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At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Combined Computer Vision and Deep Learning Approach for Rapid Drone-Based Optical Characterization of Parabolic Troughs

Optical accuracy is a primary driver of parabolic trough concentrating solar power (CSP) plant performance, but can be damaged by wind loads, gravity, error during installation, and regular plant operation. Collecting and analyzing optical measurements over an entire operating parabolic trough plant is difficult, given the large scale of typical installations. Distant Observer, a software tool developed at the National Renewable Energy Laboratory, uses images of the absorber tube reflected in the collector mirror to measure both surface slope in the parabolic mirror and offset of the absorber tube from the ideal focal point. This technology has been adapted for fast data collection using low-cost commercial drones, but until recently still required substantial human labor to process large amounts of data. A new method leveraging advanced deep learning and computer vision tools can drastically reduce the time required to process images. This new method addresses the primary analysis bottleneck, identifying featureless, reflective mirror corner points to a high degree of accuracy. Recent work has shown promising results using computer vision methods. The combined deep learning and computer vision approach presented here proved highly effective and has the potential to further automate data collection and analysis, making the tool more robust. The method presented in this paper automatically identified 74.3% of mirror corners within 2 pixels of their manually marked counterparts and 91.9% within 3 pixels. This level of accuracy is sufficient for practical Distant Observer analysis within a target uncertainty. A commercial drone collected video of over 100 parabolic trough modules at an operating CSP plant to demonstrate the deep learning and computer vision method's usefulness in processing large amounts of data. These troughs were successfully analyzed using Distant Observer, paired with the new deep learning and computer vision algorithm, and can provide plant operators and trough designers with valuable insight about plant performance, operating strategies, and plant-wide optical error trends.

computer vision↗
DOE OSTI2019

Growth interactions between icosahedral quasicrystals

We investigate the solidification pathways of a population of icosahedral quasicrystals in a liquid through in situ synchrotron x-ray tomography. The wealth of three-dimensional space- and time-resolved data enables us to test the predictions of various models and theories of crystallization on a quasi-crystal-forming alloy. Remarkably, we find the general evolution equation—that accounts for the competing effects of growth, coalescence, and coarsening—fits well our experimental data on surface area concentration. Furthermore, we quantify the orientation selection, screening length, and coarsening rate of the dodecahedra, and compare these results to that of periodic crystals. The latter is a full order of magnitude smaller than that of elemental metal dendrites in the limit of zero volume fraction, a reflection of the low solid-liquid interfacial energy of the icosahedral phase. Furthermore, our paper provides the critical input data for microstructural models used for integrated computational materials engineering of complex intermetallics, including quasicrystals.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Pd-Ag chronometry of IVA iron meteorites and the crystallization and cooling of a protoplanetary core

To constrain the timescales and processes involved in the crystallization and cooling of protoplanetary cores, we examined the Pd-Ag isotope systematics of the IVA iron meteorites Muonionalusta and Gibeon. A Pd-Ag isochron for Muonionalusta provides an initial 107 Pd/ 108 Pd = (2.57 ± 0.07) × 10 -5 . The three metal samples analyzed from Gibeon plot below the Muonionalusta isochron, but these samples also show significant effects of cosmic ray-induced neutron capture reactions, as is evident from 196 Pt excesses in the Gibeon samples. After correction for neutron capture effects on Ag isotopes, the Gibeon samples plot on the Muonionalusta isochron, indicating that these two IVA irons have indistinguishable initial 107 Pd/ 108 Pd. Collectively, the Pd-Ag data indicate cooling of the IVA core below Pd-Ag closure between 2.9 ± 0.4 Ma and 8.9 ± 0.6 Ma after CAI formation, where this age range reflects uncertainties in the initial 107 Pd/ 108 Pd ratios of the solar system, which in turn result from uncertainties in the Pb-Pb age of Muonionalusta. The Ag isotopic data indicate that the IVA core initially evolved with a modestly elevated Pd/Ag, but the low Ag concentrations measured for some metal samples indicate derivation from a source with much lower Ag contents and, hence, higher Pd/Ag. These contrasting observations can be reconciled if the IVA irons crystallized from an initially more Ag-rich core, followed by extraction of Fe-S melts during compaction of the nearly solidified core. Owing to its strong tendency to partition into Fe-S melts, Ag was removed from the IVA core during compaction, leading to the very low Ag concentration observed in metal samples of IVA irons. Alternatively, Ag was lost by evaporation from a still molten metallic body just prior to the onset of crystallization. The Pd-Ag isotopic data indicate that Muonionalusta cooled at >500 K/Ma through the Pd-Ag closure temperature of ~900 K, consistent with the rapid cooling inferred from metallographic cooling rates for IVA irons. Finally, combined, these observations are consistent with cooling of IVA irons in a metallic body with little or no silicate mantle.

