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Hydro-acoustic optimization of propellers: A review of design methods

  • Adrian Portillo-Juan*
  • , Simone Saettone
  • , Poul Andersen
  • , Esteban Ferrer
  • *Corresponding author for this work
  • Universidad Politécnica de Madrid
  • Bound4blue

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

In recent years, the escalation of anthropogenic noise within marine environments has become a pressing ecological concern. Despite the proliferation of guidelines aimed at mitigating this issue, their adoption remains non-compulsory. The International Maritime Organization (IMO) advocates for noise reduction through the refinement of propeller design and heightened research to curtail acoustic emissions, ensuring that such measures do not compromise propulsive efficiency. This review delves into the array of strategies devised to attenuate noise pollution. It scrutinizes the methodologies employed to evaluate the hydroacoustic attributes of propellers, including experimental and computational approaches. Furthermore, it summarizes the techniques employed in the multiobjective optimization of propellers, which involves balancing the maximization of efficiency against the minimization of noise emission. The findings highlight the necessity for a comprehensive, interdisciplinary approach to the hydroacoustic optimization of marine propellers, encompassing cavitation experiments and advanced numerical simulations. In the field of propeller optimization, the traditional reliance on genetic algorithms is giving way to the adoption of neural networks and surrogate models derived from computational fluid dynamics data, particularly when addressing complex, nonlinear phenomena such as hydro-acoustics and cavitation.
Original languageEnglish
Article number104158
JournalApplied Ocean Research
Volume151
Number of pages25
ISSN0141-1187
DOIs
Publication statusPublished - 2024

Keywords

  • Ffowcs-Williams and Hawkings
  • Hydro-acoustics
  • Neural networks
  • Optimisation
  • Propeller noise
  • Transfer function

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