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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

MIRC-X: A Highly Sensitive Six-telescope Interferometric Imager at the CHARA Array

Michigan InfraRed Combiner-eXeter (MIRC-X) is a new highly sensitive six-telescope interferometric imager installed at the CHARA Array that provides an angular resolution equivalent of up to a 330 m diameter baseline telescope in J- and H-band wavelengths ((λ/(2B))∼0.6 mas). We upgraded the original Michigan InfraRed Combiner (MIRC) instrument to improve sensitivity and wavelength coverage in two phases. First, a revolutionary sub-electron noise and fast-frame-rate C-RED ONE camera based on an SAPHIRA detector was installed. Second, a new-generation beam combiner was designed and commissioned to (i) maximize sensitivity, (ii) extend the wavelength coverage to J band, and (iii) enable polarization observations. A low-latency and fast-frame-rate control software enables high-efficiency observations and fringe tracking for the forthcoming instruments of the CHARA Array. Since mid-2017, MIRC-X has been offered to the community and has demonstrated best-case H-band sensitivity down to 8.2 correlated magnitude. MIRC-X uses single-mode fibers to coherently combine the light from six telescopes simultaneously with an image-plane combination scheme and delivers a visibility precision better than 1%, and closure phase precision better than 1°. MIRC-X aims at (i) imaging protoplanetary disks, (ii) detecting exoplanets with precise astrometry, and (iii) imaging stellar surfaces and starspots at an unprecedented angular resolution in the near-infrared. In this paper, we present the instrument design, installation, operation, and on-sky results, and demonstrate the imaging capability of MIRC-X on the binary system ι Peg. The purpose of this paper is to provide a solid reference for studies based on MIRC-X data and to inspire future instruments in optical interferometry.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Double Image Polarimeter—Ultra Fast: Simultaneous Three-color (BVR) Polarimeter with Electron-multiplying Charge-coupled Devices

We describe a new instrument capable of high-precision (10{sup −5}) polarimetric observations simultaneously in three passbands (BVR). The instrument utilizes electron-multiplying charge-coupled device (EM CCD) cameras for high efficiency and fast image readout. The key features of the Double Image Polarimeter—Ultra Fast (DIPol-UF) are: (i) the optical design with high throughput and inherent stability; (ii) great versatility, which makes the instrument optimally suitable for observations of bright and faint targets; and (iii) a control system, which allows the use of the polarimeter remotely. Examples are given of the first results obtained from high signal-to-noise observations of bright nearby stars and of fainter sources such as X-ray binaries in their quiescent states.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Earth as a Proxy Exoplanet: Deconstructing and Reconstructing Spectrophotometric Light Curves

Point-source spectrophotometric (single-point) light curves of Earth-like planets contain a surprising amount of information about the spatial features of those worlds. Spatially resolving these light curves is important for assessing time-varying surface features and the existence of an atmosphere, which in turn is critical to life on Earth and significant for determining habitability on exoplanets. Given that Earth is the only celestial body confirmed to harbor life, treating it as a proxy exoplanet by analyzing time-resolved spectral images provides a benchmark in the search for habitable exoplanets. The Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) provides such an opportunity, with observations of ∼5000 full-disk sunlit Earth images each year at 10 wavelengths with high temporal frequency. We disk-integrate these spectral images to create single-point light curves and decompose them into principal components (PCs). Using machine-learning techniques to relate the PCs to six preselected spatial features, we find that the first and fourth PCs of the single-point light curves, contributing ∼83.23% of the light-curve variability, contain information about low and high clouds, respectively. Surface information relevant to the contrast between land and ocean reflectance is contained in the second PC, while individual land subtypes are not easily distinguishable (<0.1% total light-curve variation). We build an Earth model by systematically altering the spatial features to derive causal relationships to the PCs. This model can serve as a baseline for analyzing Earth-like exoplanets and guide wavelength selection and sampling strategies for future observations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

