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DOE OSTI2022

AGC 226178 and NGVS 3543: Two Deceptive Dwarfs toward Virgo

The two sources AGC 226178 and NGVS 3543, an extremely faint, clumpy, blue stellar system and a low surface brightness dwarf spheroidal, are adjacent systems in the direction of the Virgo cluster. Both have been studied in detail previously, with it being suggested that they are unrelated normal dwarf galaxies or that NGVS 3543 recently lost its gas through ram pressure stripping and AGC 226178 formed from this stripped gas. However, with Hubble Space Telescope Advanced Camera for Surveys imaging, we demonstrate that the stellar population of NGVS 3543 is inconsistent with being at the distance of the Virgo cluster and that it is likely a foreground object at approximately 10 Mpc, whereas the stellar population of AGC 226178 is consistent with it being a very young (10–100 Myr) object in the Virgo cluster. Through a reanalysis of the original ALFALFA H i detection, we show that AGC 226178 likely formed from gas stripped from the nearby dwarf galaxy VCC 2034, a hypothesis strengthened by the high metallicity measured with MUSE VLT observations. However, it is unclear whether ram pressure or a tidal interaction is responsible for stripping the gas. Object AGC 226178 is one of at least five similar objects now known toward Virgo. These objects are all young and unlikely to remain visible for over ~500 Myr, suggesting that they are continually produced in the cluster.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Evaluating the effectiveness of a camera-based detection system to support informed curtailment and minimize eagle fatalities at wind energy facilities

The American Wind Wildlife Institute (AWWI – now Renewable Energy Wildlife Institute, REWI) – was appointed prime awardee of DOE award number DE-EE0007880 to lead a team of scientists, wind developers, and technology manufacturers in a collaborative evaluation of the effectiveness of a technology intended to help reduce eagle collisions with wind turbines. IdentiFlight, developed by Renewable Energy Systems (RES) in collaboration with Boulder Imaging, is an autonomous aerial monitoring and detection system. REWI partnered with ecological consultants, Western Ecosystems Technology, Inc. (WEST), to independently evaluate the performance of IdentiFlight reducing eagle collision risk under a suite of environmental conditions relative to the performance of a Biomonitor (human observer-based) Program. The project objectives were to 1) evaluate how IdentiFlight compared to biomonitors in detecting and identifying eagles under a suite of environmental conditions and 2) use the observation data to model how IdentiFlight and biomonitors reduced the eagle fatality prediction through informed curtailment. Field testing was to take place at two geographically distinct wind farms over two field seasons at each wind farm with a study design appropriate for meta-analyses across sites and years.

17 WIND ENERGY↗

DOE OSTI2024

Topography and Functional Traits Control the Distribution of Key Shrub Plant Functional Types in Low-Arctic Tundra: Supporting Data

High-resolution classification maps derived from occupied aerial systems (UASs). The UAS data were collected in August 2021 using a Skydio 2+ drone equipped with a 4K resolution red-green-blue (RGB) camera (2024 Skydio Inc) and a 3DR SOLO Quadcopter carried a Parrot Sequoia+ Multispectral Sensor (2023 Parrot Drone SAS). This package includes vegetation classification maps at four locations around Next Generation Ecosystem Experiment Arctic (NGEE Arctic) Council watershed study site on the Seward Peninsula, Alaska. The classification maps were generated using a combination of RGB and canopy height information. The map data and metadata are provided as ENVI image (.dat) and text (.txt, *hdr) formats. Additional map quicklooks are provided as GIS *.kml files. These datasets are provided in support of Yang et al., (In review), “Topography and Functional Traits Control the Distribution of Key Shrub Plant Functional Types in Low-Arctic Tundra”.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Source localization for neutron imaging systems using convolutional neural networks

The nuclear imaging system at the National Ignition Facility (NIF) is a crucial diagnostic for determining the geometry of inertial confinement fusion implosions. The geometry is reconstructed from a neutron aperture image via a set of reconstruction algorithms using an iterative Bayesian inference approach. An important step in these reconstruction algorithms is finding the fusion source location within the camera field-of-view. Currently, source localization is achieved via an iterative optimization algorithm. In this paper, we introduce a machine learning approach for source localization. Specifically, we train a convolutional neural network to predict source locations given a neutron aperture image. We show that this approach decreases computation time by several orders of magnitude compared to the current optimization-based source localization while achieving similar accuracy on both synthetic data and a collection of recent NIF deuterium–tritium shots.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Source localization for neutron imaging systems using convolutional neural networks

