Abstract:

Optimizing wind energy harvesting performance remains a significant challenge. Machine learning (ML) offers a promising approach for addressing this challenge. This study proposes an ML-based approach using the radial basis function neural network (RBFNN) and differential evolution (DE) to predict and optimize the structural parameters (the diameter of the spherical bluff body D, the total spring stiffness k, and the length of the piezoelectric cantilever beam L) of the wind energy harvester (WEH). The RBFNN model is trained with theoretical data and validated with wind tunnel experimental results, achieving the coefficient-of-determination scores R^2 of 97.8% and 90.3% for predicting the average output power P_{\text{avg}} and aero-electro-mechanical efficiency {\eta }_{\text{aem}}, respectively. The DE algorithm is used to identify the optimal parameter combinations for wind speeds U ranging from 2.5 m/s to 6.5 m/s. The maximum P_{\text{avg}} is achieved when D=57.5 mm, k=28.8 N/m, L=112.1 mm, and U=4.6 m/s, while the maximum {\eta }_{\text{aem}} is achieved when D=52.7 mm, k=29.2 N/m, L=89.2 mm, and U=4.7 m/s. Compared with that of the non-optimized structure, the WEH performance is improved by 28.6% in P_{\text{avg}} and 19.1% in {\eta }_{\text{aem}}.

Key words: wind energy harvester (WEH), vortex-induced vibration (VIV), piezoelectric effect, machine learning (ML), radial basis function neural network (RBFNN), differential evolution (DE)