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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Characterization of a mock up nuclear waste package using energy resolved MeV neutron analysis

Reliable radiographic methods for characterizing nuclear waste packages non-destructively (without the need to open containers) have the potential to significantly contribute to safe handling and future disposal options, particularly for legacy waste of unknown content. Due to required shielding of waste containers and the need to characterize materials consisting of light elements, X-ray methods are not suitable. Here, energy-resolved MeV neutron radiography is demonstrated as a first-of-its-kind application for non-destructive and remote examination of mock up nuclear waste packages from a safe position using time-of-flight techniques enabled by a novel event-mode imaging detector system. Energy-resolved neutron transmission spectra were measured spatially, permitting the detection of analogue materials to actual nuclear waste such as water, melamine, and ion exchange resin within a 2.54 cm wall thickness steel pipe. The results demonstrate the capability to locate the materials through this wall thickness by radiography and tomographic reconstruction, revealing detailed 3D distributions and structural anomalies. The method effectively detects residual water in ion exchange resin, highlighting its sensitivity to moisture content, a crucial parameter for nuclear waste characterization. Monte Carlo simulations are in agreement with the experimental findings, providing a pathway to simulate waste forms more difficult to tackle experimentally. This work paves the way to apply sub-nanosecond intense MeV neutron sources, such as laser-driven neutron sources under development, to nuclear waste characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep Convolutional Neural Networks Exploit High-Spatial- and -Temporal-Resolution Aerial Imagery to Phenotype Key Traits in Miscanthus

Miscanthus is one of the most promising perennial crops for bioenergy production, with high yield potential and a low environmental footprint. The increasing interest in this crop requires accelerated selection and the development of new screening techniques. New analytical methods that are more accurate and less labor-intensive are needed to better characterize the effects of genetics and the environment on key traits under field conditions. We used persistent multispectral and photogrammetric UAV time-series imagery collected 10 times over the season, together with ground-truth data for thousands of Miscanthus genotypes, to determine the flowering time, culm length, and biomass yield traits. We compared the performance of convolutional neural network (CNN) architectures that used image data from single dates (2D-spatial) versus the integration of multiple dates by 3D-spatiotemporal architectures. The ability of UAV-based remote sensing to rapidly and non-destructively assess large-scale genetic variation in flowering time, height, and biomass production was improved through the use of 3D-spatiotemporal CNN architectures versus 2D-spatial CNN architectures. The performance gains of the best 3D-spatiotemporal analyses compared to the best 2D-spatial architectures manifested in up to 23% improvements in R2, 17% reductions in RMSE, and 20% reductions in MAE. The integration of photogrammetric and spectral features with 3D architectures was crucial to the improved assessment of all traits. In conclusion, our findings demonstrate that the integration of high-spatiotemporal-resolution UAV imagery with 3D-CNNs enables more accurate monitoring of the dynamics of key phenological and yield-related crop traits. This is especially valuable in highly productive, perennial grass crops such as Miscanthus, where in-field phenotyping is especially challenging and traditionally limits the rate of crop improvement through breeding.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

