Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “spatial”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Predicting partner fitness based on spatial structuring in a light-driven microbial community

Microbial communities have vital roles in systems essential to human health and agriculture, such as gut and soil microbiomes, and there is growing interest in engineering designer consortia for applications in biotechnology ( e . g ., personalized probiotics, bioproduction of high-value products, biosensing). The capacity to monitor and model metabolite exchange in dynamic microbial consortia can provide foundational information important to understand the community level behaviors that emerge, a requirement for building novel consortia. Where experimental approaches for monitoring metabolic exchange are technologically challenging, computational tools can enable greater access to the fate of both chemicals and microbes within a consortium. In this study, we developed an in-silico model of a synthetic microbial consortia of sucrose-secreting Synechococcus elongatus PCC 7942 and Escherichia coli W. Our model was built on the NUFEB framework for Individual-based Modeling (IbM) and optimized for biological accuracy using experimental data. We showed that the relative level of sucrose secretion regulates not only the steady-state support for heterotrophic biomass, but also the temporal dynamics of consortia growth. In order to determine the importance of spatial organization within the consortium, we fit a regression model to spatial data and used it to accurately predict colony fitness. We found that some of the critical parameters for fitness prediction were inter-colony distance, initial biomass, induction level, and distance from the center of the simulation volume. We anticipate that the synergy between experimental and computational approaches will improve our ability to design consortia with novel function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Spatial soft sweeps: Patterns of adaptation in populations with long-range dispersal

Adaptation in extended populations often occurs through multiple independent mutations responding in parallel to a common selection pressure. As the mutations spread concurrently through the population, they leave behind characteristic patterns of polymorphism near selected loci—so-called soft sweeps—which remain visible after adaptation is complete. These patterns are well-understood in two limits of the spreading dynamics of beneficial mutations: the panmictic case with complete absence of spatial structure, and spreading via short-ranged or diffusive dispersal events, which tessellates space into distinct compact regions each descended from a unique mutation. However, spreading behaviour in most natural populations is not exclusively panmictic or diffusive, but incorporates both short-range and long-range dispersal events. Here, we characterize the spatial patterns of soft sweeps driven by dispersal events whose jump distances are broadly distributed, using lattice-based simulations and scaling arguments. We find that mutant clones adopt a distinctive structure consisting of compact cores surrounded by fragmented “haloes” which mingle with haloes from other clones. As long-range dispersal becomes more prominent, the progression from diffusive to panmictic behaviour is marked by two transitions separating regimes with differing relative sizes of halo to core. We analyze the implications of the core-halo structure for the statistics of soft sweep detection in small genomic samples from the population, and find opposing effects of long-range dispersal on the expected diversity in global samples compared to local samples from geographic subregions of the range. We also discuss consequences of the standing genetic variation induced by the soft sweep on future adaptation and mixing.

Genetics & Heredity↗
DOE OSTI2020

Pericentromeric heterochromatin is hierarchically organized and spatially contacts H3K9me2 islands in euchromatin

Membraneless pericentromeric heterochromatin (PCH) domains play vital roles in chromosome dynamics and genome stability. However, our current understanding of 3D genome organization does not include PCH domains because of technical challenges associated with repetitive sequences enriched in PCH genomic regions. We investigated the 3D architecture of Drosophila melanogaster PCH domains and their spatial associations with the euchromatic genome by developing a novel analysis method that incorporates genome-wide Hi-C reads originating from PCH DNA. Combined with cytogenetic analysis, we reveal a hierarchical organization of the PCH domains into distinct “territories.” Strikingly, H3K9me2-enriched regions embedded in the euchromatic genome show prevalent 3D interactions with the PCH domain. These spatial contacts require H3K9me2 enrichment, are likely mediated by liquid-liquid phase separation, and may influence organismal fitness. Our findings have important implications for how PCH architecture influences the function and evolution of both repetitive heterochromatin and the gene-rich euchromatin.

