Phase Synchronization in Noise Control: Advantages, Limitations, and Future Perspectives
Burak Buda Turhan, Zilei Yi, Djamel Rezgui, Mahdi Azarpeyvand
University of Bristol
内容与影响
This study provides an in-depth experimental investigation into the aeroacoustic characteristics of Distributed Electric Propulsion (DEP) systems, emphasizing the influence of relative phase angles between adjacent propellers. Phase synchronization is identified as a highly effective noise reduction strategy, leveraging destructive interference within the coherent acoustic source field generated by the propellers. Experiments were conducted at a constant propeller rotation rate of 5000 rpm across various advance ratios to examine the impact of operational conditions on noise emissions. Results reveal a clear relationship between relative phase angles and far-field noise levels, with the most significant noise attenuation observed at a phase angle of Δψ = 90°.At this configuration, reductions in the first blade passing frequency (BPF) amplitude reached up to 24 dB under inflow conditions (J > 0), compared to Δψ = 0°, highlighting the potential of phase synchronization in mitigating tonal noise. This reduction is attributed to destructive wave interference driven by the angular positioning of the propeller blades. The results confirm that phase control can be a powerful tool in reducing perceived loudness and psychoacoustic annoyance, especially under inflow conditions (J > 0). To implement and evaluate precise phase control, a LabVIEW-based dual-loop feedback controller was developed and tested on a twin-rotor system in non-axial flight. The controller maintained phase deviations within 1.5% (approximately 5°) across various tip Mach numbers and inflow velocities, demonstrating robust and consistent performance. SPL directivity measurements revealed that while Δψ = 90° effectively reduced noise in axial flight, its benefits in non-axial configurations were more localized and direction-dependent.
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计算机 / AINonlinear Dynamics and Pattern Formation
Neural Networks and Applications
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