58 GEOSCIENCES↗
DOE OSTI2022

Leaf demography spectra, February 2017, PA-SLZ: Panama

This dataset contains leaf reflectance spectra of sunlit canopy leaves from trees at the San Lorenzo Protected Area (PA-SLZ), Panama. Spectra were measured with a full-spectrum (350 -2500 nm) spectroradiometer with a leaf clip attachment. Leaves previously documented in a demography survey were targeted, and each leaf spectrum is paired with species identification, relative leaf position on each branch and estimated leaf age in days. Leaves were measured from the following species: Apeiba membranacea, Carapa guianensis, Guatteria dumetorum, Miconia borealis, Tachigali versicolor, Terminalia amazonia, Tocoyena pittieri and Vochysia ferruginea Unprocessed spectral data are included as SVC *.sig files, and metadata, including sample details, are presented in *.xlsx files. Leaf reflectance spectra of sunlit canopy leaves from trees at the San Lorenzo Protected Area (PA-SLZ), Panama. Spectra were measured with a full-spectrum (350 -2500 nm) spectroradiometer with a leaf clip attachment. Leaves previously documented in a demography survey were targeted, and each leaf spectrum is paired with species identification, relative leaf position on each branch and estimated leaf age in days. Leaves were measured from the following species: Apeiba membranacea, Carapa guianensis, Guatteria dumetorum, Miconia borealis, Tachigali versicolor, Terminalia amazonia, Tocoyena pittieri and Vochysia ferruginea Unprocessed spectral data are included as SVC *.sig files, and metadata, including sample details, are presented in *.xlsx files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Comparison of Phenology Estimated from Reflectance-Based Indices and Solar-Induced Chlorophyll Fluorescence (SIF) Observations in a Temperate Forest Using GPP-Based Phenology as the Standard

We assessed the performance of reflectance-based vegetation indices and solar-induced chlorophyll fluorescence (SIF) datasets with various spatial and temporal resolutions in monitoring the Gross Primary Production (GPP)-based phenology in a temperate deciduous forest. The reflectance-based indices include the green chromatic coordinate (GCC), field measured and satellite remotely sensed Normalized Difference Vegetation Index (NDVI); and the SIF datasets include ground-based measurement and satellite-based products. We found that, if negative impacts due to coarse spatial and temporal resolutions are effectively reduced, all these data can serve as good indicators of phenological metrics for spring. However, the autumn phenological metrics derived from all reflectance-based datasets are later than the those derived from ground-based GPP estimates (flux sites). This is because the reflectance-based observations estimate phenology by tracking physiological properties including leaf area index (LAI) and leaf chlorophyll content (Chl), which does not reflect instantaneous changes in phenophase transitions, and thus the estimated fall phenological events may be later than GPP-based phenology. In contrast, we found that SIF has a good potential to track seasonal transition of photosynthetic activities in both spring and fall seasons. The advantage of SIF in estimating the GPP-based phenology lies in its inherent link to photosynthesis activities such that SIF can respond quickly to all factors regulating phenological events. Despite uncertainties in phenological metrics estimated from current spaceborne SIF observations due to their coarse spatial and temporal resolutions, dates in middle spring and autumn—the two most important metrics—can still be reasonably estimated from satellite SIF. Our study reveals that SIF provides a better way to monitor GPP-based phenological metrics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Improved Infrared Optical Constants from Pressed Pellets: II. Ellipsometric n and k Values for Ammonium Sulfate with Variability Analysis