The Initial Mass Function of Low-mass Stars and Brown Dwarfs in the W3 Complex

We have used archival infrared images obtained with the Wide Field Camera 3 on board the Hubble Space Telescope to constrain the initial mass function of low-mass stars and brown dwarfs in the W3 star-forming region. The images cover 438 arcmin{sup 2}, which encompasses the entire complex, and were taken in the filters F110W, F139M, and F160W. We have estimated extinctions for individual sources in these data from their colors and have dereddened their photometry accordingly. By comparing an area of the images that contains the richest concentration of previously identified W3 members to an area that has few members and is dominated by background stars, we have estimated the luminosity function for members of W3 with masses of 0.03–0.4 M {sub ⊙}. That luminosity function closely resembles data in typical nearby star-forming regions that have much smaller stellar populations than W3 ( ≲ 500 versus several thousand objects). Thus, we do not find evidence of significant variations in the initial mass function of low-mass stars and brown dwarfs with star-forming conditions, which is consistent with recent studies of other distant massive star-forming regions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

cogs.c1

COGS is a four-dimensional (4D) map of cloudiness generated by multiview stereo reconstruction using the ARM stereo camera ring at the Southern Great Plains (SGP) atmospheric observatory. These data are particularly useful for studies of the life cycles and macrophysical attributes of shallow cumulus clouds. COGS covers a 6 km x 6 km x 6 km region at the SGP Central Facility centered at the position of the Doppler lidar.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Local Tractor Activity at S2 (Mt. Airy) Site

This dataset documents local tractor activity at the S2 (Mt. Airy) site during CoURAGE. It identifies periods where local tractor activity impacted aerosol measurements using both PM10/PM2.5 ratios and ARM camera data for visual confirmation of tractor. Data flags are as follows: 1 = confirmed no local tractor influence on aerosol measurements 2 = one data indicator is present (extreme PM10/PM2.5 ratio OR visual confirmation of tractor sighting), but not both 3 = both data indicators confirm local tractor influence on aerosol 0 = missing data, no determination possible

Tractor Activity using ARM camera photos and PM10/↗
DOE OSTI2020

Savannah River Site H-Canyon Process Exhaust Tunnel Inspection Crawler Development - 20036

This paper covers development of a remote crawler used to inspect a subterranean radiochemical process ventilation tunnel located at the Savannah River Site near Aiken, SC. The purpose of the inspection was to ascertain the apparent structural condition of the tunnel with video footage. Manned entry is prohibited due to the hazardous environment requiring a fully-remote approach. The inspection system used high-definition cameras to archive over 1000 linear feet in the tunnel. While previous inspections had been undertaken, this crawler was developed to overcome obstacles that had put tunnel regions out of reach of prior inspection platforms. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Infrastructure-Based Cooperative Perception at a Traffic Intersection: Overview and Challenges

Traffic intersections are crucial and challenging nodes in transportation networks where multiple lanes of vehicles and pedestrians converge. About one-quarter of traffic fatalities and about one-half of all traffic injuries in the United States happen at traffic intersections . Effective management of these intersections is important to ensure safety and efficiency of all users - vehicles, pedestrians, cyclists, and vulnerable road users (VRUs). With advancements in sensor perception technologies such as radar, light detection and ranging (lidar), and cameras, traffic intersections are developing into dynamic and data-rich environments. By using these data to create a real-time digital twin, we can enable real-time data-driven decision making and a range of applications such as sharing perception information to connected vehicles (CVs) and connected autonomous vehicles (CAVs), safety affirmative signaling, and curb optimizing to improve efficiency and enhance safety.This paper presents an overview of the concept and examines the challenges involved in implementing an infrastructure-based cooperative perception engine at a traffic intersection. In addition to outlining the physical components, this study also addresses important challenges involved in a multi-sensor system. We present results from deploying the National Renewable Energy Laboratory's (NREL's) Infrastructure Perception and Control (IPC) mobile trailer at a traffic intersection in the city of Colorado Springs, Colorado, USA that employed multiple radars and lidars to capture the data. This study provides necessary practical learning for the Cooperative Driving Automation (CDA) and traffic engineering communities for next-generation infrastructure-based cooperative perception that promises improvements in signal control for optimized traffic flow, among other applications, and documents findings for ongoing research and development efforts in other areas.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2022

Self‐Trapped and Free Exciton Dynamics in Vacuum‐Deposited Cesium Copper Iodide Thin Films

