Evaluation of RotorWake Models for Acoustic Prediction

SM-2026-VLADA-5182

1/27/2026

Authors
Abstract
Content

This study examines the capability of medium-fidelity comprehensive analysis models to predict the acoustics for manned and unmanned rotorcraft configurations. Using the automated tool NDARC2RCAS developed at DEVCOM Army Research Laboratory, multiple configurations including a single main rotor, tilt rotor, coaxial and pusher, quadcopter, and hexacopter are evaluated at various mission segments including hover, advancing climb, and forward flight. Each configuration and condition is evaluated using a range of aerodynamic models from lower to higher fidelity, including uniform inflow, dynamic inflow, prescribed wake, free wake, and viscous vortex particle method (VVPM). These evaluations are then used with another automated tool, RCAS Acoustics, to predict noise on a Voronoi observer sphere. A comparison of the results for the single main showed good agreement between all of the aerodynamic models except VVPM. For the tilt rotor in forward flight, the higher-fidelity models produced changes in rotor loads due to the interaction with the wing. With prescribed and free wake models, this change in load is sharp and causes noise increases of up to 40 dB in front of and behind the vehicle, while the VVPM model produced a smoother change that results in a smaller, 20 dB increase in noise. The quadcopter and hexacopter show similar in-plane noise levels for all models, with alternating cancellation and amplification patterns due to rotor phasing, while out-of-plane noise is increased on the hexacopter when using the higher fidelity models.

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Pages
14
Citation
Smith, B., Floros, M., and Anusonti-Inthra, P., "Evaluation of RotorWake Models for Acoustic Prediction," Vertical Lift Aircraft Design and Aeromechanics Specialists Conference, San Jose, California, Jan 2026, San Jose, California, January 27, 2026, .
Additional Details
Publisher
Published
Jan 27
Product Code
SM-2026-VLADA-5182
Content Type
Technical Paper
Language
English