Infrared spectral imaging is facilitated via the comparison of spectra recorded in situ to a databank of actual or synthetic infrared reflectance spectra. It has recently been shown that reference spectra corresponding to the many different morphological forms of the same chemical can be generated synthetically using the imaginary, k, and real, n, components of the complex refractive index, n~ = n + ik. One method to obtain the n and k vectors is infrared ellipsometry, which measures the changes in amplitude, tan ?, and phase, ?, of polarized light reflected from the sample both as a function of wavenumber and angle of incidence. The method requires specularly reflected light so best results are usually obtained with polished planar samples of large surface area. Due to the difficulties of obtaining such samples, however, we investigate the possibility of pressing powders of neat materials and obtaining the corresponding optical constants from the pellets. In this paper, variability in the sample pellet and preparation method is investigated, as is variability in the fitting procedure for the derived optical constants. The n/k vectors are derived from the measured ellipsometric parameters, tan ? and ?, as they are fit by an oscillator model which yield n(?) and k(?) vectors as a function of wavenumber, ?. Construction of the oscillator model is not automatic and depends on significant input from the analyst as well as the sample’s physical characteristics. For the pellet pressing, the experimental variability was found to be minimized for size-selected powdered samples as gauged by the minimal variance in ? and ? for three different pellets; similarly, the analytical precision for multiple measurements of the same pellet was also quite good, suggesting that a pressed pellet is a viable sample preparation method. The greatest variability came in the analytic fitting procedure with differences up to 20% arising from four different fits to the same data set. The final ellipsometric n/k data are compared to literature values obtained from crystalline (NH4)2SO4 samples as well as single-angle reflectance measurements that also used pressed pellets. Comparison with the previous literature values shows generally good agreement although higher k-values are observed for the independent sets of data derived from pressed pellets. These data are suggested as an improved set of optical constants for (NH4)2SO4.

optical constants, ellipsometry, ammonium sulfate,↗
DOE OSTI2023

Tunable plasmonic color device and method of making the same

A plasmonic system is disclosed. The system includes at least one polarizer that is configured to provide at least one linearly polarized broadband light beam, an anisotropic plasmonic metasurface (APM) assembly having a plurality of nanoantennae each having a predetermined orientation with respect to a global axis representing encoded digital data, the APM assembly configured to receive the at least one linearly polarized broadband light beam and by applying localized surface plasmon resonance reflect light with selectable wavelengths associated with the predetermined orientations of the nanoantennae, and at least one analyzer that is configured to receive the reflected light with selectable wavelength, wherein the relative angles between each of the at least one analyzers and each of the at least one polarizers are selectable with respect to the global axis, thereby allowing decoding of the digital data.

Kildishev, Alexander V.↗
DOE OSTI2022

Data Validation for Hosting Capacity Analyses [Slides]

The National Renewable Energy Laboratory (NREL), in partnership with the Interstate Renewable Energy Council (IREC), has recently released a report which identifies a suite of best practices for producing trusted, validated hosting capacity analysis results reflecting real-world grid conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Developing a Reduced 240-Bus WECC Dynamic Model for Frequency Response Study of High Renewable Integration

The ongoing changes in the generation resource mix, driven by the rapid adoption of inverter-based resources (IBR) as well as the early retirement of synchronous generators, are bringing new challenges to the planning and operation of bulk electric power systems. Consequently, there is an increasing need to understand, design, and quantify the reliability service provision from IBRs by performing integrated scheduling and dynamic simulations. However, test systems that have consistent scheduling and dynamic models rarely exist largely because of the decoupled nature of the two simulations on a synchronous generator-dominated system. This paper develops the dynamic model of a reduced Western Electricity Coordinating Council (WECC) system. In conjunction with the existing scheduling model, it is suitable for integrating scheduling and dynamic simulations. The reduced 240-bus WECC model reflects the generation resource mix of the Western Interconnection as of 2018. Moreover, the developed dynamic model is validated against field frequency events measured by FNET/GridEye and preserves the dominant inter-area oscillation mode in WECC.

dynamic simulation↗
DOE OSTI2016

Titanium and Aluminum Rod-Replacement Experiments in Fully-Reflected 6.90% Enriched UO{sub 2} Fuel Rod Lattices