The Use of Long-wave Infrared Cameras for Hazardous Waste Remediation - 20457

For over fifty years, infrared cameras have been used in military applications, nondestructive testing, condition monitoring, and predictive maintenance. As infrared cameras continue to become more sophisticated and less expensive, they are providing value in an ever-increasing variety of unique applications, including hazardous waste remediation. This paper describes several instances of how infrared cameras have been used within the US Department of Defense and the US Department of Energy in support of waste remediation projects, including the author's recent use of an infrared camera in support of the Calcine Retrieval Project at the Idaho National Laboratory. Infrared cameras provide images of infrared radiation, or heat energy, which is otherwise invisible to the unaided eye. Infrared radiation is part of the electromagnetic spectrum, which includes visible light. But unlike visible light, infrared has wavelengths longer than the human eye can detect. Infrared is emitted by everything with a temperature above absolute zero (-273 deg. C, or -459 deg. F); the higher the temperature, the greater the infrared thermal radiation, or heat, that is emitted. Even objects that feel cold to us, like ice, emit thermal radiation and can be imaged by infrared cameras. These cameras are typically used to look for abnormally hot or cold spots on a component or target area under normal operating conditions. The method provides a rapid, wide-area, noncontact technique for identifying problems associated with a temperature differential. All infrared cameras can provide qualitative thermal information by displaying relative differences in temperatures within a two-dimensional image. More expensive infrared cameras can also provide quantitative information where an absolute temperature value is assigned to each pixel associated with the displayed two-dimensional image. Proper camera calibration and a solid understanding of heat transfer and thermography techniques are required when using an infrared camera to obtain quantitative information. Case studies outlined in this paper include the rapid, non-intrusive detection of hazardous decontamination solution within one-ton shipping containers at Pine Bluff Arsenal, the non-intrusive identification of residual elemental sodium within the cooling loops of the Experimental Breeder Reactor II (EBR-II) reactor at the Idaho National Laboratory, process monitoring of heat exchanger melt-and-drain efforts during EBR-II decommissioning, and the remote detection of internal steel supports within calcine storage bins prior to bin penetration. For each use of thermography, the author describes a summary of the waste remediation effort, the infrared camera used, the thermal imaging technique employed, and the results obtained. The paper concludes with a discussion on common mistakes to avoid for similar applications of thermography. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Final Report DE-FE0031785 Mohsen Ahmadian, Ph.D. Demonstration of Proof of Concept of a Multiphysics Approach for Real-Time Remote Monitoring of Dynamic Changes in Pressure and Salinity in Hydraulically Fractured Networks

Hydraulic fracturing has evolved into a multistep process with varying flow rates, carrier fluids (e.g., gel or slickwater), proppant loadings, and proppant grain sizes. As a result, primary recovery from a hydraulically fractured tight-oil reservoir is often a tiny fraction of the original oil in place, ranging between 5 and 10%. As stated in the FOA1990, “part of this problem is due to the inability of current well completion processes to effectively stimulate the entire reservoir volume in contact with the wellbore. Innovative technologies are needed that can help improve the effectiveness of reservoir completion methods, maximize stimulated reservoir volumes, and optimize recovery over the entire producing life span of a well”. We first need to enhance the current fracture diagnostic techniques to improve a well-completion design. However, detecting and delineating a subsurface hydraulic fracture is extremely difficult because the induced fracture network is only fractionally propped, and these propped fractures are generally very thin. Microseismic and tiltmeter monitoring techniques can provide information on the fracture extent but provide little or no information on the movement and final distribution of proppant or production fluids. On the other hand, electromagnetic (EM) imaging has shown the capability to monitor proppant distribution throughout the fracture area, especially in the presence of Electrically Active Proppants (EAPs). A previous EM survey of hydraulic fracturing at the Devine Fracture Pilot Site (DFPS) and subsequent EM code developments demonstrated this survey as a robust technique to remotely interrogate the extent of the EAP-filled hydraulic fracture during its propagation. The objectives of the project were threefold: (1) to capitalize on the material properties of an EAP to demonstrate remote monitoring of relative changes in pressure, pressure, and flow that are commonly encountered during production from a hydraulically fractured reservoir; (2) to evaluate EM imaging tools, to achieve Objective 1 in near real-time; and (3) to develop a multi-physics joint inversion approach to precisely predict flow patterns and physiochemical changes within an EAP-filled fracture network. This research project was built upon our previous work at the Devine Test Site managed by the Bureau of Economic Geology (BEG) at The University of Texas at Austin (UT-Austin). It also leveraged a significant investment from the Advanced Energy Consortium (AEC) to address the DOE's interest in subsurface flow, containment, and characterization by multiple signals. This three-year and three-month project succeeded in demonstrating the feasibility of a real-time dynamic fluid flow mapping technique at Technology Readiness Level 5 (TRL5) by utilizing a commercially available surface-based Controlled-Source Electromagnetic (CSEM) method (Objectives 1, 2). We demonstrated that injections into an EAP-filled fracture could be successfully coupled with real-time electric field measurements on the surface, leading to remote monitoring of dynamic changes within the EAP-filled fracture. Furthermore, the observed electric field in our study is influenced by bottomhole pressure, flow rate, and salinity, which is demonstrated by comparing these parameters with the electrical field potentials. EM simulations solely based on assumptions of fracture conductivity changes during injection did not reproduce the whole measured electric field magnitudes. Preliminary estimates showed that including Streaming Potential (SP) in our geophysical model is likely needed to reduce the simulation misfit.

