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Detecting parametric resonance in a floating oscillating water column device for wave energy conversion: Numerical simulations and validation with physical model tests

Author

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  • Giorgi, Giuseppe
  • Gomes, Rui P.F.
  • Henriques, João C.C.
  • Gato, Luís M.C.
  • Bracco, Giovanni
  • Mattiazzo, Giuliana

Abstract

The wave energy sector has faced enormous technological improvements over the last five decades, however, due to the complexity of the hydrodynamic processes, current numerical models still have limitations in predicting relevant phenomena. In particular, floating spar-type wave energy converters are prone to large undesirable roll and pitch amplitudes caused by a dynamic instability induced by parametric resonance. Detecting this phenomenon accurately is essential as it impacts drastically on power extraction, structural loads and mooring forces. This paper presents the validation of results from a numerical model, capable of detecting parametric resonance, using experimental data. Experiments were carried out for a scaled model of the Spar-buoy OWC (Oscillating Water Column) device at a large ocean basin. The buoy uses a slack-mooring system attached to the basin floor. The scaled turbine damping effect is simulated by a calibrated orifice plate. Two different buoy draft configurations are considered to analyse the effect of different mass distributions. The numerical model considers the nonlinear Froude-Krylov forces, which allows it to capture complex hydrodynamic phenomena associated with the six-degree-of-freedom motion of the buoy. The mooring system is simulated through a quasi-static inelastic line model. Real fluid effects are accounted for through drag forces based on the Morison’s equation and determined from experimental data. The comparison of results from regular-wave tests shows good agreement, including when parametric resonance is detected. Numerical results show that parametric resonance can produce a negative impact on power extraction efficiency up to 53%.

Suggested Citation

  • Giorgi, Giuseppe & Gomes, Rui P.F. & Henriques, João C.C. & Gato, Luís M.C. & Bracco, Giovanni & Mattiazzo, Giuliana, 2020. "Detecting parametric resonance in a floating oscillating water column device for wave energy conversion: Numerical simulations and validation with physical model tests," Applied Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:appene:v:276:y:2020:i:c:s0306261920309338
    DOI: 10.1016/j.apenergy.2020.115421
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    References listed on IDEAS

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    5. Seyed Milad Mousavi & Majid Ghasemi & Mahsa Dehghan Manshadi & Amir Mosavi, 2021. "Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory," Mathematics, MDPI, vol. 9(8), pages 1-16, April.

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