Fatigue Analysis of a Gallop-based Piezoelectric Wind Energy Harvester and Strategies for Long-Term Performance Optimization

With great demand for novel forms of renewable energy, there has been substantial interest in wind energy harvesting based on aeroelastic excitations with the aid of piezoelectric materials. While most research has been dedicated to optimizing power output [17], relatively little research has been done on the device’s behavior in response to fatigue, which may be a key failure mode given its persistent cyclic loading. A Python program was created to simulate a galloping piezoelectric wind energy harvesting (GPEH) under varying wind conditions in real time, monitoring performance metrics until fatigue failure. This can generate Monte Carlo distributions for optimizing GPEH system parameters for durability. This could then determine if such a form of renewable energy is currently feasible and if so, provide a database of effective materials for the device’s implementation. In tandem with this, analytical optimization methods were tested, but remain partially inconclusive, requiring better dimensional analysis to simplify the myriad of system parameters. However, initial runs of the code still provided useful data, identifying an optimal performance state of all those tested, indicating that the tradeoff between performance and durability is not either-or.