• Medientyp: E-Artikel
  • Titel: Fast nonlinear Froude–Krylov force calculation for prismatic floating platforms: a wave energy conversion application case
  • Beteiligte: Giorgi, Giuseppe; Sirigu, Sergej; Bonfanti, Mauro; Bracco, Giovanni; Mattiazzo, Giuliana
  • Erschienen: Springer Science and Business Media LLC, 2021
  • Erschienen in: Journal of Ocean Engineering and Marine Energy
  • Sprache: Englisch
  • DOI: 10.1007/s40722-021-00212-z
  • ISSN: 2198-6444; 2198-6452
  • Schlagwörter: Ocean Engineering ; Energy Engineering and Power Technology ; Water Science and Technology ; Renewable Energy, Sustainability and the Environment
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  • Beschreibung: <jats:title>Abstract</jats:title><jats:p>Computationally fast and accurate mathematical models are essential for effective design, optimization, and control of wave energy converters. However, the energy-maximising control strategy, essential for reaching economic viability, inevitably leads to the violation of linearising assumptions, so the common linear models become unreliable and potentially unrealistic. Partially nonlinear models based on the computation of Froude–Krylov forces with respect to the instantaneous wetted surface are promising and popular alternatives, but they are still too slow when floaters of arbitrary complexity are considered; in fact, mesh-based spatial discretisation, required by such geometries, becomes the computational bottle-neck, leading to simulations 2 orders of magnitude slower than real-time, unaffordable for extensive iterative optimizations. This paper proposes an alternative analytical approach for the subset of prismatic floating platforms, common in the wave energy field, ensuring computations 2 orders of magnitude faster than real-time, hence 4 orders of magnitude faster than state-of-the-art mesh-based approaches. The nonlinear Froude–Krylov model is used to investigate the nonlinear hydrodynamics of the floater of a pitching wave energy converter, extracting energy either from pitch or from an inertially coupled internal degree of freedom, especially highlighting the impact of state constraints, controlled/uncontrolled conditions, and impact on control parameters’ optimization, sensitivity and effectiveness.</jats:p>