on the electron density and temperature, and spatial and temporal variations in the neutral gas cloud induced by the turbulent plasma fluctuations. We conclude that quantitative and, in some cases even qualitative, validation of turbulence simulations need to account for these effects. While we cannot directly compare our results with experimental data due to the absence of high quality GPI data from the shots and times simulated by XGC, we do relate the overall characteristics obtained from the synthetic GPI analysis to published Alcator C-Mod GPI data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

H420 Imager Calibration Procedure: Field of View Scan

Gamma-ray imagers with coded apertures have a finite field of view (FOV) within which an image of a source can be generated. A “FOV scan” is a facet of imager calibration procedure that involves collecting specialized data for a range of source locations filling the FOV in order to understand and correct for differences in imager performance. Systematic distortions reveal relative displacements between internal imager components, which can be accounted for in data analysis. The success of this scan requires a stable imager position during the full measurement duration (~hours). This document summarizes the hardware and software tools required to conduct a FOV calibration scan of an H3D H420 Coded Aperture Gamma-Ray Imager.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

HopBox: An image analysis pipeline to characterize hop cone morphology

Abstract Hop cone morphology can influence picking and drying ability, and color can impact consumer preference and may be indicative of quality. However, these characteristics are not generally evaluated in hop breeding programs due to the tedious nature of trait quantification and the extensive variation among cones within a genotype. We developed the HopBox, which is a simply constructed light box with a camera mount, and a publicly available image processing pipeline that identifies hop cones within color‐corrected images, reads a QR code within the image, and outputs data on hop cone length, width, area, perimeter, openness, weight, color, and density. The trained model was applied to images of 500 cones each from 15 replicated advanced hop genotypes from the USDA‐ARS breeding program in Prosser, Washington. Analysis of variance revealed significant ( p < 0.001) differences between genotypes for all traits measured, enabling breeders to discriminate between genotypes for selection purposes. Broad sense heritability for all traits ranged from 0.23 to 0.59. A random sampling of hop cones from the complete dataset revealed that imaging only 5–10 cones adequately captured genotypic variation and provided acceptable rank correlations ( r s > 0.75); however, increasing the sample size to 30 provided optimal precision. Instructions for constructing a HopBox and the code for the analysis pipeline are publicly available online and have wide applicability for hop breeding and research.

Altendorf, Kayla R.↗
DOE OSTI2025

Chasing ghosts: characterization of artifact generation in coded aperture decoding due to experimental implementation

Coded aperture imaging is a form of lensless aperture imaging that projects multiple overlapping images of the source onto the detector, enhancing signal strength, which is advantageous for low-flux sources or high-resolution imaging. This technique requires decoding of the detector signal to reconstruct the original source, which involves convolving the detector data with the aperture pattern. When the signal is from a centered point source, the reconstructed source image is known as the point spread function (PSF). A clean PSF without artifacts is a Dirac delta function [Appl. Opt. 20, 1858 (1981)]. This paper examines the robustness of the decoding process against variations in experimental tolerances by analyzing artifact growth in the reconstructed PSF. We illustrate the effects of incorrect magnification, rotation, and detector size and find that aperture–detector rotational misalignment about the imaging axis is the most sensitive parameter, with significant artifact generation occurring with angular offsets of less than one degree. We discuss compensation methods for imperfect aperture placement, finding that small detector sizes produce uncompensatable artifact generation, and compare theoretical predictions with experimental PSF measurements of a rank , 6.8 mm thick (less than one mean free path) coded aperture with a 3.5 mm cell size, conducted at the MegaJOuLe Neutron Imaging Radiography dense plasma focus [IEEE Trans. Plasma Sci. 49, 3299 (2021)] using a 2.45 MeV neutron source. Based on our findings, we recommend using magnified coded apertures in the under-sampled regime, which allows for the inclusion of fiducial markers to characterize aperture–detector rotational offsets and the addition of mechanical coupling, where possible, to constrain rotational and magnification offsets.