Abstract Highly photoluminescent, lead‐free perovskites are of interest for displays and solid‐state light‐emitting devices. In this report, streak camera‐based time‐resolved emission and transient absorption spanning visible to deep‐ultraviolet (UV) wavelengths are utilized to study self‐trapped and free exciton dynamics in vacuum‐deposited cesium copper halide thin films of CsCu 2 I 3 and Cs 3 Cu 2 I 5 . Self‐trapped exciton emission of CsCu 2 I 3 exhibits more noticeable changes with time in the peak position and width than Cs 3 Cu 2 I 5 . UV‐to‐blue emission is detectable for both compositions, where free exciton emission is distinct for CsCu 2 I 3 . Transient absorption shows loss of ground‐state bleach signals at early time delays for both, and the bleach signal shifts toward higher energy as time delay increases, likely due to strains induced by the newly created self‐trapped excitons. Global analysis performed on the transient absorption results yields time constants in these materials that build an overall dynamic scheme. This work aids in building a complete picture regarding light emission in these promising materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Spatially Resolved Performance Analysis for Perovskite Solar Cells

This review explores the current state of the art in spatially resolved characterization of mixed-halide perovskite solar cells. As the size of perovskite cells and modules continues to grow, quantification of the spatial distribution of key cell parameters will become increasingly valuable in predicting ultimate cell-level performance and tracking process homogeneity. Here, both high resolution microscopic approaches using scanning techniques and camera-based methods for full-area cell and/or module analysis are discussed. The value of this local data in predicting performance losses at the cell level is particularly emphasized. Measurable physical parameters sensitive to losses of voltage, current, fill factor, and efficiency are discussed together with selected experimental results. It is demonstrated that a combination of spatially resolved cell parameter mapping/imaging can be used to quantitatively discriminate various loss contributions at high resolution. The impact and control of inhomogeneities become particularly important when upscaling from small devices to large formats compatible with industrial mass production.

14 SOLAR ENERGY↗
DOE OSTI2020

Atomic‐Resolution Imaging of Halide Perovskites Using Electron Microscopy

Abstract Atomic‐resolution imaging of halide perovskites (HPs) using transmission electron microscopy (TEM) is challenging because of the sensitivity of their structures to the electron beam. In this article, recent achievements in this area are reviewed, covering both all‐inorganic and organic–inorganic hybrid HPs, with an emphasis on the specific imaging conditions that have proven to be effective in avoiding electron beam‐induced structural damage. The discussion focusses on the total electron dose that HPs can bear before being damaged and the effects of different imaging modes, accelerating voltages, and temperatures. The crucial role of a direct‐detection electron‐counting camera in reducing the required electron dose is outlined, which is indispensable for imaging extremely sensitive organic–inorganic hybrid perovskites. In addition to reviewing published works, the results of initial attempts to perform atomic‐resolution elemental mapping for an all‐inorganic HP and image a hybrid HP using scanning TEM are introduced. The preparation of a TEM specimen from macroscopic crystals or devices of HPs, which is very important for practical applications but has not yet received attention, is also discussed. This article aims to provide guidance on the acquisition of atomic‐resolution TEM images of HPs and inspire the development of more imaging technologies for sensitive materials.

Song, Kepeng↗
DOE OSTI2021

Population balance modeling of polyurethane foam formation with pressure‐dependent growth kernel

Abstract Polyurethane foams are widely used materials often chosen for their useful characteristics such as low thermal conductivity, ease of application, and high strength‐to‐weight ratios. Computational models are needed to predict the dynamics of the flow and expansion, and the resulting material properties, to improve manufacturing processes. In this paper, a model for PMDI, a water‐blown polyurethane foam, is presented. By extending a kinetics‐based approach by adding bubble‐scale information via a population balance equation (PBE) using the quadrature method of moments, we can track bubble size distributions during foaming. We present results from a three‐dimensional computational fluid dynamics model using arbitrary Lagrangian–Eulerian interface tracking implemented in finite element software. The model compares favorably with experimental data, including dynamics, bubble distributions measured by both camera and diffusion wave spectroscopy, and post‐test bubble size from scanning electron microscopy and density measurements from x‐ray computed tomography.