Genetics & Heredity↗
DOE OSTI2016

The Composition and Spatial Patterns of Bacterial Virulence Factors and Antibiotic Resistance Genes in 19 Wastewater Treatment Plants

Bacterial pathogenicity and antibiotic resistance are of concern for environmental safety and public health. Accumulating evidence suggests that wastewater treatment plants (WWTPs) are as an important sink and source of pathogens and antibiotic resistance genes (ARGs). Virulence genes (encoding virulence factors) are good indicators for bacterial pathogenic potentials. To achieve a comprehensive understanding of bacterial pathogenic potentials and antibiotic resistance in WWTPs, bacterial virulence genes and ARGs in 19 WWTPs covering a majority of latitudinal zones of China were surveyed by using GeoChip 4.2. A total of 1610 genes covering 13 virulence factors and 1903 genes belonging to 11 ARG families were detected respectively. The bacterial virulence genes exhibited significant spatial distribution patterns of a latitudinal biodiversity gradient and a distance-decay relationship across China. Moreover, virulence genes tended to coexist with ARGs as shown by their strongly positive associations. In addition, key environmental factors shaping the overall virulence gene structure were identified. This study profiles the occurrence, composition and distribution of virulence genes and ARGs in current WWTPs in China, and uncovers spatial patterns and important environmental variables shaping their structure, which may provide the basis for further studies of bacterial virulence factors and antibiotic resistance in WWTPs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Pore-scale hydrodynamics influence the spatial evolution of bacterial biofilms in a microfluidic porous network

Bacteria occupy heterogeneous environments, attaching and growing within pores in materials, living hosts, and matrices like soil. Systems that permit high-resolution visualization of dynamic bacterial processes within the physical confines of a realistic and tractable porous media environment are rare. In this paper, we use microfluidics to replicate the grain shape and packing density of natural sands in a 2D platform to study the flow-induced spatial evolution of bacterial biofilms underground. We discover that initial bacterial dispersal and grain attachment is influenced by bacterial transport across pore space velocity gradients, a phenomenon otherwise known as rheotaxis. We find that gravity-driven flow conditions activate different bacterial cell-clustering phenotypes depending on the strain’s ability to product extracellular polymeric substances (EPS). A wildtype, biofilm-producing bacteria formed compact, multicellular patches while an EPS-defective mutant displayed a linked-cell phenotype in the presence of flow. Finally, these phenotypes subsequently influenced the overall spatial distribution of cells across the porous media network as colonies grew and altered the fluid dynamics of their microenvironment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models

Monitoring the ecological impacts of environmental pollution and the effectiveness of remediation efforts requires identifying relationships between contaminants and the disruption of biological processes in populations, communities, or ecosystems. Wildlife are useful bioindicators, but traditional comparative experimental approaches rely on a staunch and typically unverifiable assumption that, in the absence of contaminants, reference and contaminated sites would support the same densities of bioindicators, thereby inferring direct causation from indirect data. We demonstrate the utility of spatial capture-recapture (SCR) models for overcoming these issues, testing if community density of common small mammal bioindicators was directly influenced by soil chemical concentrations. By modeling density as an inhomogeneous Poisson point process, we found evidence for an inverse spatial relationship between Peromyscus density and soil mercury concentrations, but not other chemicals, such as polychlorinated biphenyls, at a site formerly occupied by a nuclear reactor. Although the coefficient point estimate supported Peromyscus density being lower where mercury concentrations were higher (β = –0.44), the 95% confidence interval overlapped zero, suggesting no effect was also compatible with our data. Estimated density from the most parsimonious model (2.88 mice/ha; 95% CI = 1.63–5.08), which did not support a density-chemical relationship, was within the range of reported densities for Peromyscus that did not inhabit contaminated sites elsewhere. Environmental pollution remains a global threat to biodiversity and ecosystem and human health, and our study provides an illustrative example of the utility of SCR models for investigating the effects that chemicals may have on wildlife bioindicator populations and communities.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Raccoon spatial ecology in the rural southeastern United States

