In the last few decades, piezoelectric (PZT) materials have played a vital role in the aerospace industry because of their energy harvesting capability. PZT energy harvesters (PEH) absorb the energy from an operational environment and can transform it into useful energy to drive nano/micro-electronic components. In this research work, a PEH based on the flag-flutter mechanism is presented. This mechanism is based on fluid-structure interaction (FSI). The flag is subjected to the axial airflow in the subsonic wind tunnel. The performance evaluation of the harvester and aeroelastic analysis is investigated numerically and experimentally. A novel solution is presented to extract energy from Limit Cycle Oscillations (LCOs) phenomenon by means of PZT transduction. The PZT patch absorbs the flow-induced structural vibrations and transforms it into electrical energy. Furthermore, the optimal resistance and length of the flag is predicted to maximize the energy harvesting. Different configurations of flag i.e., with Aluminium (Al) patch and PZT patch for flutter mode vibration mode are studied numerically and experimentally. The bifurcation diagram is constructed for the experimental campaign for the flutter instability of a cantilevered flag in subsonic wind-tunnel. Moreover, the flutter boundary conditions are analysed for reduced critical velocity and frequency. The designed PZT energy harvester via flag-flutter mechanism is suitable for energy harvesting in aerospace engineering applications to drive wireless sensors. The maximum output power that can be generated from the designed harvester is 6.72 mW and the optimal resistance is predicted to be 0.33 M$\Omega$.

In this paper, a nonlinear reduced-order model based on neural networks is introduced in order to model vertical sloshing in presence of Rayleigh–Taylor instability of the free surface for use in fluid–structure interaction simulations. A box partially filled with water, representative of a wing tank, is first set on vertical harmonic motion via a controlled electrodynamic shaker. Accelerometers and load cells at the interface between the tank and an electrodynamic shaker are employed to train a neural network-based reduced-order model for vertical sloshing. The model is then investigated for its capacity to consistently simulate the amount of dissipation associated with vertical sloshing under different fluid dynamics regimes. The identified tank is then experimentally attached at the free end of a cantilever beam to test the effectiveness of the neural network in predicting the sloshing forces when coupled with the overall structure. The experimental free response and random seismic excitation responses are then compared with that obtained by simulating an equivalent virtual model in which the identified nonlinear reduced-order model is integrated to account for the effects of violent vertical sloshing.