Ortiz, Weston↗
DOE OSTI2023

Time‐Lapse Image Classification Using a Diffractive Neural Network

Diffractive deep neural networks (D 2 NNs), comprised of spatially engineered passive surfaces, collectively process optical input information at the speed of light propagation through a thin diffractive volume, without any external computing power. Diffractive networks were demonstrated to achieve all‐optical object classification and perform universal linear transformations. Herein, a “time‐lapse” image classification scheme using a diffractive network is demonstrated for the first time, significantly advancing its classification accuracy and generalization performance on complex input objects by using the lateral movements of the input objects and/or the diffractive network, relative to each other. In a different context, such relative movements of the objects and/or the camera are routinely being used for image super‐resolution applications; inspired by their success, a time‐lapse diffractive network is designed to benefit from the complementary information content created by controlled or random lateral shifts. The design space and performance limits of time‐lapse diffractive networks are numerically explored, revealing a blind testing accuracy of 62.03% on the optical classification of objects from the CIFAR‐10 dataset. This constitutes the highest inference accuracy achieved so far using a single diffractive network on the CIFAR‐10 dataset. Time‐lapse diffractive networks will be broadly useful for the spatiotemporal analysis of input signals using all‐optical processors.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Smart connected worker edge platform for smart manufacturing: Part 2—Implementation and on‐site deployment case study

Abstract In this paper, we describe specific deployments of the Smart Connected Worker (SCW) Edge Platform for Smart Manufacturing through implementation of four instructive real‐world use cases that illustrate the role of people in a Smart Manufacturing paradigm through which affordable, scalable, accessible, and portable (ASAP) information technology (IT) acquires and contextualizes data into information for transmission to operation technologies (OT). For case one, the platform captures the relationships between energy consumption and human workflows for improved energy productivity while workers interact with machines during semiconductor manufacturing. The platform utilizes human cognition to identify anomalous machine behavior for root cause analysis of system faults via neural network (NN) that recognize alarm postures of workers with cameras. For case two, a smart assembly line is demonstrated for state monitoring and fault detection. Machine learning (ML) models are used to recognize system states and identify fault scenarios with human intervention. For case three, the platform monitors human–machine interactions to classify manufacturing machine states for proper operations and energy productivity. Internal energy states of individual or collections of manufacturing equipment are determined via NN based algorithms that disaggregate signals associated with smart metering typically deployed at manufacturing facilities. These methods predict the real time energy profile of each machine from the total energy profile of a manufacturing site. For case four, a software defined sensor system built with scientific workflow engines is demonstrated for contextualizing data from laser surface refraction for characterization, and diagnostics in the processing of additively manufactured titanium alloy.

Donovan, Richard P.↗
DOE OSTI2025

Contaminant Investigation and Pre‐Processing Opportunities for Textile‐To‐Textile Recycling

Millions of metric tons of textiles are landfilled or incinerated each year in the United States, with less than 1% of textiles recycled into new clothing or fabrics. To counter this trend, a growing number of companies and researchers are exploring how a circular economy can be applied to support textile‐to‐textile recycling. A significant barrier they face comes down to quickly and efficiently extracting pure feedstock material from post‐consumer garments that feature a mix of natural and synthetic fibers. Textile recyclers prefer pure feedstocks, as working with mixed sources typically means lower throughput, higher risk of equipment failure, and diminished business margins. To facilitate a circular economy for textiles, methods, and technologies are needed that can efficiently separate out materials and contaminants from end‐of‐life textiles to increase the flow of pure feedstocks to recyclers. This paper summarizes findings from interviews with a cross section of textile recyclers and from a review of literature to define basic feedstock requirements. In addition to our qualitative research, we deconstruct a bale of post‐consumer textiles and analyze them using computer‐vision imaging, Fourier transform infrared spectroscopy (FTIR), and machine learning. The resulting data are used to set system‐level design inputs for an automated contaminant removal system to process post‐consumer clothing into appropriate feedstocks for recycling. To set the system's levels for automated real‐time near‐infrared analysis, we identify the minimum percentage of primary material that any single garment in a load of used clothing must contain for the average of the full output stream to meet the target purity levels of recyclers. Here, the envisioned automated system can also address undesirable trace materials that might contaminate the processed stream by using imaging cameras coupled with artificial intelligence to identify sections of clothing for de‐trimming. Proof‐of‐concept machine learning algorithms are evaluated to locate and identify trims or garment areas with hidden contaminant materials. Integrating these methods into automated textile cutting systems can provide a cost‐effective means for increasing feedstock purity from used clothing, which can advance circularity for textiles by helping recyclers to reach production volumes and quality targets that were not possible solely with manual dismantling operations.