The movement ecology of raccoons varies widely across habitats with important implications for the management of zoonotic diseases such as rabies. However, the spatial ecology of raccoons remains poorly understood in many regions of the United States, particularly in the southeast. To better understand the spatial ecology of raccoons in the southeastern US, we investigated the role of sex, season, and habitat on monthly raccoon home range and core area sizes in three common rural habitats (bottomland hardwood, upland pine, and riparian forest) in South Carolina, USA. From 2018–2022, we obtained 264 monthly home ranges from 46 raccoons. Mean monthly 95% utilization distribution (UD) sizes ranged from 1.05 ± 0.48 km 2 (breeding bottomland females) to 5.69 ± 3.37 km 2 (fall riparian males) and mean monthly 60% UD sizes ranged from 0.25 ± 0.15 km 2 (breeding bottomland females) to 1.59 ± 1.02 km 2 (summer riparian males). Males maintained home range and core areas ~2–5 times larger than females in upland pine and riparian habitat throughout the year, whereas those of bottomland males were only larger than females during the breeding season. Home ranges and core areas of females did not vary across habitats, whereas male raccoons had home ranges and core areas ~2–3 times larger in upland pine and riparian compared to bottomland hardwood throughout much of the year. The home ranges of males in upland pine and riparian are among the largest recorded for raccoons in the United States. Such large and variable home ranges likely contribute to elevated risk of zoonotic disease spread by males in these habitats. These results can be used to inform disease mitigation strategies in the southeastern United States.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Detecting outbreaks using a spatial latent field

In this paper, we present a method for estimating the infection-rate of a disease as a spatial-temporal field. Our data comprises time-series case-counts of symptomatic patients in various areal units of a region. We extend an epidemiological model, originally designed for a single areal unit, to accommodate multiple units. The field estimation is framed within a Bayesian context, utilizing a parameterized Gaussian random field as a spatial prior. We apply an adaptive Markov chain Monte Carlo method to sample the posterior distribution of the model parameters condition on COVID-19 case-count data from three adjacent counties in New Mexico, USA. Our results suggest that the correlation between epidemiological dynamics in neighboring regions helps regularize estimations in areas with high variance (i.e., poor quality) data. Using the calibrated epidemic model, we forecast the infection-rate over each areal unit and develop a simple anomaly detector to signal new epidemic waves. Our findings show that anomaly detector based on estimated infection-rates outperforms a conventional algorithm that relies solely on case-counts.

Safta, Cosmin [Sandia National Laboratories (SNL-C↗
DOE OSTI2022

Temporal and spatial tuning of optical constants in praseodymium doped ceria by electrochemical means

Abstract Temporal and spatial tuning of the refractive index of optical thin films is desired for flat optics applications. The redistribution of mobile ions in mixed ionic-electronic conductors (MIEC) has been demonstrated to serve as a viable means for achieving optical tuning down to the nanoscale. Here we studied the dynamic range of the optical tuning achievable in the refractive index, in the MIEC oxide – Pr x Ce 1− x O 2− δ (PCO), for x = 0.1, 0.2 and 0.4, at 500 °C, by in-situ spectrophotometry. Significant increases in the modulation of both the imaginary and real optical constants in the visible and the adjacent spectra were obtained for increased doping levels. Device employing an electrochemical titration method was implemented to modulate the oxygen concentration, and thereby the optical transmission of PCO. Incorporation of a patterned top electrode allowed for the demonstration of spatial control of PCO thin film properties by in-situ video imaging of the optical switching process. The electrochemically induced optical state is shown to remain non-volatile upon quenching the device to room temperature under applied bias.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Arctic sea ice albedo: Spectral composition, spatial heterogeneity, and temporal evolution observed during the MOSAiC drift