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Vegetation Warming Experiment: Chamber and ambient plot digital camera imagery for vegetation phenology, Utqiagvik (Barrow), Alaska, 2018

Time lapse photography of experimental plots within five warming chambers (ZPWs) and paired control plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. Images were recorded from 17 June to 24 September, 2018 to capture vegetation dynamics during the growing season. Vegetation phenology, including green up and senescence were captured. Images are *.jpg files recorded daily at 30 minute intervals, from 11:00 - 14:30 Alaska daylight time (AKDT, UTC-8), using Wingscapes TimelapseCam cameras. The target species was Arctagrostis latifolia. Additional information in *.pdf and *.csv files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Chemical and physical transformations of carbon-based nanomaterials observed by liquid phase transmission electron microscopy

This article addresses recent advances in liquid phase transmission electron microscopy (LPTEM) for studying nanoscale synthetic processes of carbon-based materials that are independent of the electron beam—those driven by nonradiolytic chemical or thermal reactions. In particular, we focus on chemical/physical formations and the assembly of nanostructures composed of organic monomers/polymers, peptides/DNA, and biominerals. The synthesis of carbon-based nanomaterials generally only occurs at specific conditions, which cannot be mimicked by aqueous solution radiolysis. Carbon-based structures themselves are also acutely sensitive to the damaging effects of the irradiating beam, which make studying their synthesis using LPTEM a unique challenge that is possible when beam effects can be quantifi ed and mitigated. With new direct sensing, high frame rate cameras, and advances in liquid-cell holder designs, combined with a growing understanding of irradiation effects and proper experimental controls, microscopists have been able to make strides in observing traditionally problematic carbon-based materials under conditions where synthesis can be controlled, and imaged free from beam effects, or with beam effects quantified and accounted for. These materials systems and LPTEM experimental techniques are discussed, focusing on nonradiolytic chemical and physical transformations relevant to materials synthesis.

Parent, Lucas R.↗

DOE OSTI2021

Performance of Pulsed Thermal Tomography Imaging with Machine Learning-Based Classification of Defects in Additively Manufactured Structures

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures non-destructively. Pulsed Thermal Tomography Imaging (PTT) provides a capability for non-destructive evaluation (NDE) of subsurface defects in arbitrary size structures. The PTT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PTT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PTT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PTT capabilities in detecting and visualizing microscopic defects in metallic specimens. The thermal tomography (TT) algorithm obtains depth reconstructions of spatial effusivity from the data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring of images with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images. CNN is trained on a database of TT images created for a set of simulated metallic structures with elliptical subsurface voids. Test of CNN performance demonstrate the ability to classify radii and angular orientation of subsurface defects in TT images. In addition, we have shown that CNN trained on elliptical defects is capable of classifying irregular-shaped defects obtained from scanning electron microscopy (SEM) of stainless steel sections printed with LPBF.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components. Third Annual Progress Report

Additive manufacturing (AM) of high-strength corrosion resistance alloys for nuclear energy applications, such as stainless steel and Inconel, is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Probability of crack initiation at the pore depends on size, shape, and orientation of the defect. Pulsed Infrared Thermography Imaging (PIT) provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PIT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PIT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PIT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PIT capabilities in detecting microscopic subsurface defects in metals, and classifying shapes and orientation of pores in thermal images. For detection of microscopic defects in PIT imaging data, we have developed Spatial Temporal Denoised Thermal Source Separation (STDTSS) unsupervised machine learning (ML) image processing algorithm. We show that flat bottom hole (FBH) defects as small as 200µm in SS316 and IN718 specimens, can be detected with STDTSS algorithm. To the best of our knowledge, these are the smallest detected defects which are reported in literature. For classification of defects shapes, we have previously developed thermal tomography (TT) algorithm to obtain depth reconstructions of material defects from data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Vegetation Warming Experiment: Landscape-scale digital camera imagery for vegetation phenology, Utqiagvik (Barrow), Alaska, 2018