The Seven Percent Critical Experiment (7uPCX) was designed to provide benchmark criticality and reactor physics data for water-moderated pin-fueled nuclear reactor cores. The enrichment of the fuel was chosen to explore the enrichment range above the current 5% ceiling for US commercial pressurized water reactors. The experiment was part of the US Department of Energy (DOE) Nuclear Energy Research Initiative Project 01-124 titled 'Reactor Physics and Criticality Benchmark Evaluations for Advanced Nuclear Fuel'. Three sets of benchmark experiments have been completed using the 7uPCX and documented as LEUCOMP-THERM-080, LEU-COMP-THERM-078, and LEUCOMP-THERM-096. The experiments for LEUCOMP-THERM-080 and LEU-COMP-THERM-078 were done in fully-reflected arrays with the number of fuel rods in the array as the approach parameter. The experiments for LEU-COMP-THERM-096 are similar to those in LEUCOMP-THERM-080 except that the arrays are partially-reflected - the arrays were larger than would be possible with full reflection and the approach-to-critical experiments were done with the depth of the water in the critical assembly as the approach parameter. The experiments described here are similar to the fully-reflected experiments in LEU-COMP-THERM-080 with some of the central fuel rods in the array replaced by titanium rods, aluminum rods, and/or water. The experiments are designed to provide criticality safety benchmarks with significant reactivity from titanium and equivalent configurations with aluminum experiment rods. These experiments are documented as LEU-COMP-THERM-097. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

Variogram Identification Aided by a Structural Framework for Improved Geometric Modeling of Faulted Reservoirs: Jeffara Basin, Southeastern Tunisia

This article describes a proposed work-sequence to generate accurate reservoir-architecture models, describing the geometry of bounding surfaces (i.e., fault locations and extents), of a structurally complex geologic setting in the Jeffara Basin (South East Tunisia) by means of geostatistical modeling. This uses the variogram as the main tool to measure the spatial variability of the studied geologic medium before making any estimation or simulation. However, it is not always easy to fit complex experimental variograms to theoretical models. Thus, our primary purpose was to establish a relationship between the geology and the components of the variograms to fit a mathematically consistent and geologically interpretable variogram model for improved predictions of surface geometries. We used a three-step approach based on available well data and seismic information. First, we determined the structural framework: a seismo-tectonic data analysis was carried out, and we showed that the study area is cut mainly by NW-SE-trending normal faults, which were classified according to geometric criteria (strike, throw magnitude, dip, and dip direction). We showed that these normal faults are at the origin of a large-scale trend structure (surfaces tilted toward the north-east). At a smaller scale, the normal faults create a distinct compartmentalization of the reservoirs. Then, a model of the reservoir system architecture was built by geostatistical methods. An efficient methodology was developed, to estimate the bounding faulted surfaces of the reservoir units. Emphasis was placed on (i) elaborating a methodology for variogram interpretation and modeling, whereby the importance of each variogram component is assessed in terms of probably geologic factor controlling the behavior of each structure; (ii) integrating the relevant fault characteristics, which were deduced from the previous fault classification analysis, as constraints in the kriging estimation of bounding surfaces to best reflect the geologic structure of the study area. Finally, the estimated bounding surfaces together with seismic data and variogram interpretations were used to obtain further insights into the tectonic evolution of the study area that has induced the current reservoirs configuration.

58 GEOSCIENCES↗
DOE OSTI2026

An interactive machine learning platform for analyzing multi-particle coincidence data from cold target recoil ion momentum spectroscopy

