Prediction and Control of Broadband Noise Associated with Advanced Air Mobility—A Review

Abstract

This review presents an overview of advanced air mobility broadband noise (BBN) prediction and control techniques, highlighting significant advancements in various prediction models. Methods such as the semi-empirical Brooks–Pope–Marcolini (BPM) model, analytical Amiet model, and time-domain models based on the FW-H equation have been extensively studied. Machine learning (ML) shows promise in BBN prediction but requires extensive data training and application to noise source mechanisms. Passive control methods, such as leading and trailing edge serrations and blade tip designs, have been partially successful but often compromise the aerodynamic performance. Active control methods, like suction and blowing control, trim adjustments, and dielectric barrier discharge (DBD) plasma actuators, show great potential, with the latter two being particularly effective for reducing BBN in thin propeller structures. Overall, while progress has been made in understanding and predicting BBN, further research is needed to refine these methods and develop comprehensive noise control strategies. These advancements hold significant promise for effective and efficient noise mitigation in future AAM vehicles.