HARVESTING WIND ENERGY VIA PIEZOELECTRIC FLAGS
Recent years have seen a tremendous increase in the use of small-scale sensors, biomedical devices and wearable electronics. Most of these devices require small amounts of power. However, short operative life of batteries forces a routinely replacement. Piezoelectric materials are an attractive alternative to recharge batteries by converting mechanical energy into electrical energy.
The video shows the flexible piezoelectric flag during self-sustained flapping inside a custom built wind tunnel.
The novelty of the work is the orientation and position of the piezo-leaf with respect to the wind direction called "inverted flag". The self-fluttering only relies in a non-dimensional parameter called bending stiffness.
The piezo-leaf reached fluttering at a desire wind speeds including slow ambient-city or faster off-shore wind conditions. Just by simply adjusting the length and properties of the piezo-leaf (e.g. stiffness), we can tune the desire wind regime to induce fluttering.
We harvested wind energy from the flow-induced flutter of a piezoelectric membrane called “piezo-leaf ”. One of the novel features of this research is that the piezo-leaf can generate self-sustained oscillations with large deformations at any desired wind speed regime.
Left video: High-speed videography revealed vortices formation (mechanism driving the self-fluttering) during self-sustained oscillations matching with numerical simulations observations. The video was taken at 600 frames per second and slowed down half speed. Right video: CFD simulations revealed formation of periodic vortices, which contribute to the self-induced sustained osculations. The colormap represents the vorticity.
Using this method to harness the wind’s mechanical energy, we provide a durable power source that can be deployed in extremely remote locations without access to an electric grid. The following YouTube video shows the live feed of the current outdoor setup and the operation of the temperature sensor.
Results from this multi-disciplinary work has been obtained with collaborative efforts between the Flow Physcis & Computational Lab and Kang Group under the supervision of Prof. Rajat Mittal and Prof. Sung Hoon Kang, respectively.
Mr. Andre Ruas
Mr. Aaron Rips
Mr. Kyle Doran
Mr. Brett Caggiano