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Modeling wave energy converters (WECs) to accurately predict theirhydrodynamic behavior has been a challenge for the wave energy field. Often,this results in either low-fidelity, linear models that break down in energeticseas, or high-fidelity numerical models that are too computationally expensivefor operational use. To bridge this gap, we propose the use of dynamic modedecomposition (DMD) as a purely data-driven technique that generates anaccurate and computationally efficient model of an oscillating surge WEC(OSWEC). Our goal is to model and predict the behavior of the OSWEC inmonochromatic and polychromatic seas without knowledge of the governingequations or incident wave field. We generate the data for the algorithm usinga semi-analytical model and the open-source code WEC-Sim, then evaluate howwell DMD can describe past dynamics and predict future state behavior. Weconsider realistic challenges including noisy sensor measurements, nonlinearWEC dynamics, and irregular wave forcing. In each of these cases, we generateaccurate models for past and future OSWEC behavior using DMD, even with limitedsensor measurements. These findings provide insight into the use of DMD onsystems with limited time-resolved data and present a framework for applyingsimilar analysis to lab- or field-scale experiments.

Data-driven modeling of an oscillating surge wave energy converter using dynamic mode decomposition