The magnitude, spectral composition, and variability of the Arctic sea ice surface albedo are key to understanding and numerically simulating Earth’s shortwave energy budget. Spectral and broadband albedos of Arctic sea ice were spatially and temporally sampled by on-ice observers along individual survey lines throughout the sunlit season (April–September, 2020) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The seasonal evolution of albedo for the MOSAiC year was constructed from spatially averaged broadband albedo values for each line. Specific locations were identified as representative of individual ice surface types, including accumulated dry snow, melting snow, bare and melting ice, melting and refreezing ponded ice, and sediment-laden ice. The area-averaged seasonal progression of total albedo recorded during MOSAiC showed remarkable similarity to that recorded 22 years prior on multiyear sea ice during the Surface Heat Budget of the Arctic Ocean (SHEBA) expedition. In accord with these and other previous field efforts, the spectral albedo of relatively thick, snow-free, melting sea ice shows invariance across location, decade, and ice type. In particular, the albedo of snow-free, melting seasonal ice was indistinguishable from that of snow-free, melting second-year ice, suggesting that the highly scattering surface layer that forms on sea ice during the summer is robust and stabilizing. In contrast, the albedo of ponded ice was observed to be highly variable at visible wavelengths. Notable temporal changes in albedo were documented during melt and freeze onset, formation and deepening of melt ponds, and during melt evolution of sediment-laden ice. While model simulations show considerable agreement with the observed seasonal albedo progression, disparities suggest the need to improve how the albedo of both ponded ice and thin, melting ice are simulated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Data for Post-Access Report: Tidal Energy Resource Characterization and Model Validation via the Assessment of Cost-effective Spatially Distributed Current Measurements from microFloat Swarms

This project evaluated how high-resolution, spatially distributed field data can be used to refine and validate site-scale hydrodynamic simulations of tidal channels. Use of such spatially-distributed field observations or site-scale hydrodynamic simulations will be needed for producing accurate predictions of tidal energy production over larger arrays of tidal turbines. The data contained in this archive includes field observations of water velocity from a series of short surveys in Agate Pass taken on August 20th, 2020 using station-keeping ADCP (acoustic doppler current profiler) measurements, drifting downward-looking ADCPs, and microFloats, gathered by researchers at APL-UW under a previous project. It also includes high-resolution hydrodynamic model runs for the site during that time generated under TEAMER support. An explanatory file (.pdf format) describes the survey and field data structure. Processed data is stored in .mat format. Survey summary: start time, number of floats, water level. microFloat data: - Cleaned data for each float in survey, - Time series of position (lat., lon.), depth (relative to surface), and horizontal velocity (u, v) Drifting ADCP data: - Cleaned data for each drifting ADCP in survey - Time series of position (lat., lon.), sample depth (relative to surface), and observed water velocity (u,v,w) Stationary ADCP data: - Cleaned data for each station-keeping data set in survey - Time series of position (lat., lon.), sample depth (relative to surface), and observed water velocity (u,v,w) FVCOM (Finite Volume Coastal Ocean Model) model outputs: Water level and velocity, as well as derived variables (e.g., power density) from the final refined model simulations for the Agate Pass subdomain. Contained in NetCDF and ASCII files for the duration of August 18th and 20th 2020 field surveys. Additional scripts for processing the data and generating the figures in the report are included.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Stochastic simulations of temporally and spatially variable Fe cycling within a floodplain aquifer

