The British Klemm "Eagle" commercial airplane : a low-wing cantilever monoplane
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Rapid expansion of wind energy development across the world has highlighted the need to better understand turbine-caused avian mortality. The risk to golden eagles (Aquila chrysaetos) is of particular concern due to their small population size and conservation status. Golden eagles subsidize their flight in part by soaring in orographic updrafts, which can place them in conflict with wind turbines utilizing the same low-altitude wind resource. Understanding the behavior of soaring raptors in varying atmospheric conditions can therefore be relevant to predicting and mitigating their risk of collision. We present a predictive movement model that simulates individual paths of golden eagles during directional flight (such as migration) that is subsidized by orographic updraft. We modeled eagles in a 50 km by 50 km study area in Wyoming containing three wind power plants with documented golden eagle collisions with turbines. The movement model is applicable to any region where ground elevation is known at turbine scale (50 m) and wind conditions are known at facility scale (3 km). For a given set of atmospheric conditions, the model simulates movements of thousands of orographic soaring eagles to produce a density map quantifying the relative probability of eagle presence. We validated the simulated tracks with GPS telemetry data showing four directional tracks made by golden eagles transiting through the area in 2019 and 2020. For each eagle track, validation was performed using the ratio of the model-simulated eagle presence likelihood with uniform eagle presence and the presence computed using directed random-walk movements. We found that the predictive performance of the model was significantly better (likelihood ratio 1) for low-altitude movements than high-altitude movements that can involve thermal-soaring. We employed the model to produce seasonal presence maps for migrating golden eagles. We found significant turbine-level variations in eagle presence between northerly and southerly migration routes through the study area. Overall, the proposed model offers a generalizable, probabilistic, and predictive tool to assist wind energy developers, ecologists, wildlife managers, and industry consultants in estimating the potential for conflict between soaring birds and wind turbines, thereby reducing the need for site-specific data on golden eagle movements.
Advances in fine-scale movement modeling of soaring birds can aid efforts to understand and resolve the impacts of anthropogenic activities on such birds. Soaring birds often rely on underlying terrain and low-altitude updrafts to govern their flights at rotor-swept altitudes (≤ 200 m above ground level), which puts them at risk of collision with wind turbines. We developed a data-driven Markov model at 1-s resolution that predicts the fine-scale flight behavior of golden eagles (Aquila chrysaetos) as a function of ecological covariates at the current location as well as those within an eagle's line of sight. We only considered ecological covariates that are readily available in real-time (ground elevation and wind conditions). Latent factors (age, sex, species, behavioral intent, migratory status) were intentionally left out of the model. We calibrated the model using golden eagle telemetry data collected in two different ecoregions of the United States. Given a starting location, the calibrated model simulates multiple stochastic 3D paths to produce a time-explicit and spatially explicit risk map of turbine collisions. We discovered an empirical relation between the rate of change of heading and the orographic updraft conditions within an eagle's line of sight. Our model performed most effectively when predicting predominantly-soaring flights at rotor-swept altitudes during wind conditions in which turbines are likely to be operational. The calibrated model could be used in concert with automated eagle detection and turbine curtailment technologies. Specifically, once an eagle is detected by those systems, our model could then provide accurate predictions of turbines the eagle is likely to interact with in the near term.
SSRS (Stochastic Soaring Raptor Simulator) is a generalizable, probabilistic, and predictive tool for wind energy developers, ecologists, wildlife managers and industry consultants to estimate the potential for soaring raptors to interact with operating wind turbines, without the need for site-specific data collection. Rapid expansion of wind energy development across the world has exposed the risk of turbine collisions for birds and bats. The risk to obligate soaring raptors such as golden eagles is of particular concern due to their small population and influence on ecological balance. Golden eagles rely heavily on updrafts to subsidize their flight, putting them in direct conflict with operational wind turbines that utilize the same wind resource. Understanding the behavior of soaring raptors with varying atmospheric conditions is crucial for predicting and mitigating the risk of turbine collision. This software contains a predictive movement model that simulates individual flight paths of golden eagles during updraft-subsidized long-distance flight, including migration. For a given set of atmospheric conditions, the model simulates thousands of eagles at turbine-scale spatial resolution (50m) to produce a relative presence density map. The simulated eagles rely on updrafts to pursue uninterrupted directional flight with minimal energy expenditure, following fluid-flow principles. The simulator includes a stochastic model of eagle behavior and a systematic method of accounting for spatiotemporal variations in atmospheric conditions. This framework only requires publicly available atmospheric data to estimate orographic and thermal updrafts, ensuring general usability.