02 PETROLEUM↗
DOE OSTI2026

Custom surface reflectance, shade mask, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study (2025)

This dataset contains land surface reflectance estimates and additional derived products generated from NEON Imaging Spectrometer (NIS) data collected in the Upper Gunnison river basin during June and July of 2025. Data was collected over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). These products were derived from radiance and LiDAR data collected by the NEON Airborne Observation Platform (AOP) campaign funded by the Colorado Headwaters Ecological Spectroscopy Study (CHESS) (doi:10.15485/3017965). Products include per-pixel surface reflectance (rfl) and reflectance uncertainty (rfl_unc), observational data (obs), canopy equivalent water thickness (ewt), and shade masks. Atmospheric correction was performed per flightline using the ISOFIT (Imaging Spectrometer Optimal FITting) optimal estimation framework to estimate surface reflectance and the associated per-band reflectance uncertainty. Reflectance retrievals achieved a mean absolute error of 1.5% across diverse validation surfaces (see validation report.pdf). Equivalent water thickness was calculated from surface reflectance using the Beer–Lambert absorption of liquid water. Shade masks were generated based on the geometry between the sun angle, ground surface, and sensor at the time of flight. Data products are provided per-flightline and as mosaics for each domain. Flightline data products are provided as ENVI-formatted binary files (rfl, rfl_unc, ewt) and GeoTIFFs (shade). Reflectance and uncertainty mosaics are provided as tiled NetCDFs, while all other mosaicked products are provided as cloud-optimized GeoTIFFs. These formats are supported by common geospatial software (e.g., QGIS, ArcGIS, ENVI) and programmatic libraries in Python (e.g., rasterio, xarray, spectral, netCDF4) and R (e.g., terra, ncdf4). Processing workflows were designed to be equivalent to those used to generate the 2018 CHESS campaign airborne imaging spectroscopy data products (doi:10.15485/3013527). All outputs were co-registered to a common spatial grid to support time series analyses. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: Data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). Computational research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) and was funded by EMIT Extended Mission Phase E Science.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2025

Deploying Adversarial Attacks in Super-Resolution Models

Reliable super-resolution methods are crucial for applications like remote sensing, grid resilience and disaster impact analysis, and standoff biometrics. These methods infuse additional high-frequency information into reconstructions, allowing for better contextualization and image intelligence. However, super-resolution models can also introduce hallucinations or other unseen vulnerabilities that could be exploited by an adversary. This is further compounded by the prominence of deep learning in these models, as models are often blindly applied on out-of-distribution images. In this work, we implement adversarial attacks in common open-source super-resolution models and examine their impact on reconstructions and downstream classification tasks. We find that an adversarially trained super-resolution model can produce high-quality reconstructions that degrade downstream classifications. Moreover, these attacks do not require access to low-resolution imagery or class labels at inference time. These results demonstrate the vulnerability of super-resolution methods to malicious actors and motivates the development of a detector for super-resolution adversarial attacks. Further exploration of adversarial attacks in this domain is required to ensure trustworthiness and robustness of super-resolution models for national security applications.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Smart quantum statistical imaging beyond the Abbe-Rayleigh criterion

The wave nature of light imposes limits on the resolution of optical imaging systems. For over a century, the Abbe-Rayleigh criterion has been utilized to assess the spatial resolution limits of imaging instruments. Recently, there has been interest in using spatial projective measurements to enhance the resolution of imaging systems. Unfortunately, these schemes require a priori information regarding the coherence properties of “unknown” light beams and impose stringent alignment conditions. Here, we introduce a smart quantum camera for superresolving imaging that exploits the self-learning features of artificial intelligence to identify the statistical fluctuations of unknown mixtures of light sources at each pixel. This is achieved through a universal quantum model that enables the design of artificial neural networks for the identification of photon fluctuations. Our protocol overcomes limitations of existing superresolution schemes based on spatial mode projections, and consequently provides alternative methods for microscopy, remote sensing, and astronomy.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Exploiting universal nonlocal dispersion in optically active materials for spectro-polarimetric computational imaging