Images captured using a StarDot NetCam SC phenocamera looking east from the top of the Barrow Environmental Observatory (BEO) Sled Shed, Utqiagvik, Alaska. The camera was installed to remotely monitor plant phenology and operation of the BNL TEST group's ZPW (Zero Power Warming) chambers during the growing season of 2018. Images were captured from early spring (13 April) through to mid fall (18 October). Snowmelt, vegetation growth and senescence, and snow accumulation were captured. Images were uploaded to the BNL FTP server every hour until 16 June, and then every 10 minutes. Files were renamed with the date and time of the image. jpg images have been compressed in *.tar.gz format (7.9 GB). Different fields of view (northeasterly) were also captured using 3 Wingscapes TimelapseCam cameras mounted on a mast on the sled shed. Images were recorded from 21 June to 24 September, 2018, at 30 minute intervals, from 11:00 - 14:30, Alaska daylight time (AKDT, UTC-8). Movie files (.mp4) of each Wingscapes camera dataset. Additional metadata included in *.pdf and *.csv files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Vegetation Warming Experiment: Chamber and ambient plot digital camera imagery for vegetation phenology, Utqiagvik (Barrow), Alaska, 2017

Time lapse photography (*.jpg) of experimental plots within five warming chambers (ZPWs) and paired control plots located on the Barrow Environmental Observatory (BEO), Barrow (now Utqiagvik), Alaska. Images were recorded from 21 June to 18 September, 2017 to capture vegetation dynamics during the growing season. Vegetation phenology, including green up and senescence were captured. Images were recorded daily at 30 minute intervals, from 11:00-14:30 Alaska daylight time (AKDT, UTC-8), using Wingscapes TimelapseCam cameras. The target species was Petasites frigidus. Also included multiple metadata files including reporting formats as *.pdf and *.csv files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Handheld multi-sensor biometric imaging device and processing pipeline

A handheld biometric imaging device having an array of cameras configured to simultaneously capture face, iris and fingerprint biometrics of a subject. The device includes a plurality of visible-light cameras and a plurality of infrared-light cameras capable of being triggered simultaneously to obtain a plurality of images from which a 3D image of the light field can be constructed. The device includes a plurality of visible-light illuminators and a plurality of infrared-light illuminators that allow images of a subject to be captured under different lighting profiles. The device may include an onboard control system that is capable of reconstructing a face region, an iris region and a fingerprint region from the 3D light-field image, and then extract a corresponding face template, an iris template, and a fingerprint template from the respective reconstructed regions. The extracted face, iris, and fingerprint templates may be used for enrollment and/or comparison against a biometric-template database associated with one or more watchlists.

Bolme, David S.↗

DOE OSTI2020

An Assessment of Homogeneous Mixture Method Cavitation Models in Predicting Cavitation in Nozzle Flow

The homogeneous mixture method (HMM) is a popular class of models used in the computational prediction of cavitation. Several cavitation models have been developed for use with HMM to govern the development and destruction of vapor in a fluid system. Two models credited to Kunz and Schnerr–Sauer are studied in this paper. The goal of this work is to provide an assessment of the two cavitation submodels in their ability to predict cavitation in nozzle flow. Validation data were obtained via experiments which employ both passive cavitation detection, (PCD) via acoustic sensing and optical cavitation detection (OCD) via camera imaging. The experiments provide quantitative information on cavitation inception and qualitative information on the vapor in the nozzle. The results show that initial vapor formation is not predicted precisely but within reason. A sensitivity analysis of the models to input parameters shows that the Schnerr–Sauer method does not depend upon the estimation of nuclei size and number density. Small changes in the vapor formation rate but not the total vapor volume can be seen when weighting parameters are modified. In contrast, changes to the input parameters for the Kunz model greatly change the final total vapor volume prediction. The assessment also highlights the influence of vapor convection within the method. Finally, the analysis shows that if the fluid and nozzle walls do not support nuclei larger than 40 μm, the methods would still predict cavitation when indeed there would be none in practice.

Engineering↗

DOE OSTI2025

Smartphone application for visualizing building air leakage

Annually, unwanted air leakage through building envelopes accounts for 4 quads of energy consumption in the United States, which translates to about 10% of total building energy consumption. Locating and sealing leakage sites is crucial fordecreasing building energy consumption. Smartphones are ubiquitous and contain sophisticated cameras and highperformance processors that could be employed to visualize air leakage using the background-oriented schlieren imaging technique, making leak detection cheaper and easier. This technique requires a textured and high-contrast background such as a brick or concrete masonry unit wall, a building air leak that has a temperature difference compared with the ambient air, and an imaging system. This work focuses on using smartphones as the imaging system to visualize air leakages. The paper discusses application development and the results of testing to determine leakage visualization performance as a function of leak temperature. The results show that leaks with a temperature difference greater than 16°C compared with the ambient air temperature were visualized using existing smartphones.