Parsons, Ryan [Rochester Institute of Technology, ↗
DOE OSTI2020

Super resolution for root imaging

Premise High‐resolution cameras are very helpful for plant phenotyping as their images enable tasks such as target vs. background discrimination and the measurement and analysis of fine above‐ground plant attributes. However, the acquisition of high‐resolution images of plant roots is more challenging than above‐ground data collection. An effective super‐resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors, reducing storage space requirements, and boosting the performance of subsequent analyses. Methods We propose an SR framework for enhancing images of plant roots using convolutional neural networks. We compare three alternatives for training the SR model: (i) training with non‐plant‐root images, (ii) training with plant‐root images, and (iii) pretraining the model with non‐plant‐root images and fine‐tuning with plant‐root images. The architectures of the SR models were based on two state‐of‐the‐art deep learning approaches: a fast SR convolutional neural network and an SR generative adversarial network. Results In our experiments, we observed that the SR models improved the quality of low‐resolution images of plant roots in an unseen data set in terms of the signal‐to‐noise ratio. We used a collection of publicly available data sets to demonstrate that the SR models outperform the basic bicubic interpolation, even when trained with non‐root data sets. Discussion The incorporation of a deep learning–based SR model in the imaging process enhances the quality of low‐resolution images of plant roots. We demonstrate that SR preprocessing boosts the performance of a machine learning system trained to separate plant roots from their background. Our segmentation experiments also show that high performance on this task can be achieved independently of the signal‐to‐noise ratio. We therefore conclude that the quality of the image enhancement depends on the desired application.

Ruiz‐Munoz, Jose F.↗
DOE OSTI2025

Raspberry Pi–powered temperature monitoring of growth chamber microclimates

While controlled environments are desirable for growing and measuring plants, growth chambers and greenhouses typically have microclimates that impact plant growth, development, and stress responses. Furthermore, opening and closing the doors of a controlled environment introduces variation in the environment, especially at temperature extremes, affecting both the measurements and the organisms within. Using multiple temperature data loggers to normalize results can be cost-prohibitive and rarely offers real-time feedback on temperature status. We used low-cost single-board computers, cameras, and temperature sensors to manage and capture growth chamber temperatures while acquiring plant image data. Detailed here are methods to document microclimates within a growth chamber so that data can be normalized to measured temperature information. This protocol describes a low-cost method for automated temperature monitoring, which enables both high-throughput measurements of temperature along with plant growth and stress responses via plant imaging.

Plant Sciences↗
DOE OSTI2020

Past and present disturbances generate spatial variation in seed predation

Seed survival is a key process for plant populations; variation in the activity and abundance of animals that consume seeds can lead to dramatic shifts in seed fate. Because granivores may respond to contemporary disturbance as well as to enduring changes in habitats caused by past disturbances, understanding seed fate requires studies capable of evaluating how past and present disturbances modify granivore communities, foraging activity, and ultimately, seed predation. Historic agricultural land use and contemporary canopy harvesting are widespread disturbances that could generate large-scale patterns of seed fate by modifying environmental characteristics that determine granivore identity and behavior. Here, to evaluate whether land-use history and canopy harvesting affect seed–animal interactions, we conducted an experiment distributed across 80,000 ha of longleaf pine woodlands that coupled large-scale canopy harvesting at seven 4-ha sites containing both post-agricultural land use and nonagricultural land use in South Carolina, United States. We deployed a total of 28,000 nail-tagged seeds and recovered the tags to quantify seed fate. Past agricultural land use and contemporary canopy harvesting interacted to affect the rate of seed predation. Seed predation rates in harvested sites depended on land-use history: Seed predation was 30% lower in post-agricultural plots than in nonagricultural plots. This interaction was driven by the differential effect of land-use history and canopy harvesting on rodent activity. Camera traps revealed that Sigmodon hispidus only foraged in harvested plots and was most active in nonagricultural plots. In harvested plots, seed removal increased with S. hispidus activity. In unharvested plots, seed removal increased with Sciurus niger activity, but S. niger was not affected by land-use history. In finding that land-use history and canopy harvesting determine the outcomes of seed–animal interactions, we show that understanding patterns of seed predation is contingent upon the interplay of disturbances in both the distant past and recent past. These results suggest that patterns of past land use and present land use may help reconcile the considerable variation in seed fate observed in ecological communities.

60 APPLIED LIFE SCIENCES↗