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Dragonfly: The Mysterious Insect Driving Engineering Innovation


Who hasn't dreamt of flying like a bird? From Leonardo da Vinci’s drawings of flying machines to Otto Lilienthal’s gliders, inventors have focused, quite logically, on human transport. We now take flying on airplanes for granted. But mechanical flight on a smaller, insect-level scale is less well-known. Micro-air vehicles (MAVs) have gained popularity in recent years due to wide range of small-scale applications in areas such as military, transportation, electronics, security systems, search and rescue missions, video recordings and many more. Successful prototypes depend upon valid, yet imaginative, designs as a starting point.

For small-scale flying with flapping wings, we turn naturally to mimicking the flight mechanisms of birds and insects. For this purpose, there is no better model of a flying creature than the dragonfly. The dragonfly is one of the most highly maneuverable flying insects on the earth. It can achieve speeds up to 55 km/h, turn 360° in microseconds, fly sideways, glide, hover in the air and even go backwards. Many simulation engineers have used the dragonfly’s physics and governing equations to capture, analyze and even try to improve on its the flying mechanism.

Geometry in ANSYS SpaceClaim

You can explore the simulation of dragonfly flight right now by accessing the free ANSYS Student version instantly and building this test model step-by-step.

Producing a dragonfly’s geometry is challenging due to the varying curvature over both body and wings in all three directions. ANSYS SpaceClaim makes it easy for you to import and edit the geometry and extract the fluid volume. Two fluid zones are created, one around the body and one around the wings. The inner zone enclosing the wings controls the mesh size in the flapping region. To reduce the number of mesh nodes and the computation time, you can model only half of the geometry of the dragonfly by defining a symmetry plane.

Good quality meshing (maximum skewness < 0.85) is desired around the wing since the dynamic mesh solver will further modify the mesh when the wing is in motion. Tetrahedral elements are created throughout the fluid domain using ANSYS meshing. The inner fluid volume is meshed with a fixed cell size, whereas a specified finer mesh is created over the surface of the dragonfly body to capture the wakes. In the first step, the wings are separated from the body by three to four cells to avoid meshing issues; in the next step this distance can be reduced down to one cell.

Volume and surface meshing

Wing Kinematics: Many researchers have studied the hovering flight mechanism of dragonflies using high speed cameras to convert the realistic wing motion into mathematical equations. The kinematic pitching and flapping motions of the forewings are derived from Azuma’s equation (Ref: Flight Mechanics of Dragonfly, Azuma et al.). The motion of forewing and hindwing is separated by 0.004 seconds, with the hindwing following the forewing. You need a user-defined function (UDF) describing this motion for simulation using ANSYS Fluent.

Angular Velocity vs Time

The dragonfly is assumed to be hovering at a fixed location above the ground with air flowing over the body at 2 m/s. Dynamic meshing with spring-based smoothing and local remeshing in Fluent is perfectly suited for moving and deforming meshes. A negligible spring constant and local remeshing of cell parameters computed by the solver are used with a motion UDF assigned to wings. A pressure-based unsteady solver with a time step of 0.00002 s is used to make sure that the dynamic meshing algorithm works perfectly with no negative volumes.

Generated pressure field over body and air vortex around wings

The primary aim of this exercise is to examine the flow field around the wings and the resulting pressure field due to wing motion. If you want to take it a few steps further, try calculating the lift-to-drag ratio for improving aerodynamic performance, updating wing motion and optimizing the wing shape. In conclusion, simulation is a quick way to optimize complicated small-scale, model-based flying mechanisms. Here is the short video describing this model. I hope you enjoyed this exercise.