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95 results for “Aerodynamics”
Flapping Wing Aerodynamics with PRSSM
<p>Flying animals resort to fast, large-degree-of-freedom motion of flapping wings, a key feature that distinguishes them from rotary or fixed-winged robotic fliers with limited motion of aerodynamic surfaces. However, flapping-wing aerodynamics are characterised by highly unsteady and three-dimensional flows difficult to model or control, and accurate aerodynamic force predictions often rely on expensive computational or experimental methods. Here, we developed a computationally efficient and data-driven state-space model to dynamically map wing kinematics to aerodynamic forces/moments. This model was trained and tested with a total of 548 different flapping-wing motions and surpassed the accuracy and generality of the existing quasi-steady models. This model used 12 states to capture the unsteady and nonlinear fluid effects pertinent to force generation without explicit information of fluid flows. We also provided a comprehensive assessment of the control authority of key wing kinematic variables and found that instantaneous aerodynamic forces/moments were largely predictable by the wing motion history within a half-stroke cycle. Furthermore, the angle of attack, normal acceleration, and pitching motion had the strongest effects on the aerodynamic force/moment generation. Our results show that flapping flight inherently offers high force control authority and predictability, which can be key to developing agile and stable aerial fliers.</p>
Stiff stabilisation and position control of suspended loads with aerodynamic actuators
<p>The video presents the work done in the project 'Position control of suspended loads via aerodynamic thrust for sling load rescue operations', financed by the Spark SNF programme.</p>
Data for publication "Aerodynamic interactions of drops on parallel fibers"
<p>A collection of the data and scripts used to produce figures and derive conclusions for: "Aerodynamic interactions of drops on parallel fibers."</p> <p>The DOI number for this paper is 10.1038/s41567-023-02159-4. It is available at the following URL: <a href="https://www.nature.com/articles/s41567-023-02159-4">https://www.nature.com/articles/s41567-023-02159-4</a>.</p> <p>Please see Repo_Contents.pdf for details.</p>
A Generic Model for Benchmark Aerodynamic Analysis of Fifth-Generation High-Performance Aircraft
<p>Openly available supplementary data to accompany paper https://doi.org/10.3390/aerospace10090746. Data set includes geometry, Pointwise (2022.1.2) and Fluent (2022R1) grid files and corrected experimental data for lift, drag and pitching moment at a freestream velocity of 20 m/s and standard sea level conditions for the SSAM-Gen5 model. When using this data, please cite:</p> <p>Giannelis, N.F.; Bykerk, T.; Vio, G.A. A Generic Model for Benchmark Aerodynamic Analysis of Fifth-Generation High-Performance Aircraft. Aerospace 2023, 10, 746.</p>
Datasheet-Experimental and Computational Strategies for Improving the Aerodynamic Performance with SETE
<p>Paper Title: Experimental and Computational Strategies for Improving The Aerodynamic Performance with SETE</p> <p>Paper Abstract: An open-loop wind tunnel experiment and Computational Fluid Dynamics simulations are carried out on several Static Extended Trailing Edge (SETE) attached to the NACA0012 airfoil wing model to investigate the effect of a SETE on the aerodynamic characteristics at low Reynolds. A thin extended trailing edge of length 20% of the chord and 15° deflection appears to generate 74.24% more lift concerning the baseline model. SETE enables a smooth, gradual stall, resulting in more time for stall recovery. A high benefit margin makes SETE an ideal option to achieve more cruise flight efficiency.</p> <p>*This repository contains:</p> <ol> <li><strong>Datasheet </strong> <p>Datasheet Contents:</p> <p>Sheet 1: Wind Tunnel Experiment Data (Paper section <strong>IV.A.3</strong>)</p> <p>Sheet 2: CFD Simulation Data (Paper section <strong>IV.B.3</strong>)</p> <p>Sheet 3: Baseline Model (Paper section <strong>IV.A.1-2</strong>)</p> <p>Sheet 4: Calculation of Lift Increment (Paper section <strong>IV.C</strong>)</p> <p>Sheet 5: Calculation of Benefit Margin (Paper section <strong>IV.D</strong>)</p> <p>Sheet 6: Coefficient of Pressure sample (Paper section <strong>IV.B.3</strong>)</p> </li> <li> <p><strong>CAD geometries in .STEP format</strong></p> </li> <li> <p>Ansys Workbench files (Upon request)</p> </li> </ol> <p>Contact: nazibeadin@gmail.com</p>
DAeVid - Dummy for Aerodynamic Validation