Selwood, M. P. [Lawrence Livermore National Labora↗
DOE OSTI2026

Decision Making Under Uncertainty Human Subjects Data - Fire Evacuation Task

This dataset contains de-identified data from human subjects experiments, along with the images and code that were used to run the experiments (as a crowdsourced online study). In this study, participants were shown the probability of a house being in the burn zone of a wildfire. They were asked if they would stay in the house or evacuate in that scenario. The probability information was presented in different ways, including text and maps. The studies tested the impact of different visual cues on the participants' patterns of decisions.

Matzen, Laura E. [Sandia National Laboratories (SN↗
DOE OSTI2024

2D Polyhedral Template Matching for Atomic Resolution Microscopy

SAND2024-13879O The 2D Polyhedral Template Matching for Atomic Resolution Microscopy is a suite of functions for analyzing atomic resolution electron microscopy images using the 2D polyhedral template matching (2D-PTM) method. This software analyzes atomic resolution microscopy data from electron microscopic imaging or scanning probe microscopies. The primary application is for identifying different crystal phases, crystal orientations, and defect structures obtained in such atomic resolution images. Written in MATLAB and starting from an atomic resolution image, the code identifies the positions of atomic intensity peaks. It then matches predefined structural templates to the local atomic environments. Outputs include the local structural identification, the template scaling factor and rotation angle, root-mean-squared deviation (RMSD), and centrosymmetry parameter.

Medlin, Douglas↗
DOE OSTI2022

System and method for remote detection and location of gas leaks

A system for monitoring for a gas leak from a gas containing structure is disclosed. The system includes a lens that directs an image of a scene of interest through an optical filter onto a detector. The filter is associated with the lens and the filter has one or more passbands that passes wavelengths which match one or more emission or reflectively wavelengths of the gas being monitored. A detector receives the image after the image passes through the lens and the filter. The detector generates image data representing the scene including the gas containing structure. A processor is configured to process the image data by executing machine executable code stored on a memory. The machine executable code processes the image data to identify turbulence flows in the image data such that turbulence flow indicates a gas leak, and generate and send an alert in response to the identification of a turbulence flow.

O'Neill, Mary D.↗
DOE OSTI2021

REAL-TIME EDGE INFERENCE FOR PTYCHOGRAPHIC NANOSCALE XRAY IMAGING

The consolidated code is deployed in an embedded GPU System (Jetson AGX Xavier Developer Kit), performs inference on the pVA stream sent from the X-ray detector. The inferred output is sent back to the local detector machine as a PVA stream and can be viewed in an ImageJ viewer.

VASANTHAKUMARIBABU, ANAKHA↗
DOE OSTI2021

Deep learning for lipid droplet recognition in quantitative phase images

This library of Python code is used for performing semantic segmentation of images using 6 different machine learning methods. Five of the methods are implemented entirely within the scikit-learn framework. The Convolutional Neural Network (CNN) method requires Keras with a TensorFlow backend and generally uses a different set of scripts in order to perform the complete training and evaluation.

Sheneman, Lucas↗
DOE OSTI2024

Stereo-DIC Challenge 1.0 – Rigid Body Motion of a Complex Shape

Background Stereo-DIC is a widely used optical measurement technique that provides a dense full-field 3D measurement of the shape, displacement, and strain of a solid sample. When compared with 2D-DIC, Stereo-DIC provides greater flexibility and expands its use beyond flat, planar specimens. Furthermore, the widespread availability of commercial systems has led to the adoption of the technique throughout industry, academia, and government research labs. Objective Even though some research has been done to understand the effects of different experimental and stereo-DIC parameters, no reference is available to benchmark and compare the performance of current stereo-DIC algorithms to each other. Methods This paper provides the description and analysis of a carefully controlled 3D experiment and associated images used to compare the results from five subset based DIC software packages. Both the images and analysis codes used in this paper to compare the results are described here and are available for download and use for continued research. Results We show that over a very large range of motion, the 3D errors are very small, less than 80μm over a travel of ±20 mm out-of-plane and ±20 mm in-plane. While all codes performed similarly, there are important differences noted in the paper. Conclusion The image sets and results comparison software are hosted by the International DIC Society (www.iDICs.org) and are freely available for download and analysis for comparison with results in this paper. Furthermore, it is hoped that this set of images can be used for future research in improving stereo-DIC by future authors.

