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Geometry optimization of a floating platform with an integrated system of wave energy converters using a genetic algorithm

Author

Listed:
  • Ekweoba, Chisom
  • El Montoya, Dan
  • Galera, Lander
  • Costa, Susana
  • Thomas, Sarah
  • Savin, Andrej
  • Temiz, Irina

Abstract

This study uses a genetic algorithm(GA) to investigate the practicality of optimizing the geometry and dimensions of a floating platform, which houses pitching wave energy converters (WEC). Using frequency-domain analysis, sensitivity tests for the search start point, choice of optimized variable, number of iterations, simulation time, and contents of the search space are made. Results show that the required number of iterations to convergence increases with an increased number of optimized variables. Furthermore, for the studied platform geometry, no single global optimum exists. Instead, various combinations of characteristic features can lead to comparable performances of the integrated wave absorber. Finally, it is observed that when the solution space is controlled and made to contain a subset of potential solutions known to improve the system performance, computation time, absorption efficiency and range are observed to improve. Additionally, the GA optimum tends towards platform geometries for which the wave absorber’s resonance response corresponds to the dominating wave climate frequencies. A key contribution of this study is the controlled manipulation of the solution space to contain a subset of potential solutions that enhance system performance. This controlled approach leads to improvements in computation time, absorption efficiency, and range of the system.

Suggested Citation

  • Ekweoba, Chisom & El Montoya, Dan & Galera, Lander & Costa, Susana & Thomas, Sarah & Savin, Andrej & Temiz, Irina, 2024. "Geometry optimization of a floating platform with an integrated system of wave energy converters using a genetic algorithm," Renewable Energy, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:renene:v:231:y:2024:i:c:s0960148124009376
    DOI: 10.1016/j.renene.2024.120869
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    References listed on IDEAS

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