# Prediction and Control of Broadband Noise Associated with Advanced Air Mobility--A Review
## 7 Conclusions and Future Work  
A comprehensive overview of BBN prediction and control techniques relevant to AAM has been conducted. Notable progress has been made in BBN prediction models, with approaches ranging from semi-empirical models, like the BPM model, to more sophisticated time-domain models based on the FW-H equation. The fidelity of aerodynamic models plays a crucial role in the accuracy of rotor noise predictions. Low- and mid-fidelity models can quickly predict mid- to high-frequency BBN, while high-fidelity methods like DES and LES offer greater accuracy, especially when combined with the FW-H equation. Despite these advancements, challenges persist, particularly the computational expense of high-fidelity simulations, the difficulty in accurately predicting TIN, and the limitations of low- and mid-fidelity models in capturing low-frequency noise.  
Experimental studies have consistently shown that blade self-noise remains the dominant source of BBN, particularly at higher frequencies. However, interactions such as BWI become increasingly significant during edgewise flight and in multi-rotor systems. For TIN, the BBN noise increases substantially, and the haystacking phenomenon has been observed.  
Both passive and active control methods have been reviewed for their effectiveness in mitigating BBN. While passive methods like serrations and blade shape modifications can reduce noise, they often involve trade-offs in aerodynamic performance. Active control methods, such as suction and blowing, have demonstrated considerable success in reducing noise for airfoils and turbine fans but face significant challenges when applied to propellers. To address these challenges, novel approaches like trim control and dielectric barrier discharge (DBD) plasma actuators have shown great promise.  
In conclusion, while substantial progress has been made in BBN prediction and control, ongoing research and development are essential to overcome existing limitations and fully realize the potential of these techniques in real-world AAM applications. Future work should focus on refining low-fidelity methods to improve their ability to capture low-frequency BBN, balancing the trade-offs in high-fidelity simulations, and expanding the analytical model to include other self-noise mechanisms. Additionally, more accurate unsteady models and wavenumber spectra should be developed for analytical TIN models. Further development is needed for semi-empirical and empirical models to better predict TIN. New noise control methods, particularly for TIN, should be explored, and the applicability of machine learning should be extended to enhance noise prediction and reduction strategies. Moreover, more complex turbulence environments should be studied in noise experiments to better understand real-world scenarios.  
Conceptualization, R.R.M. and J.H.; methodology, J.H.; writing--original draft preparation, J.H.; writing--review and editing, R.R.M. All authors have read and agreed to the published version of the manuscript.  
Authors were funded by [NASA Aeronautics Research Mission Directorate (ARMD) University Leadership Initiative (ULI)] grant number [80 NSSC21M0057].  
This research is based upon work supported by the NASA Aeronautics Research Mission Directorate (ARMD) University Leadership Initiative (ULI) under cooperative agreement number 80 NSSC21M0057. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Aeronautics and Space Administration.  
The authors declare no conflict of interest.