Algorithms comparison↗
DOE OSTI2024

Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding

Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers significant advantages in terms of energy efficiency, closely mimicking the behavior of biological neurons. In this work, we delve into the role of skip connections, a widely used concept in Artificial Neural Networks (ANNs), within the domain of SNNs with TTFS coding. Our focus is on two distinct types of skip connection architectures: (1) addition-based skip connections, and (2) concatenation-based skip connections. We find that addition-based skip connections introduce an additional delay in terms of spike timing. On the other hand, concatenation-based skip connections circumvent this delay but produce time gaps between after-convolution and skip connection paths, thereby restricting the effective mixing of information from these two paths. To mitigate these issues, we propose a novel approach involving a learnable delay for skip connections in the concatenation-based skip connection architecture. This approach successfully bridges the time gap between the convolutional and skip branches, facilitating improved information mixing. We conduct experiments on public datasets including MNIST and Fashion-MNIST, illustrating the advantage of the skip connection in TTFS coding architectures. Additionally, we demonstrate the applicability of TTFS coding on beyond image recognition tasks and extend it to scientific machine-learning tasks, broadening the potential uses of SNNs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Demonstration of object location, classification, and characterization by developed deep learning dust ablation trail analysis code package using plasma jets

Based on deep learning, a Dust Ablation Trail Analysis (DATA) code package is developed to detect dust ablation trails in tokamaks, which is intended to analyze a large amount data of tokamak dusts. To validate and benchmark the DATA code package, 2440 plasma jet images are exploited for the training and test of the deep learning DATA code package, since plasma jets resemble the shape and size of dust ablation clouds in tokamaks. After being trained by 1920 plasma jet images, the DATA code package is able to locate 100% plasma jets, classify plasma jets with the accuracy of >99.9%, and output image skeleton information for classified plasma jets. The DATA code package trained by the plasma jet images is also used to analyze the dust ablation trails captured in the Experimental Advanced Superconducting (EAST) tokamak with the satisfactory performance, further verifying its applicability in the fusion dust ablation investigation. Based on its excellent performance presented here, it is demonstrated that our DATA code package is able to automatically identify and analyze dust ablation trails in tokamaks, which can be used for further detailed investigations, such as the three-dimensional reconstruction of dusts and their ablation trails.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Orthogonal spatial coding with stimulated parametric down-conversion

Orthogonal optical coding is widely used in classical multi-user communication networks. Using the phase conjugation property of stimulated parametric down-conversion, we extend the current time-domain orthogonal optical coding scheme to the spatial domain to encode and decode image information. In this process, the idler beam inherits the complex conjugate of the field information encoded in the seed beam. An encoding phase mask introduced onto the input seed beam blurs the image transferred to the idler. The original image is restored by passing the coded transferred image through a corrective phase mask placed in the momentum space of the idler beam. We expect that this scheme can also inspire new techniques in secure image transmission, aberration cancellation, and frequency conversion imaging.

Xu, Yang↗
DOE OSTI2021

High-Resolution Scanning Coded-Mask-Based X-ray Multi-Contrast Imaging and Tomography

Near-field X-ray speckle tracking has been used in phase-contrast imaging and tomography as an emerging technique, providing higher contrast images than traditional absorption radiography. Most reported methods use sandpaper or membrane filters as speckle generators and digital image cross-correlation for phase reconstruction, which has either limited resolution or requires a large number of position scanning steps. Recently, we have proposed a novel coded-mask-based multi-contrast imaging (CMMI) technique for single-shot measurement with superior performance in efficiency and resolution compared with other single-shot methods. We present here a scanning CMMI method for the ultimate imaging resolution and phase sensitivity by using a coded mask as a high-contrast speckle generator, the flexible scanning mode, the adaption of advanced maximum-likelihood optimization to scanning data, and the multi-resolution analysis. Scanning CMMI can outperform other speckle-based imaging methods, such as X-ray speckle vector tracking, providing higher quality absorption, phase, and dark-field images with fewer scanning steps. Scanning CMMI is also successfully demonstrated in multi-contrast tomography, showing great potentials in high-resolution full-field imaging applications, such as in vivo biomedical imaging.