Recent years have seen significant advancements in exploring novel light-matter interactions such as hyperbolic dispersion within natural crystals. However, current studies have predominantly concentrated on local optical response of materials characterized by a dielectric tensor without spatial dispersion. Here, we investigate the nonlocal response in optically-active crystals with screw symmetries, revealing their lossless, super-dispersive properties compared to traditional optical response functions. We leverage this universal nonlocal dispersion, i.e. the dispersion of optical rotatory power, to explore a novel spectral de-multiplexing scheme compared to conventional gratings, prisms and metasurfaces. We design and demonstrate an ‘Nonlocal-Cam’ - a camera that exploits nonlocal dispersion through sampling of polarized spectral states and the application of computational spectral reconstruction algorithms. The Nonlocal-Cam captures information in both laboratory and outdoor field experiments which is unavailable to traditional intensity cameras - the spectral texture of polarization. Merging the fields of nonlocal electrodynamics and computational imaging, our work paves the way for exploiting nonlocal optics of optically active materials in a variety of applications, from biological microscopy to physics-driven machine vision and remote sensing.

Wang, Xueji [Purdue Univ., West Lafayette, IN (Uni↗
DOE OSTI2025

Multiscale maps of Active Layer Depth for Teller site Mile Marker 27 and Kougarok Mile Marker 80, Seward Peninsula, AK

Remote sensing maps of active layer depth derived from Unmanned Areal System (UAS) data. The UAS datasets were stepwise scaled until matching the AVIRIS-NG (Airborne Visible / Infrared Imaging Spectrometer - Next Generation) and Sentinel-2 spatial resolutions. Using the field observed Active Layer Depth (ALD) measurement in combination with spectral and topographic predictors derivatives from DJI UAS imagery, we used a spatially explicit RF regression model to predict and map ALD across our study landscapes. This package includes maps for Next-Generation Ecosystem Experiment Arctic (NGEE Arctic)’s Teller Mile Marker (MM) 27, and Kougarok MM80 (aka Mile 80) watersheds. The field, map data, and metadata are provided as geoTIF and text (*.csv) formats. These datasets are provided in support of Hantson et al., 2024 (accepted) “Scaling Arctic landscape and permafrost features improves active layer depth modeling”

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Source Physics Experiment: Rock Valley Interferometric Synthetic Aperture RADAR Earthquake Detection Study

Seismic signals from 1993 show a series of magnitude (Mw) 3.7 or less seismic events in Rock Valley on the Nevada National Security Site (NNSS). Historic synthetic aperture radar images of that location were found that could provide interferometric synthetic aperture radar (InSAR) measures of the ground height during 1993. Given this historic SAR imagery, we explore answering the question if ground movement from the 1993 Rock Valley earthquake activity could be sensed by remote sensing means. Finding earthquake surface movement would assist in locating the Rock Valley fault and the 1993 earthquake hypocenter where the Source Physics Experiment Phase III series of experiments will be conducted. In this report, we show that InSAR can sense very small height differences, and for the European Radar Satellite-1 InSAR collections during 1992 and 1993 over Rock Valley earth surface movements were measured with 8 mm uplift and 12.5 mm subsidence over isolated areas. One of these earth movement areas coincides with an InSAR image pair coherence drop between March 5, 1993 and June 18, 1993. The coherence drop is over an approximately 13 square km area south southeast of Skull Mountain centered at 36° 43' 30" N latitude and 116° 05' 00" W longitude. Measured small surface movement and a loss of InSAR coherence may be caused by the series of earthquakes. The location of these InSAR detections may also coincide with water drainage or erosion displacement. There are no records to disambiguate the earthquake and erosion earth surface motion possibilities. Therefore, the InSAR findings of earth surface movement by InSAR are inconclusive.

58 GEOSCIENCES↗
DOE OSTI2025

Tracking seasonal variability in plant traits from spaceborne PRISMA and NEON AOP across forest types and ecoregions