We present SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training), a comprehensive software platform for analyzing tabulated high-dimensional multi-particle coincidence data from Cold Target Recoil Ion Momentum Spectroscopy (COLTRIMS) experiments. The software addresses critical challenges in modern momentum spectroscopy by integrating advanced machine learning techniques with physics-informed analysis in an interactive web-based environment. SCULPT implements uniform manifold approximation and projection for non-linear dimensionality reduction to reveal correlations in high-dimensional data. We also discuss potential extensions to deep autoencoders for feature learning and genetic programming for automated discovery of physically meaningful observables. A novel adaptive confidence scoring system provides quantitative reliability assessments by evaluating user-selected clustering quality metrics with predefined weights that reflect each metric’s robustness. The platform features configurable molecular profiles for different experimental systems, interactive visualization with selection tools, and comprehensive data filtering capabilities. Utilizing a subset of SCULPT’s capabilities, we analyze photo-double-ionization data measured using the COLTRIMS method for three-body dissociation of the D 2 O molecule, revealing distinct fragmentation channels and their correlations with physics parameters. The software’s modular architecture and web-based implementation make it accessible to the broader atomic and molecular physics community, significantly reducing the time required for complex multi-dimensional analyses. This opens the door to finding and isolating rare events exhibiting non-linear correlations on the fly during experimental measurements, which can help steer exploration and improve the efficiency of experiments.

Artificial neural networks↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series: Preprint

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗
DOE OSTI2017

Novel Analysis and Visualization of Chemical Events for Public Health Surveillance

Pacific Northwest National Laboratory hosted an intern-based web application development contest in the summer of 2016 centered around developing novel chemical surveillance applications to aid in health situational awareness. Making up the three teams were three graduate students (n=9) from various US schools majoring in non-public health domains, such as computer science and user design. The interns successfully developed three applications that demonstrated a value-add to chemical surveillance—ChemAnalyzer (text analytics), RetroSpect (retrospective analysis of chemical events),and ToxicBusters (geo-based trend analytics). These applications will be the basis for the first chemical surveillance application to be incorporated into the DTRA Biosurveillance Ecosystem (BSVE).Pacific Northwest National Laboratory (PNNL), on behalf the Defense Threat Reduction Agency (DTRA; project number CB10190), hosts an annual intern- based web app development contest. Previous competitions have focused on mobile biosurveillance. The 2016 competition pivoted away from biosurveillance to focus on addressing challenges within the field of chemical surveillance and increasing public health chemical situational awareness. The result of the app will be integrated within the DTRA BSVE. PNNL hosted nine graduate interns for a 10-week period in the summer of 2016 as participants in a summer web application development contest. Students were drawn from such fields as software engineering and user experience and design and placed into three teams of three students. The challenge presented to the interns was to design and develop a fully-functional web application that would address a critical need within the chemical surveillance community. The interns developed their own ideas (vetted by PNNL and DTRA), discovered and integrated their own data sources, and produced their own visualizations and analytics, independent of any assistance outside of that provided in an advisory capacity. The competition end with a judging event with a panel of subject matter experts and cash awards were distributed to the teams. Each team produced a unique application. Although there was mild overlap between some of the ideas, the applications were developed independently and each reflected the unique contributions of the teams. ChemAnalyzer is a text-analytics platform designed to facilitate more data-driven decision, given a corpus of text data about a chemical event. Their platform provided the ability to automatically identify and highlight key words in documents related to chemical events. The keywords are drawn from an ontology installed with the system, as well as any user-identified keywords. The ChemAnalyzer team finished in third place. The RetroSpect team developed a visual analytic tool for performing retrospective analysis and monitoring of chemical events. Their app provided the ability to search and analyze past events, as well as visualization of state and county information for the recorded chemical events. The RetroSpect team finished in second place. The Toxic busters team - the winners of the competition - created a geo-based situational awareness tool for tracking chemical events. Their app featured an updateable map overlay, search functionality for finding specific or related events, incident and city/state/national-level statistics and trends, as well as news and social media integration based on keywords related to chemical surveillance. Each of the apps developed by the teams provides value to an analyst tasked with monitoring chemical events. The apps integrated unique data sources to provides a full picture of a chemical event, and its effects upon the surrounding population. This integrated analytics provides a valuable benefit over existing workflows, where analysts must monitor news, social, and other information sources manually for real-time information. The apps developed by these interns are designed to enable identification and analysis of the incident as quickly as possible, allowing for more timely assessments of the incident and its impacts. The web app development contest provided a unique opportunity for students to learn about the emerging needs in chemical surveillance as it relates to health situational awareness. Students were drawn from a variety of fields and were tasked with developing novel web apps addressing some of the most pressing challenges in the field of chemical surveillance. The ideas generated by the students will help form the basis for future chemical surveillance application development to be integrated with the DTRABSVE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Understanding Biases in Sample Preparation Techniques for Coupled Scanning Electron Microscopy and MAMA PuO 2 Morphological Analysis

