Assessing voltage and power prediction in vibrating cylinders using machine learning algorithms: Insights from wind tunnel experiments

Abstract

Flow-induced vibrations (FIV) of circular cylinders offer a promising mechanism for low-power energy harvesting, but accurately predicting the resulting voltage and power is challenging due to the nonlinear nature of fluid–structure interactions. In this study, wind tunnel experiments were conducted to generate three datasets based on different configurations of tandem circular cylinders. The datasets were used to evaluate the performance of three machine learning regression algorithms including Support Vector Regression (SVR), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost), in predicting the root mean square (RMS) voltage and harvested power. Sobol sensitivity analysis was applied to quantify the influence of input parameters. XGBoost showed the best performance, with R² values of 0.91, 0.98, and 0.86 for datasets 1, 2, and 3. Despite using 1000 estimators, the XGBoost model demonstrated efficient training time due to its parallel tree boosting structure and built-in regularization, offering a favorable balance between accuracy and computational complexity. Sensitivity analysis revealed that the displacement between cylinders and upstream cylinder diameter were the most influential parameters depending on the configuration. The results show that machine learning techniques, particularly XGBoost, can successfully model complex nonlinear relationships in FIV-based energy harvesting systems, providing a data-driven tool for improving design and efficiency.