Plant traits serve as critical indicators of how plants adapt to environmental changes and influence ecosystem functions. While airborne hyperspectral remote sensing effectively maps plant traits through detailed reflectance properties, it is limited by cost and scale, making large-scale and temporal studies challenging. The recently launched spaceborne hyperspectral imager, PRecursore IperSpettrale della Missione Applicativa (PRISMA), offers frequent, large scale and high-fidelity observations on a spatial resolution of 30 m and a revisit time of around 29 days, making it suitable for large-scale seasonal trait mapping. However, their potential remains largely unexplored. This study developed a multi-stage framework by leveraging the PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits — chlorophyll content, carotenoid content, equivalent water thickness, and nitrogen content — across eleven NEON sites representing diverse forest types and ecoregions in the contiguous U.S. Our results demonstrated that PRISMA hyperspectral data can reliably track seasonal variability in plant traits, achieving overall R 2 values ranging from 0.78 to 0.88 and normalized root mean square error (NRMSE) values ranging from 5.4% to 8.4% for the four traits. Seasonal patterns revealed bell-shaped trajectories for chlorophyll and carotenoids, while equivalent water thickness decreased steadily across most sites, driven by structural changes during leaf maturation and senescence. Nitrogen content exhibited less pronounced seasonal variation but followed expected nutrient resorption patterns. Analysis of environmental drivers showed that seasonal variability is primarily controlled by solar radiation and day length in northern sites, vapor pressure in semi-arid regions, and temperature in mid-southeastern sites. Spatial variability, meanwhile, was primarily driven by soil properties, particularly during the peak growing season. However, the influence of soil variables slightly declines toward the end of the season at several sites, as climatic factors become more prominent. This study highlights the capability of PRISMA, and potentially other similar spaceborne hyperspectral data for large-scale, time-series plant trait mapping and provides valuable insights into the interactions between plant traits and environmental factors. In conclusion, these findings contribute to advancing our understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.

Environmental drivers↗
DOE OSTI2021

ThermalTracker 3D

ThermalTracker-3D is a stereo-vision solution for evaluating flight tracks of birds and bats around offshore wind turbines. Using a pair of thermal video cameras, the technology remotely senses movement of animals and objects, day and night, near critical assets. It generates motion tracks by collapsing a sequence of video frames from each camera into a single image that contains an entire flight track and then applies stereo-vision processing to transform the flight track into three dimensions. The approach allows tracking in near real time and automatically identifies moving objects based on features from the motion track and object size.

Matzner, Shari↗
DOE OSTI2021

Desert Peak Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to the Desert Peak Geothermal Field. It includes all input and output files used in the project. The files include data categories of raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs including Radar, SWIR, Thermal, Geophysics, Geology, and Wells. The files for the Desert Peak Geothermal Site are used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Desert Peak Geothermal Field.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Salton Sea Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Salton Sea Geothermal Field. It includes all input and output files used with the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. The files are used with the Geothermal Exploration Artificial Intelligence for the Salton Sea Geothermal Site to identify indicators of blind geothermal systems. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Salton Sea Geothermal Site.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Deep Learning for Land Cover Classification Using Only a Few Bands

There is an emerging interest in using hyperspectral data for land cover classification. The motivation behind using hyperspectral data is the notion that increasing the number of narrowband spectral channels would provide richer spectral information and thus help improve the land cover classification performance. Although hyperspectral data with hundreds of channels provide detailed spectral signatures, the curse of dimensionality might lead to degradation in the land cover classification performance. Moreover, in some practical applications, hyperspectral data may not be available due to cost, data storage, or bandwidth issues, and RGB and near infrared (NIR) could be the only image bands available for land cover classification. Light detection and ranging (LiDAR) data is another type of data to assist land cover classification especially if the land covers of interest have different heights. In this paper, we examined the performance of two Convolutional Neural Network (CNN)-based deep learning algorithms for land cover classification using only four bands (RGB+NIR) and five bands (RGB+NIR+LiDAR), where these limited number of image bands were augmented using Extended Multi-attribute Profiles (EMAP). The deep learning algorithms were applied to a well-known dataset used in the 2013 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest. With EMAP augmentation, the two deep learning algorithms were observed to achieve better land cover classification performance using only four bands as compared to that using all 144 hyperspectral bands.

58 GEOSCIENCES↗
DOE OSTI2022

Micro-spectroscopic and freezing characterization of ice-nucleating particles collected in the marine boundary layer in the eastern North Atlantic