Boudreaux, Philip [ORNL] (ORCID:0000000229564665)↗

DOE OSTI2024

Angle Assessment for Upper Limb Rehabilitation: A Novel Light Detection and Ranging (LiDAR)-Based Approach

The accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost–benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology.

Klein, Luan C. (ORCID:000000016306574X)↗

DOE OSTI2024

Effect of a collapsing gas bubble on the shock-to-detonation transition in liquid nitromethane

We studied the shock-induced collapse of butane gas bubbles in the homogeneous explosive nitromethane (NM) to investigate the effects of hot spot formation on the detonation process. A butane bubble was injected into a sample of NM, and a shock wave from a flat plate impactor compressed the bubble, creating a localized hot spot. We measured shock and detonation wave speeds with optical velocimetry, and we used a high-speed camera to image the shock propagation and bubble collapse processes. A multiband optical fiber pyrometer measured the time-resolved thermal radiance, and we used the results and emissivity values extracted from spectral fits to estimate temperatures. We measured the characteristics of the shock-to-detonation transition in NM with and without a bubble. All experiments were performed at shock pressures near 8 GPa, where neat NM can detonate. A single bubble in this system was shown to sensitize NM, leading to a reduced run-to-detonation time. We used hydrodynamic modeling to predict shock wave propagation, the extent of chemical reaction, and subsequent temperature rise from the collapsing bubble. We used a temperature-dependent Arrhenius burn model for simulations, and it yielded much better results than reactive burn models that depend only on pressure and density.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

The STRong lensing Insights into the Dark Energy Survey (STRIDES) 2017/2018 follow-up campaign: discovery of 10 lensed quasars and 10 quasar pairs

Here, we report the results of the STRong lensing Insights into the Dark Energy Survey (STRIDES) follow-up campaign of the late 2017/early 2018 season. We obtained spectra of 65 lensed quasar candidates with ESO Faint Object Spectrograph and Camera 2 on the NTT and Echellette Spectrograph and Imager on Keck, confirming 10 new lensed quasars and 10 quasar pairs. Eight lensed quasars are doubly imaged with source redshifts between 0.99 and 2.90, one is triply imaged (DESJ0345-2545, z = 1.68), and one is quadruply imaged (quad: DESJ0053-2012, z = 3.8). Singular isothermal ellipsoid models for the doubles, based on high-resolution imaging from SAMI on Southern Astrophysical Research Telescope or Near InfraRed Camera 2 on Keck, give total magnifications between 3.2 and 5.6, and Einstein radii between 0.49 and 1.97 arcsec. After spectroscopic follow-up, we extract multi-epoch grizY photometry of confirmed lensed quasars and contaminant quasar + star pairs from DES data using parametric multiband modelling, and compare variability in each system’s components. By measuring the reduced χ 2 associated with fitting all epochs to the same magnitude, we find a simple cut on the less variable component that retains all confirmed lensed quasars, while removing 94 per cent of contaminant systems. Based on our spectroscopic follow-up, this variability information improves selection of lensed quasars and quasar pairs from 34-45 per cent to 51–70 per cent, with most remaining contaminants being star-forming galaxies. Using mock lensed quasar light curves we demonstrate that selection based only on variability will over-represent the quad fraction by 10 per cent over a complete DES magnitude-limited sample, explained by the magnification bias and hence lower luminosity/more variable sources in quads.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Cadillac CT6 Super Cruise On-road Data

This dataset encompasses about 60 individual drives of a 2019 Cadillac CT6 with Super Cruise in its relevant operational domain covering more than 1,000 miles. Adhering to the limited operational design domain of the investigated version of the Supercruise system, the majority of the data was collected during highway driving. Information collected includes vehicle CAN data as well as Lidar and camera data from a vehicle mounted sensor array. Vehicle CAN data and information on traffic surrounding the Ego-vehicle derived from the sensor array are postprocessed and merged to provide one combined CVS data file per drive. ![cadillac-ct6 image](ct6.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