# Review of Control Technologies for Quiet Operations of Advanced Air-Mobility
## 5 Discussion  
Several passive control methods are reviewed, from leading edge trip, trailing edge serrations, leading edge notch, effects of blade counts, and ducts. These methods showed some successes in mitigating the noise of the blades, but ultimately affects the performance of the rotors. Several works tackled this problem by optimizing the design process with performance constraints [32; 34]. These still have some trade-offs that may work as intended but may result in difficulty in manufacturing or may present some structural difficulties. To overcome the physical modifications, we looked at several active control methods. Zone cancellation was found to be infeasible for open rotors as it would require a high acoustic impedance to counter the noise source for every observer location [41]. For on-blade source manipulations, taking some notes from several decades of active BVI noise control in legacy helicopters, the active twist methodology [48] showed improvements in the overall noise reduction in the higher frequencies, but were detrimental at the lower frequencies. Perhaps this approach can be applied to specific stages of the flight envelope, for example, during approach/landing phase, where blade-wake interactions are highest. The pulsed tip jets method [43] showed some acoustic benefits but required cumbersome jet configurations and equipment, which imposes a weight penalty on the vehicle. The winglets and trailing edge flaps also showed good acoustic benefits but may add a structural penalty to the rotor blades. As a possible remedy, an on-blade actuation using an embedded speaker system may require less structural loads and weight. The techniques discussed have been applied to single rotor blades, thus there is a need to investigate their effectiveness in multi-bladed and multi-rotor applications as in the case of [PERSON] & Mankbadi [55] Additionally,  
Figure 14: Contour plots of acoustic pressure showing (**a**): No control, and (**b**): With control for a zone of observers [55]. Figure 13: (**a**) Optimized Loading Solution, \(L_{r}\), for each blade; (**b**) Total Noise at target observer, \(\psi_{obs}=0^{\text{o}}\). there is a need for a high-fidelity analysis of the above methods and their effectiveness in more realistic conditions. Overall, a great deal of research has been performed on understanding propeller and rotor noise sources, as well as predicting them accurately. Research on the control of these noise sources has been ongoing, but with allusions to helicopter rotors, they show great promise for total noise control with the eventual hope to apply the methods to the emergence of urban air mobility vehicles. _Closed Loop ANC_: The actual radiated sound is expected to differ from that obtained by the simplified methodologies discussed above. Therefore, a feedback loop is needed to measure/estimate the radiated sound and decide on adjusting the actuator signal accordingly. Similar work is done by [PERSON] et al. [48], but targets the rotor floor or region below the rotor. This may be useful, again during descent or highly impulsive vehicles (helicopters). For typical AAM vehicles, the BVI phenomena is not as pronounced and thus the in-plane thickness noise may be more prominent especially on approach. With this, we cannot apply this method of feedback control. We can measure some of the near-field parameters, such as the fluctuating pressure, as a surrogate parameter for the far field noise. However, to develop a closed-loop controller, a Reduced-Order Model (ROM) may be useful. _Anti-Noise and Machine learning (ML)_: When applying the anti-noise principle to AAM it becomes too complicated. First, because the radiated signal is composed of tonal and broadband noise over a range of frequencies, and secondly, because we want to cancel or reduce the noise in more than one point.