1. Dynamical transition in the unsteady flow field of rigid and flexible flapping wings.

Chaos

Natural flyers flap their wings periodically to generate desired aerodynamic loads to fly by leaving a variety of wake patterns in the trail. The nature of these vortex shedding patterns hold the key to the aerodynamic load generation. The manifestation of chaos in the flow-field behind periodically flapping foils is an interesting phenomenon which, in turn, results in chaotic force generation. The leading-edge vortex is found to be the primary trigger behind the transition from order to chaos in the flow topology. Even a small erratic behavior in the leading-edge vortex could spell a complete eventual breakdown of a regular wake, which is sustained by the frequent and spontaneous formation of the vortex couples and the subsequent vortex interactions.


GFM Poster: Chaotic Manifolds of a flapping foil. DOI: 10.1103/APS.DFD.2018.GFM.P0011.

GFM Video: Transition from Order to Chaos in the Wake of a Flapping Airfoil. DOI: 10.1103/APS.DFD.2018.GFM.V0031.

2. Three dimensional flow dynamics behind finite-span flapping wings.

Chaos

More details here..

3. Effect of gust on the unsteady aerodynamics and FSI dynamics of flapping wings.

Chaos

More details here..

4. Experimental Investigation of aerodynamic load generation by flapping wings.

Chaos

More details here..

5. Undulated swimming and fish schooling.

Chaos

More details here..

1. Dynamic stall and stall flutter in turbulent flow regime using hybrid RANS/ DES/ LES.
2. Effect of structural (distributed/ concentrated) and aerodynamic nonlinearity on the bifurcation behavior aeroelastic systems.
3. Synchronization characteristics of nonlinear aeroelastic systems.
4. Improving the lower order aerodynamic models (LVM/ UVLM/ LB) to capture the effect of leading-edge separation.
5. Suppressing the parametric instability of aeroelastic systems using nonlinear energy sinks.

1. Proper orthonal decomposition.
2. Dynamic mode decomposition.

1. Flow dynamics of phonation.

1. Development of robust FSI coupling interfaces.
2. Development of IBM based FSI solver.

1. Physics-informed neural networks.