<p>The simplified athlete geometry, denoted DAeVid (Dummy for Aerodynamic Validation), was prepared by merging, sculpting and smoothing existing athlete 3D scans to generate an anonymized 3D model. The legs and parts of the arms were removed to simplify the geometry, and make it more generalized across multiple sports disciplines. The intented purpose is for validation and comparison of,computational fluid dynamics models within sports aerodynamics.</p>
The aerodynamic force platform as an ergometer
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Dynamic snow surface aerodynamic roughness lengths (z0) characterized by snow depths using LIDAR
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An aerodynamic perspective on hurricane-induced selection on Anolis lizards
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Hovering flight in hummingbird hawkmoths: Kinematics, wake dynamics and aerodynamic power
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Fresh snow, peak accumulation, and ablation-Sun Cup snow surface datasets for evaluation of geometry aerodynamic roughness code
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Flapping Wing Aerodynamics with PRSSM
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Data from: Ecology of tern flight in relation to wind, topography and aerodynamic theory
Flight is an economic mode of locomotion, because it is both fast and relatively cheap per unit of distance, enabling birds to migrate long distances and obtain food over large areas. The power required to fly follows a U-shaped function in relation to airspeed, from which context dependent 'optimal' flight speeds can be derived. Crosswinds will displace birds away from their intended track unless they make compensatory adjustments of heading and airspeed.We report on flight track measurements in five geometrically similar tern species ranging one magnitude in body mass, from both migration and the breeding season at the island of O ¨ land in the Baltic Sea. When leaving the southern point of O ¨ land, migrating Arctic and common terns made a 608 shift in track direction, probably guided by a distant landmark. Terns adjusted both airspeed and heading in relation to tail and sidewind, where coastlines facilitated compensation. Airspeed also depended on ecological context (searching versus not searching for food), and it increased with flock size. Species-specific maximum range speed agreed with predicted speeds from a new aerodynamic theory. Our study shows that the selection of airspeed is a behavioural trait that depended on a complex blend of internal and external factors.
Wind Tunnel Measurements of Aerodynamic Entrainment Rate of Particles
<p>This dataset is related to a wind tunnel experiment of aerodynamic entrainment rate of particles. Wind profiles are measured by pitot tube, for calibrating surface shear stress measured by Irwin sensors. Aerodynamic entrainment rate is measured through the mass difference weighting before and after the erosion event. Each case is repeated three times to find the average value and the error. The experimental setup is showed in figure 1.</p>
Figure 14 in Computational investigation of cicada aerodynamics in forward flight
Figure 14. Vortex structure at the end of downstroke (coloured by unified spanwise vorticity, colour max/min ¼ ±4). (Online version in colour.)
Figure 13 in Computational investigation of cicada aerodynamics in forward flight
Figure 13. Transverse plane cut at mid-downstroke. (a) Cut through wing and body and (b) cut through the near wake (no wings or body being cut). (i) Contour of the Q-criterion, velocity vector and two-dimensional streamline seen from the back view. (ii) Vortex structure (Q ¼ 20), and the velocity vector. The view angle is different from that in column (i), and is adjusted to give a better view of the vortex structure. The velocity vectors are three dimensional, and are drawn at every three grid points only. (Online version in colour.)
Figure 11 in Computational investigation of cicada aerodynamics in forward flight
Figure 11. Force generation in one stroke. (a) Total force (FXT, FYT, FZT) is composed of forces from wings in both sides and the cicada body. (b) Force generated by the right wings and the body. (Online version in colour.)
Figure 9 in Computational investigation of cicada aerodynamics in forward flight
Figure 9. Leading edge vortex at the mid-downstroke, coloured by the spanwise vorticity. (Online version in colour.)
Figure 12 in Computational investigation of cicada aerodynamics in forward flight
Figure 12. Instantaneous specific power in a stroke cycle of cicada flight. (Online version in colour.)
Figure 6. Wing motion during a in Computational investigation of cicada aerodynamics in forward flight
Figure 6. Wing motion during a stroke. (a) Start of downstroke; (b) middownstroke; (c) start of upstroke; and (d) mid-upstroke.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.