Noise Prediction and Mitigation for UAS and eVTOL Aircraft: A Survey

Abstract

Highlights: What are the main findings? This survey identifies and discusses recent advances in noise prediction tools, empirical measurements, and mitigation strategies for UAS and eVTOL aircraft, integrating technical solutions with public perception studies and regulatory frameworks. The review provides a comparative analysis of prediction and mitigation approaches, highlighting their modeling fidelity, operational applicability, and limitations across different environmental and urban scenarios. A taxonomy of noise prediction and mitigation approaches is developed. What is the implication of the main finding? The results emphasize that effective UAM noise management requires an integrated approach that combines accurate prediction models, mitigation strategies, regulatory adaptation, and proactive community engagement. These insights can guide vehicle designers, urban planners, and policymakers in creating noise-aware airspace corridors, developing certification standards, and enhancing public acceptance for UAM deployment. The integration of small unmanned aircraft systems (sUASs) and electric vertical takeoff and landing (eVTOL) aircraft into urban airspace presents a new challenge in managing environmental noise, which is a critical factor for the public acceptance of urban air mobility (UAM). This survey investigates the noise characteristics of UAS and eVTOL platforms, particularly multi-rotor and distributed propulsion configurations, and examines whether the operational benefits of these vehicles outweigh their acoustic footprint in dense urban environments. While eVTOLs are often perceived as quieter than conventional helicopters due to the absence of combustion engines and mechanically simpler drivetrains, their dominant noise sources are aerodynamic in nature. These include blade vortex interactions, rotor loading noise, and broadband noise, which persist regardless of whether propulsion is electric or combustion-based. Recent studies suggest that community perception of drone noise is influenced more by tonal content, frequency, and modulation patterns than by absolute sound pressure levels. This paper presents a comprehensive review of state-of-the-art noise prediction tools, empirical measurement techniques, and mitigation strategies for sUAS operating in UAM scenarios. The discussion provided in this paper assists in vehicle design, certification standards, airspace planning, and regulatory frameworks focused on minimizing noise impact in urban settings.