This data package contains model input and output files for local and global sensitivity analysis (SA) of a reactive transport model simulating redox cycling within a floodplain aquifer. The reaction network is implemented in CrunchFlow and focuses on spatially and temporally heterogeneous Fe cycling, though it contains several other species and redox pathways (37 aqueous species and 7 minerals in total). The package contains data from 600 Monte Carlo simulations (for global SA) and 104 one-at-a-time simulations (for local SA). It includes all input files required to run the CrunchFlow model, as well as limited model output – .csv files containing time series of primary species concentrations at 21 grid cells within the domain. These time series files contain the following species: H+, CO32-, SO42-, Cl-, Ca2+, Mg2+, Na+, K+, Fe(II), Fe(III), HS-, CH3COO-, O2(aq), S(aq), and Br-.These data were used to demonstrate the application of a novel form of global SA, distance-based generalized sensitivity analysis (DGSA) to a reactive transport model. The 2D model simulates the export of reduced species from pockets of fine-grained, organic-rich sediments embedded in a coarse sand aquifer. Stochastic simulations jointly varied 17 key model input parameters across several orders of magnitude, including the spatial variability of each parameter. DGSA on the model results reveal that the amplitude and variability of exported Fe(II) are most sensitive to the interaction between sand permeability and individual reaction rates. By contrast, the propagation of reducing conditions downgradient from the fine-grained lenses depends most heavily on the rate of dissolved organic carbon and sulfate release from the fines.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Model Inputs, Outputs, and Scripts associated with: “Spatial microbial respiration variations in the hyporheic zones within the Columbia River Basin”

This data package is associated with the publication “Spatial microbial respiration variations in the hyporheic zones within the Columbia River Basin” published in the Journal of Geophysical Research: Biogeosciences (Son et al. 2022) available at doi: 10.1029/2021JG006654. This data package includes the key model inputs/outputs of the river corridor model for the Columbia River Basin (CRB) and the model source codes, which were used in the manuscript. The model is a carbon-nitrogen-coupled river corridor model (RCM), and the model is used to quantify hyporheic zone (HZ) aerobic and anaerobic respiration at the NHDPLUS stream reach scales. The RCM used in this study combines empirical substrate models derived from observations and three microbially driven reactions to compute respiration of the HZ for each National Hydrography Dataset (NHD) reach within the CRB. The reactions in HZs of each NHD reach include anaerobic respiration and two-step anaerobic respiration via denitrification. Our HZ respiration estimates are limited to the lotic (or flowing) stream/river systems, and do not account for the respiration process in water column. Note that the RCM only simulates the HZ’s contribution to the dissolved carbon dioxide (CO2) concentrations in the streams, and the CO2 emissions to the atmosphere are not modelled. The model computes at hourly timesteps because of the fast reaction rates. The key input data of the model are exchange flux, residence time, and stream solute (dissolved organic carbon (DOC), dissolved oxygen (DO), and nitrate concentrations). These inputs are constant over time and represent long-term averaged values.This modeling framework successfully quantified HZ respiration components over multiple scales. It revealed key mechanisms driving the spatial variation of HZ aerobic and anaerobic respiration in reaches with varying hydrologic and substrate conditions. Thus, this modeling study offers a testing hypothesis in different river system (e.g., climate and biomes) for the HZ respiration processes, and can be used as a sampling design tool for large-scale HZ experimental studies.This dataset contains five folders: (1) model_inputs, (2) model_outputs, (3) Rscripts, (4) figures, and (5) model_codes. It also contains a readme, file level metadata (FLMD), and data dictionary (dd). Please see the FLMD for a list of all the files contained in this data package and descriptions for each. The model_inputs folder contains the model inputs used to drive the model simulations. The model_outputs folder contains key model output files from the river corridor model. The Rscripts folder contains the Rscripts for pre- and post- processing model results. The figures folder contains the raw figures associated with the manuscript. The model_codes folder includes key model source codes/input files. All files are .jpg, .jpeg, .out, .e, .od, .dat, .sub, .F90, .0, .R, .sbx, .cpg, .sbn, .shx, .shp, .dbf, .prj, .tfw, .tif, .xml, .pdf, or .csv.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Modeling spatial patterns in soil arsenic to estimate natural baseline concentrations