In this project, the scanning electron microscopy (SEM) sampling method used during the statistical design study (SDS) was investigated to determine if any sampling biases were present in the analyzed data. Using standard particle size distribution powders from the National Institute of Standards and Technology (NIST 1984 standard reference material) with the origin wet dispersion method, it was determined that a bias to smaller particles was present. This was supported by theoretical calculations using Stokes’ law to determine the settling rate of spherical particles of roughly the same size and mass as those found in the SDS. Based on the theoretical calculations, it was determined that the settling rate for each of the 76 powder sets in the SDS could be unique based on specific particle shape and mass distributions, making a universal correction factor/formula not applicable. Therefore, priority shifted to developing an improved wet dispersion method that significantly reduced the particle settling rate for all particle size and shapes. This was achieved by replacing the original solvent (isopropyl alcohol) with a heavy liquid (lithium heteropolytungstates), which dramatically slowed the settling rate and allowed for the capture of a suitable homogeneous aliquot. SEM imaging and Morphological Analysis for Material Attribution (MAMA) software analysis were conducted on the NIST standard, and the SEM/MAMA data were compared to data captured by a dynamic image analysis particle size analyzer. The resulting data confirmed that the new wet dispersion method does indeed deliver an improved representative aliquot to the SEM stub. For instance, in the NIST certificate, the average particle size is ~17.1 µm ± 2.2 µm with a normal distribution. The initial wet dispersion method resulted in a drastically reduced average particle size of 6.1 µm in addition to a non-representative heavy bi-modal distribution whereas the improved LST wet dispersion method resulting in an average particle size that was much closer to the NIST certificate (12.7 µm) with a similar normal distribution. Although the improved method was still short of the NIST certificate average, atomic force microscopy analysis determined that the resulting ~20-25% reduction in size was due to particles sinking into the carbon sticky tape used for SEM imaging. It is believed that that this bias can be calibrated in a much more predicable manner than the original settling rate bias. In addition, the matching normal distribution curves between the NIST certificate and the heavy liquid method indicate a much-improved representative aliquot has been sampled and imaged. A surrogate CeO 2 powder was used to reflect PuO 2 more accurately and to aid in implementing radiological controls and shielding. The resulting data sets from the SEM/MAMA method and the particle size analyzer give almost identical average particle sizes and particle distribution statistics. Future work will re-analyze several select runs from the SDS to determine if morphological signatures can be found with the improved sampling method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

The Formation of Magnetic Depletions and Flux Annihilation Due to Reconnection in the Heliosheath

The misalignment of the solar rotation axis and the magnetic axis of the Sun produces a periodic reversal of the Parker spiral magnetic field and the sectored solar wind. The compression of the sectors is expected to lead to reconnection in the heliosheath (HS). We present particle-in-cell simulations of the sectored HS that reflect the plasma environment along the Voyager 1 and 2 trajectories, specifically including unequal positive and negative azimuthal magnetic flux as seen in the Voyager data. Reconnection proceeds on individual current sheets until islands on adjacent current layers merge. At late time, bands of the dominant flux survive, separated by bands of deep magnetic field depletion. The ambient plasma pressure supports the strong magnetic pressure variation so that pressure is anticorrelated with magnetic field strength. There is little variation in the magnetic field direction across the boundaries of the magnetic depressions. At irregular intervals within the magnetic depressions are long-lived pairs of magnetic islands where the magnetic field direction reverses so that spacecraft data would reveal sharp magnetic field depressions with only occasional crossings with jumps in magnetic field direction. This is typical of the magnetic field data from the Voyager spacecraft. Voyager 2 data reveal that fluctuations in the density and magnetic field strength are anticorrelated in the sector zone, as expected from reconnection, but not in unipolar regions. The consequence of the annihilation of subdominant flux is a sharp reduction in the number of sectors and a loss in magnetic flux, as documented from the Voyager 1 magnetic field and flow data.

79 ASTRONOMY AND ASTROPHYSICS↗