Golden Eagle (Aquila chrysaetos) habitats may overlap with wind energy development in some regions of the US. Eagles, and similar soaring bird species, are therefore at risk of collision with wind turbines when flying through wind farms. Recently developed behavioral modeling approaches can predict the presence of eagles near turbines within the rotor-swept layer but require reliable prediction of atmospheric flowfield conditions. In particular, the vertical component of the wind speed dictates a soaring bird's ability to maintain or gain altitude, since they rely on updrafts to subsidize their flight. In this work, we investigate the atmospheric conditions around a wind farm in complex terrain and compare methods for atmospheric characterization. We use computational fluid dynamics (specifically, large-eddy simulations, or LES) to simulate the atmospheric boundary layer over a region encompassing multiple wind farms with high temporal and spatial resolution (seconds and 10's of meters, respectively). We compare traditional non-simulation-based methods of determining the orographic updraft potential based on wind direction, terrain slope and aspect, with the flowfields from LES that include both orographic updrafts alone and combined thermal and orographic updrafts. Preliminary analysis suggests that although the model captures the horizontal pattern of vertical updrafts, their magnitude can be improved with information about the surface heat flux, which is usually correlated with time of the day. Within our study region, we found that the low-fidelity model may over- or underestimate updraft potential by up to 400% at 80 m AGL, depending on local orographic features. This can result in an inaccurate representation of eagle presence and, consequently, risk. Another important finding is that flowfield time-averaging can hide important details about the flight environment, including how thermally generated flow structures within the atmospheric boundary layer (e.g., convective rolls and/or cells) may be important drivers of eagle flight.
Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSFs) or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from golden eagles Aquila chrysaetos from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During the summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.
A preliminary design study is presented for the Lidar Atmospheric Sounder and Altimeter (LASA), an active remote sensor for the Earth Observing System. LASA is designed to measure the global vertical distributions of aerosols, cloud top heights, atmospheric trace gases, atmospheric temperature and pressure, and the height of the PBL. The measurements are made using optical radar, differential absorption lidar, and laser altimetry. An experiment proposed for the LASA facility, the EOS Atmospheric Global Lidar Experiment (EAGLE) is described in detail. The EAGLE program is planned to determine aerosol and cloud distributions, water vapor concentrations, and temperature profiles over the troposphere and aerosol distribution over the stratosphere. The measurement techniques, receiver system, and transmitter for the EAGLE program are examined and the conceptual design of the EAGLE experiment and the LASA/EAGLE interface is outlined.
An old and proven grid generation code, the EAGLE grid generation package, is given an added dimension of a graphical interface and a real time data base manager. The Numerical Aerodynamic Simulation (NAS) Panel Library is used for the graphical user interface. Through the panels, EAGLEView constructs the EAGLE script command and sends it to EAGLE to be processed. After the object is created, the script is saved in a mini-buffer which can be edited and/or saved and reinterpreted. The graphical objects are set-up in a linked-list and can be selected or queried by pointing and clicking the mouse. The added graphical enhancement to the EAGLE system emphasizes the unique capability to construct field points around complex geometry and visualize the construction every step of the way.
We estimate the Milky Way (MW) halo properties using satellite kinematic data including the latest measurements from Gaia DR2. With a simulation-based 6D phase-space distribution function (DF) of satellite kinematics, we can infer halo properties efficiently and without bias, and handle the selection function and measurement errors rigorously in the Bayesian framework. Applying our DF from the EAGLE simulation to 28 satellites, we obtain an MW halo mass of $M={1.23}_{-0.18}^{+0.21}\times {10}^{12}{M}_{\odot }$ and a concentration of $c={9.4}_{-2.1}^{+2.8}$ with the prior based on the M–c relation. The inferred mass profile is consistent with previous measurements but with better precision and reliability due to the improved methodology and data. Potential improvement is illustrated by combining satellite data and stellar rotation curves. Using our EAGLE DF and best-fit MW potential, we provide much more precise estimates of the kinematics for those satellites with uncertain measurements. Compared to the EAGLE DF, which matches the observed satellite kinematics very well, the DF from the semi-analytical model based on the dark-matter-only simulation Millennium II (SAM-MII) over-represents satellites with small radii and velocities. We attribute this difference to less disruption of satellites with small pericenter distances in the SAM-MII simulation. Finally, by varying the disruption rate of such satellites in this simulation, we estimate a ~5% scatter in the inferred MW halo mass among hydrodynamics-based simulations.
The MIT Light Eagle human-powered aircraft underwent long-duration testing over Rogers Dry Lake in California during January, 1987. Designed as a prototype for the MIT Daedalus Project, the Light Eagle's forty-eight flights provided pilot training, established new distance records for human-powered flight, and provided quantitative data through a series of instrumented flight experiments. The experiments focused on: (1) evaluating physiological loads on the pilot, (2) determining airframe power requirements, and (3) developing an electronic flight control system. This paper discusses the flight test program, its results and their implications for the follow-on Daedalus aircraft, and the potential uses of the Light Eagle as a low Reynolds number testbed.
