Piezoelectric energy harvesters (PEHs) are a potential alternative to batteries in large-scale sensor networks and implanted health trackers, but the low output power and the narrow work range has been a bottleneck for its practical application.
To alleviate this problem, the present research will develop a data-driven reduced-order model for flow-induced PEHs based on the dataset obtained from a nonlinear and parametric electro-mechanical model. This model will be a high-fidelity monolithic computational model established by the weighted residuals method and corresponding numerical solutions will be calculated by the finite element method in FEniCS. Then a projection-based model order reduction will be implemented and machine learning will be introduced to address challenges resulting from nonlinearity and multi-parameters.
Once the reduced-order model is validated, a reliable and fast method to predict the performance of flow-induced PEHs will be achieved, promising real-time optimization of the design of PEHs. It will promote the further commercialization of PEHs.