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Related Concept Videos

Kinematic Equations - II01:17

Kinematic Equations - II

The second kinematic equation expresses the final position of an object in terms of its initial position, the distance traveled with the initial constant velocity, and the distance traveled due to a change in velocity. Similar to the first kinematic equation, this equation is also only valid when the acceleration is constant throughout the motion of an object.
Suppose a car merges into freeway traffic on a 200 m long ramp. If its initial velocity is 10 m/s and it accelerates at 2 m/s2, then the...
Convergent Evolution01:54

Convergent Evolution

Evolution shapes the features of organisms over time, ensuring that they are suited for the environments in which they live. Sometimes, selection pressure leads to the rise of similar but unrelated adaptations in organisms with no recent common ancestors, a process known as convergent evolution.The structures that arise from convergent evolution are called analogous structures. They are similar in function even if they are dissimilar in structure. Further, structures can be analogous while also...
Kinematic Equations - III01:18

Kinematic Equations - III

The first two kinematic equations have time as a variable, but the third kinematic equation is independent of time. This equation expresses final velocity as a function of the acceleration and distance over which it acts. The fourth kinematic equation does not have an acceleration term and provides the final position of the object at time t in terms of the initial and final velocities. This equation is useful when the value of the constant acceleration is unknown.
Using the kinematic equations,...
Kinematic Equations - I01:26

Kinematic Equations - I

When an object moves with constant acceleration, the velocity of the object changes at a constant rate throughout the motion. The kinematic equations of motions are derived for such cases where the acceleration of the object is constant. The first kinematic equation gives an insight into the relationship between velocity, acceleration, and time. We can see, for example:
Vector Functions and Motion: Problem Solving01:30

Vector Functions and Motion: Problem Solving

Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...
Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...

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Generation of Warfighter Avatars from Weapon Training Scene Images for Blast Exposure Simulations
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Simulating avian wingbeat kinematics.

Ben Parslew1, William J Crowther

  • 1School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, UK. ben.parslew-2@postgrad.manchester.ac.uk

Journal of Biomechanics
|August 25, 2010
PubMed
Summary

Researchers simulated bird flight using inverse dynamics, finding wingbeat patterns change gradually with speed. Pigeon flight analysis suggests retracting wings during upstrokes saves energy.

Area of Science:

  • Biomechanics
  • Aerodynamics
  • Computational Biology

Background:

  • Understanding avian flight mechanics is crucial for biomechanical and aerodynamic research.
  • Simulating complex wingbeat kinematics requires advanced computational models.

Purpose of the Study:

  • To simulate avian wingbeats under varying flight conditions using inverse dynamics.
  • To optimize bird flight models for minimal mechanical power output while achieving desired aerodynamic forces.

Main Methods:

  • Construction of a geometrically scalable multi-segment bird model.
  • Application of optimization techniques to determine segment motions for efficient flight.
  • Analysis of wingbeat kinematics and aerodynamic force coefficients at different speeds.

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Main Results:

  • Wingbeat kinematics exhibit gradual changes with cruise speed, aligning with experimental observations.
  • Optimized pigeon flight simulations indicate upstroke wing retraction conserves energy.
  • Aerodynamic force coefficient variations suggest a new gait metric incorporating thrust and lift.

Conclusions:

  • Bird flight kinematics are adaptable to varying speeds, with energy-saving mechanisms like wing retraction observed.
  • A comprehensive gait metric for avian flight should consider both thrust and lift generation across wingstrokes.