Testing for the Space Station Freedom Nickel Hydrogen Cell Test Program began in 1990 at Crave Division, Naval Surface Warfare Center. The program has included receipt inspection, random vibration, acceptance, characterization, and life cycle testing of Ni-H2 cells in accordance with the NASA LeRC Interagency Order C-31001-J. A total of 400 Ni-H2 cells have been received at NAVSURFWARCENDIV Crane from three separate manufacturers; Yardney Technical Products (Yardney), Eagle Picher Industries (Eagle Picher), and Gates Energy Products (Gates). Of those, 308 cells distributed among 39 packs have undergone life cycle testing under a test regime simulating low earth orbit conditions. As of 30 September 1993, there are 252 cells assembled into 32 packs still on life cycle test. Since the beginning of the program, failed cells have been detected in all phases of testing. The failures include the following; seven 65 AmpHr and 81 AmpHr Yardney cells were found to be leaking KOH on receipt, one 65 AmpHr Eagle Picher cell failed the acceptance test, one 65 AmpHr Gates cell failed during the characterization test, and six 65 AmpHr Gates cells failed the random vibration test. Of the 39 life cycle packs, testing on seven packs, 56 cells, has been suspended because of low end of discharge voltages. All of the failed life cycle packs were cycled at 60% depth of discharge.
Eagle-Picher currently has several advanced nickel-hydrogen (NiH2) cell component and battery designs under development including common pressure vessel (CPV), single pressure vessel (SPV), and dependent pressure vessel (DPV) designs. A CPV NiH2 battery, utilizing low-cost 64 mm (2.5 in.) cell diameter technology, has been designed and built for multiple smallsat programs, including the TUBSAT B spacecraft which is currently scheduled (24 Nov. 93) for launch aboard a Russian Proton rocket. An advanced 90 mm (3.5 in.) NiH2 cell design is currently being manufactured for the Space Station Freedom program. Prototype 254 mm (10 in.) diameter SPV batteries are currently under construction and initial boilerplate testing has shown excellent results. NiH2 cycle life testing is being continued at Eagle-Picher and IPV cells have currently completed more than 89,000 accelerated LEO cycles at 15% DOD, 49,000 real-time LEO cycles at 30 percent DOD, 37,800 cycles under a real-time LEO profile, 30 eclipse seasons in accelerated GEO, and 6 eclipse seasons in real-time GEO testing at 75 percent DOD maximum. Nickel-metal hydride battery development is continuing for both aerospace and electric vehicle applications. Eagle-Picher has also developed an extensive range of battery evaluation, test, and analysis (BETA) measurement and control equipment and software, based on Hewlett-Packard computerized data acquisition/control hardware.
During the first six months of 1994, the NAS (National Airspace System) 16-CPU Y-MP C90 Von Neumann (VN) delivered an average throughput of 4.045 GFLOPS while the ACSF (Aeronautics Consolidated Supercomputer Facility) 8-CPU Y-MP C90 Eagle averaged 1.658 GFLOPS. The VN rate represents a machine efficiency of 26.3% whereas the Eagle rate corresponds to a machine efficiency of 21.6%. VN displayed a greater efficiency than Eagle primarily because the stronger workload demand for its CPU cycles allowed it to devote more time to user programs and less time to idle. An additional factor increasing VN efficiency was the ability of the UNICOS 8.0 Operating System to deliver a larger fraction of CPU time to user programs. Although measurements indicate increasing vector length for both workloads, insufficient vector lengths continue to hinder HSP (High Speed Processor) performance. To improve HSP performance, NAS should continue to encourage the HSP users to modify their codes to increase program vector length.
The Moessbauer spectrometers on the twin MER rovers Spirit and Opportunity have provided significant new information on the distribution of iron among its oxidation states, the identification of the mineralogical composition of iron-bearing phases, and the distribution of iron among those phases for rock and soil at Gusev Crater and Meridiani Planum. The plains of Gusev Crater are dominated by olivine-bearing basalt (approximately Fo(60)) and Fe(3+)/Fe(total)=0.1 - 0.5. The oxide mineral generally present is magnetite. In contrast, initial results for the Columbia Hills are consistent with the presence of hematite and a ferrous iron phase, possibly pyroxene. Gusev spectra also have a ferric doublet (not jarosite) that is tentatively associated with nano-phase ferric oxide. A wider diversity of material is present at Meridiani Planum. Significantly, jarosite-bearing outcrop is present throughout the region, with good exposures in impact craters such as Eagle and Endurance (Fe(3+)/Fe (total) approx. 0.9). The Moessbauer identification of jarosite (a hydroxyl-bearing sulfate mineral) is evidence for aqueous, acid-sulfate processes on Mars. Hematite is observed within the outcrop matrix and in the spheroidal particles (Blueberries) found within the outcrop and as a surface lag. An isolated rock (Bounce Rock) was the only sample at either landing site whose iron-bearing phase was dominated by pyroxene. The basaltic sand in the central portion of Eagle crater, in the intercrater plains, and between slabs of outcrop at both Eagle and Endurance craters is olivine-bearing basalt. The widespread occurrence of olivine-bearing basalt at both MER landing sites implies that physical, rather than chemical, weathering processes dominate at the surface of contemporary Mars.