• Energy Research
• 2025-2025close

Modeling wave energy converters (WECs) to accurately predict their hydrodynamic behavior has been a challenge for the wave energy field. Often, this results in either low-fidelity linear models or high-fidelity numerical models that are too computationally expensive for operational use. To bridge this gap, we propose the use of dynamic mode decomposition (DMD) as a purely data-driven technique that can generate an accurate and computationally efficient model of WEC dynamics. Specifically, we model and predict the behavior of an oscillating surge wave energy converter (OSWEC) in mono- and polychromatic seas without an equation of motion or knowledge of the incident wave field. We generate data with the open-source code WEC-Sim, then evaluate how well DMD can describe past dynamics, and predict future behavior. We consider realistic challenges including noisy sensors, nonlinear dynamics, and irregular wave forcing. Specifically, by using an extension of DMD, we reduce the effect of noise on our system and significantly increase model accuracy outside the training region. Additionally, by introducing time delays, we accurately describe weakly nonlinear dynamics, even though DMD is a linear algorithm. Finally, we use Optimized DMD (optDMD) to model OSWEC behavior in response to irregular waves. While optDMD accurately models training data, future prediction is inaccurate, demonstrating the limits of modeling efforts without access to information about the incident wave field. These findings provide insight into the use of DMD, and its extensions, on systems with limited time-resolved data and present a framework for applying similar analysis to lab- or field-scale experiments.

Cross-flow turbine (known as vertical-axis wind turbines or “VAWTs” in wind) blades encounter a relatively undisturbed inflow for the first half of each rotational cycle (“upstream sweep”) and then pass through their own wake for the latter half (“downstream sweep”). While most research on cross-flow turbine optimization focuses on the power-generating upstream sweep, we use single-bladed turbine experiments to show that the downstream sweep strongly affects time-averaged performance. We find that power generation from the upstream sweep continues to increase beyond the optimal tip-speed ratio. In contrast, the downstream sweep consumes power beyond the optimal tip-speed ratio due to unfavorable lift and drag directions relative to rotation and a potentially detrimental pitching moment arising from rotation-induced virtual camber. Downstream power degradation increases faster than upstream power generation, such that downstream sweep performance determines the optimal tip-speed ratio. In addition to performance measurements, particle image velocimetry data are obtained inside the turbine swept area at three tip-speed ratios. This illuminates the mechanisms underpinning the observed performance degradation in the downstream sweep and motivates an analytical model for a limiting case with high induction. Performance results are shown to be consistent across 55 unique combinations of chord-to-radius ratio, preset pitch angle, and Reynolds number, underscoring the general significance of the downstream sweep.