ABSTRACT Arsenic in soil is an important public health concern. Toxicity guidelines and models based on laboratory studies (i.e., U.S. EPA’s Integrated Risk Information System) should consider natural soil As concentrations to avoid unnecessary remediation burdens on society. We used soil and stream sediment samples from the USGS National Geochemical Survey database to assess the spatial distribution of natural As in a 1.16E+5 km2 area. Samples were collected at 348 soil and 144 stream locations, providing approximately one sample for every 290 km2. Sample sites were selected to minimize the potential influence of anthropogenic inputs. Samples were processed using acid digestion of whole samples (concentrated HCl and ascorbic acid) and concentrations were measured using hydride-generation atomic absorption spectrometry. Soil As ranged from 2.0 to 45.6 mg kg-1. Geostatistical techniques were used to model and map the spatial variability of As. The mean and variance at unsampled locations were estimated using sequential Gaussian simulation. Five areas of elevated concentration (> the median of 10 mg kg-1) were identified and the relationships to geologic parent materials, glacial sedimentation patterns, and soil conditions interpreted. Our results showed As concentrations >10 mg kg-1 were common, and >20 mg kg-1 were not unusual for the central and west central portions of Ohio (USA). In contrast, concentrations <4 mg kg-1 were rare. Measured concentrations typically exceeded the soil As human generic screening levels of 0.39 mg/kg (1); the calculated value that corresponds to a cancer risk level of 1 in 1,000,000 for soil ingestion. Because the As content of Ohio soils is similar to many world soils, the USEPA generic soil screening level of 0.39 mg/kg is of little utility. A more useful and practical approach would be the uses of natural background levels. Regional soil As patterns based on geology and biogeochemistry and not political boundaries should be used for soil screening and other risk assessment determinations.

arsenic, background, mapping, geostatistics, toxic↗
DOE OSTI2013

Spatially Varying Embedded Stochastic Galerkin Methods for Steady-State PDEs

Existing discretizations for stochastic PDEs, based on a tensor product between the deter ministic basis and the stochastic basis, treat the required resolution of uncertainty as uniform across the physical domain. However, solutions to many PDEs of interest exhibit spatially localized features that may result in uncertainty being severely over or under-resolved by existing discretizations. In this report, we explore the mechanics and accuracy of using a spatially varying stochastic expansion. This is achieved through an adaptive refinement algorithm where simple error estimates are used to independently drive refinement of the stochastic basis at each point in the physical domain. Results are presented comparing the accuracy of the adaptive techinque to the accuracy achieved using uniform refinement.

Cyr, Eric C.↗
DOE OSTI2013

Spatially-Explicit Bat Impact Screening Tool for Wind Turbine Siting

As the U.S. seeks to increase energy production from renewable energy sources, development of wind power resources continues to grow. One of the most important ecological issues restricting wind energy development, especially the siting of wind turbines, is the potential adverse effect on bats. High levels of bat fatality have been recorded at a number of wind energy facilities, especially in the eastern United States. The U.S. Department of Energy contracted with Versar, Inc., and Exponent to develop a spatially-explicit site screening tool to evaluate the mortality of bats resulting from interactions (collisions or barotrauma) with wind turbines. The resulting Bat Vulnerability Assessment Tool (BVAT) presented in this report integrates spatial information about turbine locations, bat habitat features, and bat behavior as it relates to possible interactions with turbines. A model demonstration was conducted that focuses on two bat species, the eastern red bat (Lasiurus borealis) and the Indiana bat (Myotis sodalis). The eastern red bat is a relatively common tree-roosting species that ranges broadly during migration in the Eastern U.S., whereas the Indiana bat is regional species that migrates between a summer range and cave hibernacula. Moreover, Indiana bats are listed as endangered, and so the impacts to this species are of particular interest.