Qiao, Zhi (ORCID:0000000233928732)↗
DOE OSTI2024

Fusing 4D coded thermoacoustic, electromagnetic, and acoustic/seismic wavefields for subsurface characterization and imaging of fluid flow in porous media

This research program aims to address critical challenges in subsurface exploration and monitoring of anthropogenic CO 2 storage by advancing quantitative dynamic sensing and imaging technologies. Two primary applications—petrophysical assessment of hydrocarbon reservoirs (C1) and CO 2 storage monitoring (C2)—require significant innovation to overcome three barriers (B1-B3). These barriers include the need for enhanced understanding of interactions between physical wave fields (thermoacoustic, electromagnetic, acoustic/seismic, and X-ray) with fluid-filled porous media, the development of multi-sensor data fusion for real-time imaging, and upscaling microscopic quantum effects for macroscopic observations. The project’s objective was to establish a unified 4D coded sensing and imaging approach, integrating EM, AC/S, and TA fields for multi-scale and multi-physics material characterization and subsurface imaging.

58 GEOSCIENCES↗
DOE OSTI2023

A coded aperture with sub-mean-free-path thickness for implosion geometry imaging on ICF and IFE experiments

Inertial confinement fusion and inertial fusion energy experiments diagnose the geometry of the fusion region through imaging of the neutrons released through fusion reactions. Pinhole arrays typically used for such imaging require thick substrates to obtain high contrast along with a small pinhole diameter to obtain high resolution capability, resulting in pinholes that have large aspect ratios. This leads to expensive pinhole arrays that have small solid angles and are difficult to align. Here, we propose a coded aperture with scatter and partial attenuation (CASPA) for fusion neutron imaging that relaxes the thick substrate requirement for good image contrast. These coded apertures are expected to scale to larger solid angles and are easier to align without sacrificing imaging resolution or throughput. We use Monte Carlo simulations (Geant4) to explore a coded aperture design to measure neutron implosion asymmetries on fusion experiments at the National Ignition Facility (NIF) and discuss the viability of this technique, matching the current nominal resolution of 10 µm. The results show that a 10 mm thick tungsten CASPA can image NIF implosions with neutron yields above 10 14 with quality comparable to unprocessed data from a current NIF neutron imaging aperture. Furthermore, this CASPA substrate is 20 times thinner than the current aperture arrays for fusion neutron imaging and less than one mean free-path of 14.1 MeV neutrons through the substrate. Since the resolution, solid angle, and throughput are decoupled in coded aperture imaging, the resolution and solid angle achievable with future designs will be limited primarily by manufacturing capability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Radiation and heat transport in divergent shock–bubble interactions

Shock–bubble interactions (SBIs) are important across a wide range of physical systems. In inertial confinement fusion, interactions between laser-driven shocks and micro-voids in both ablators and foam targets generate instabilities that are a major obstacle in achieving ignition. Experiments imaging the collapse of such voids at high energy densities (HED) are constrained by spatial and temporal resolution, making simulations a vital tool in understanding these systems. In this study, we benchmark several radiation and thermal transport models in the xRAGE hydrodynamic code against experimental images of a collapsing mesoscale void during the passage of a 300 GPa shock. We also quantitatively examine the role of transport physics in the evolution of the SBI. This allows us to understand the dynamics of the interaction at timescales shorter than experimental imaging framerates. We find that all radiation models examined reproduce empirical shock velocities within experimental error. Radiation transport is found to reduce shock pressures by providing an additional energy pathway in the ablation region, but this effect is small (∼1% of total shock pressure). Employing a flux-limited Spitzer model for heat conduction, we find that flux limiters between 0.03 and 0.10 produce agreement with experimental velocities, suggesting that the system is well-within the Spitzer regime. Higher heat conduction is found to lower temperatures in the ablated plasma and to prevent secondary shocks at the ablation front, resulting in weaker primary shocks. Finally, we confirm that the SBI-driven instabilities observed in the HED regime are baroclinically driven, as in the low energy case.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