Abstract. Formation of atmospheric ice plays a crucial role in the microphysical evolution of mixed-phase and cirrus clouds and thus climate. How aerosol particles impact ice crystal formation by acting as ice-nucleating particles (INPs) is a subject of intense research activities. To improve understanding of atmospheric INPs, we examined daytime and nighttime particles collected during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign conducted in summer 2017. Collected particles, representative of a remote marine environment, were investigated for their propensity to serve as INPs in the immersion freezing (IMF) and deposition ice nucleation (DIN) modes. The particle population was characterized by chemical imaging techniques such as computer-controlled scanning electron microscopy with energy-dispersive X-ray analysis (CCSEM/EDX) and scanning transmission X-ray microscopy with near-edge X-ray absorption fine-structure spectroscopy (STXM/NEXAFS). Four major particle-type classes were identified where internally mixed inorganic–organic particles make up the majority of the analyzed particles. Following ice nucleation experiments, individual INPs were identified and characterized by SEM/EDX. The identified INP types belong to the major particle-type classes consisting of fresh sea salt with organics or processed sea salt containing dust and sulfur with organics. Ice nucleation experiments show IMF events at temperatures as low as 231 K, including the subsaturated regime. DIN events were observed at lower temperatures of 210 to 231 K. IMF and DIN observations were analyzed with regard to activated INP fraction, ice-nucleation active site (INAS) densities, and a water activity-based immersion freezing model (ABIFM) yielding heterogeneous ice nucleation rate coefficients. Observed IMF and DIN events of ice formation and corresponding derived freezing rates demonstrate that the marine boundary layer aerosol particles can serve as INPs under typical mixed-phase and cirrus cloud conditions. The derived IMF and DIN parameterizations allow for implementation in cloud and climate models to evaluate predictive effects of atmospheric ice crystal formation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Development of intelligent control module for the J-TEXT electron cyclotron emission imaging system

An Electron Cyclotron Emission Imaging (ECEI) system has been developed on the J-TEXT tokamak and employed for the multi-dimensional electron temperature fluctuation imaging measurement in 2019 campaign. The intelligent control module has been developed and been used to flexibly setup the 256-channel ECEI system configurations for different plasma scenarios. The adjustments of key parameters are merged in this module including size and position of the geometric observation window, and the dynamic range of the signal and image resolution. The user-friendly interface is also developed for the tokamak operator to set the ECEI system focusing on physics interested region. Furthermore, this module is installed close to the diagnostic system around the J-TEXT tokamak, which can be remotely controlled by authorized operators in the control room. It significantly improves ECEI flexibility and stability, and also keeps the operation technician away from the harsh radiation environment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A Heterogeneous System for Eagle Detection, Deterrent, and Wildlife Collision Detection for Wind Turbines (Final Technical Report)

This report summarizes the design, implementation, and test of an integrated system for automated detection and deterrence of eagles, with included wind turbine blade strike detection and imaging functionality. A machine learning approach was used in conjunction with a 360° camera system for automated detection and classification of golden eagles. This was developed using footage obtained from trained golden eagles and other raptors, in collaboration with wildlife biologists and professional bird handlers. Oregon State University developed a visual deterrent system, which uses inflatable anthropomorphic sculptures with random, kinetic motion to deter eagles, and conducted limited field testing on live eagles; the deterrent can be triggered by the visual detection of eagles using the vision system. Finally, a multi-sensor module was developed that is mounted at the turbine blade root. This module measures vibration and other motions to detect blade strikes, and an integrated on-blade camera captures an image of any impacting objects. Long-term, this blade strike detection system is intended to support an automatic monitoring and certification system for the eagle detection and deterent system. Independent field testing of each system component is described. Testing of the integrated system on an operational wind turbine was conducted across three separate field tests. This includes multi-day fields tests on a General Electric 1.5MW wind turbine at the National Renewable Energy Laboratory (NREL) National Wind Technology Center (NWTC) in Boulder, CO in October 2018 and July 2019; installation procedures, test procedures, and a summary of collected data are presented. A third multi-day on-turbine field test is also presented, which was performed using a General Electric 1.5MW wind turbine at the North American Wind Research and Training Center (NAWRTC) at Mesalands Community College, Tucumcari, NM in April 2019. Across these field tests, the vision system was demonstrated using unmanned aerial vehicles (UAV), and the eagle classification algorithm was not tested; the visual deterrent system was demonstrated, including automatic, remote deployment following surrogate visual detections; and, multi-sensor on-blade data was recorded across multiple wind turbine operational conditions and through more than 100 surrogate blade strikes using soft projectiles, including the successful demonstration of automatic image capture of striking objects. This data set was also used for offline development and validation of enhanced collision detection algorithms. As summarized in this report, the development and field validation of an integrated detection, deterrent, and blade collision detection system represents a critical proof of concept for future technology development of related detection and deterrent technologies, where both deterrent as well as collision detection recording devices are needed for future siting, monitoring, and operation of wind turbine installations, both onshore and offshore.

17 WIND ENERGY↗