BATS↗
DOE OSTI2014

Property Improvement in CZT via Modeling and Processing Innovations . Te-particles in vertical gradient freeze CZT: Size and Spatial Distributions and Constitutional Supercooling

A section of a vertical gradient freeze CZT boule approximately 2100-mm 3 with a planar area of 300-mm 2 was prepared and examined using transmitted IR microscopy at various magnifications to determine the three-dimensional spatial and size distributions of Te-particles over large longitudinal and radial length scales. The boule section was approximately 50-mm wide by 60-mm in length by 7-mm thick and was doubly polished for TIR work. Te-particles were imaged through the thickness using extended focal imaging to locate the particles in thickness planes spaced 15-µm apart and then in plane of the image using xy-coordinates of the particle center of mass so that a true three dimensional particle map was assembled for a 1-mm by 45-mm longitudinal strip and for a 1-mm by 50-mm radial strip. Te-particle density distributions were determined as a function of longitudinal and radial positions in these strips, and treating the particles as vertices of a network created a 3D image of the particle spatial distribution. Te-particles exhibited a multi-modal log-normal size density distribution that indicated a slight preference for increasing size with longitudinal growth time, while showing a pronounced cellular network structure throughout the boule that can be correlated to dislocation network sizes in CZT. Higher magnification images revealed a typical Rayleigh-instability pearl string morphology with large and small satellite droplets. This study includes solidification experiments in small crucibles of 30:70 mixtures of Cd:Te to reduce the melting point below 1273 K (1000°C). These solidification experiments were performed over a wide range of cooling rates and clearly demonstrated a growth instability with Te-particle capture that is suggested to be responsible for one of the peaks in the size distribution using size discrimination visualization. The results are discussed with regard to a manifold Te-particle genesis history as 1) Te-particle direct capture from melt-solid growth instabilities, 2) Te-particle formation from dislocation core diffusion and the formation and breakup of Te-tubes, and 3) Te-particle formation due to classical nucleation and growth as precipitates.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Developing high-resolution spatial data of migration corridors for avian species of concern in regions of high potential wind development

The future of the US economy, our national security, and our environmental quality all depend on decreasing our reliance on foreign oil and on fossil fuels. An essential component of decreasing this reliance is the development of alternative energy sources. Wind power is among the most important alternative energy sources currently available, and the mid-Atlantic region is a primary focus for wind power development. In addition to being important to the development of wind power, the mid-Atlantic region holds a special responsibility for the conservation of the eastern North America's golden eagles (Aquila chrysaetos). This small population breeds in northeastern Canada, winters in the southern Appalachians, and nearly all of these birds pass through the mid-Atlantic region twice each year. Movement of these birds is not random and, particularly during spring and autumn, migrating golden eagles concentrate in a narrow 30-50 mile wide corridor in central Pennsylvania. Thus, because the fate of these rare birds may depend on responsible management of the habitat they use it is critical to use research to identify ways to mitigate prospective impacts on this and similar raptor species. The goal of this project was to develop high-resolution spatial risk maps showing migration corridors of and habitat use by eastern golden eagles in regions of high potential for wind development. To accomplish this, we first expanded existing models of raptor migration for the eastern USA to identify broad-scale migration patterns. We then used data from novel high-resolution tracking devices to discover routes of passage and detailed flight behavior of individual golden eagles throughout the eastern USA. Finally, we integrated these data and models to predict population-level migration patterns and individual eagle flight behavior on migration. We then used this information to build spatially explicit, probabilistic maps showing relative risk to birds from wind development. This project has numerous benefits to people and to wildlife, primarily because it will provide a framework for safer and less controversial development of wind power. Because golden eagles are an important "umbrella" for other raptors, this project benefits a suite of species that may be impacted by wind turbines. Finally this work is a recognized priority for central Appalachian states and it is explicitly called for in, and meets the goals of, numerous state wildlife conservation plans. The final product we created, a region-wide map of relative risk to eagles of development of wind power, has allowed us to make specific recommendations regarding siting and operation of and mitigation at wind facilities. This approach also serves as a model for other projects to protect eagles in other places and to conserve suites of species beyond raptors.

17 WIND ENERGY↗