Numerical analysis and data-driven optimization of energy harvesting efficiency from wake-induced vibration in a multi-cylinder configuration

Wake-induced vibrations (WIV) in multi-cylinder configurations have demonstrated greater energy harvesting efficiency in hydrokinetic applications compared to conventional vortex-induced vibrations (VIV) of a single cylinder. However, the complex fluid-structure interactions make it challenging to identify optimal configurations for maximum power output, as extensive simulations across numerous parameter combinations lead to substantial computational costs with traditional computational fluid dynamics (CFD) methods. To address this challenge, we developed a data-driven model using automated machine learning (AutoML) techniques, focusing on four key parameters: spacing, diameter, damping, and reduced velocity. Trained on comprehensive datasets from validated CFD simulations, this model integrates multiple algorithms to predict the power efficiency of WIV systems with high accuracy. Our approach enables rapid and precise evaluations of power efficiency across a broad range of configurations, significantly reducing the computational burden compared to traditional CFD approaches. The results indicate that optimal configurations, characterized by larger upstream cylinder diameters, higher damping ratios, and ideal spacing ratios, can achieve power generation efficiencies of up to 59.15%. Further analysis of vorticity contours reveals that synchronized interactions between upstream vortex shedding and downstream structure motions enhance WIV, thereby improving energy harvesting efficiency.