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<p>The paper describes activities currently run in the frame of the MOTUS project. Thanks to source separation (filtering of the main rotor, anti-torque and residual noise contributions to the total helicopter noise), the developed methodology allows to correct an acoustic measurement database from simulation and/or experimental results at contributor level to model the noise emission of a new helicopter candidate design, accounting for the introduction of low-noise technology bricks (e.g. rotational speed modification, alternate main rotor blade or Fenestron™ anti-torque design, engine noise control treatment…). The resulting hemisphere database can then be used in an in-house sound propagation tool to simulate various use-cases, from predicting certification noise levels to computing noise footprints based on realistic scenarios, including also Low Noise Procedures, taking into account available demographical and/or topographical data. All these results can then be auralized in a subsequent step to address advanced metrics related to noise annoyance. </p>

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A comprehensive helicopter acoustic modeling tool based on simulation and experiment

  • 期刊论文

  • 发表日期：

    2025年7月21日

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  • Frédéric Guntzer,
  • Julien Caillet,
  • Charles Cariou,
  • Jean-Paul Pinacho,
  • Pierre Dieumegard,
  • Enric Roca León

摘要

The paper describes activities currently run in the frame of the MOTUS project. Thanks to source separation (filtering of the main rotor, anti-torque and residual noise contributions to the total helicopter noise), the developed methodology allows to correct an acoustic measurement database from simulation and/or experimental results at contributor level to model the noise emission of a new helicopter candidate design, accounting for the introduction of low-noise technology bricks (e.g. rotational speed modification, alternate main rotor blade or Fenestron™ anti-torque design, engine noise control treatment…). The resulting hemisphere database can then be used in an in-house sound propagation tool to simulate various use-cases, from predicting certification noise levels to computing noise footprints based on realistic scenarios, including also Low Noise Procedures, taking into account available demographical and/or topographical data. All these results can then be auralized in a subsequent step to address advanced metrics related to noise annoyance.