# Prediction and Control of Broadband Noise Associated with Advanced Air Mobility--A Review
## 6 Discussion  
This review presents an overview of advanced air mobility broadband noise (BBN) prediction and control techniques. Significant advancements have been highlighted in BBN prediction models, applicable across low- to high-fidelity methods. One of the widely used BBN prediction models is the semi-empirical BPM model [4], which considers blade self-noise and requires boundary layer and effective angle parameters. To enhance accuracy, [PERSON] and [PERSON] [10] introduced the blade-wake interaction (BWI) noise to the BPM model; however, it cannot predict turbulence ingestion noise (TIN). The analytical model by [PERSON] and [PERSON] [23] considers only the trailing-edge noise and requires force loading data. To address TIN, [PERSON] [7] proposed a model assuming TIN as the leading-edge noise, derived using a similar approach to the trailing-edge noise model. The time-domain model based on the Flowcs Williams and Hawkings (FW-H) equation [25] can calculate all BBN categories, offering a comprehensive approach. Recently, there have been some advancements, such as [PERSON] et al. [24], F1B [27], F2B [28], [PERSON] and [PERSON] [50], [PERSON] et al. [47], and [PERSON] et al. [49], but we still need further validation and improvement. Empirical models are rarely used due to their limitations and reliance on experimental data rather than underlying mechanisms, such as the new empirical model by [PERSON] and [PERSON] [33]. However, empirical models can be beneficial for optimization of these design studies. All the prediction models are related to the aerodynamic model. The primary limitation for predicting rotor noise is the fidelity of the aerodynamic modeling blade element theory, which has demonstrated superior capabilities for predicting blade loads and, with appropriate corrections, can accurately predict the steady thrust and torque of the blades. More recent methods, such as the surface-vorticity panel solver [61] and non-linear vortex lattice methods [60], have been shown to predict unsteady loading but cannot predict separated flows, although they are computationally efficient compared to the more expensive URANS, DES, and LES approaches. Additionally, the lattice Boltzmann method (LBM) has shown potential in propeller simulations. However, it exhibits lower accuracy and certain limitations compared to the Navier-Stokes solver. Among these methods, it is accepted that only the more costly approaches, such as DES or LES combined with the FW-H equation, can accurately predict broadband noise. To improve the performance and accuracy of lower fidelity methods, studies such as those by [PERSON] et al. [50], [PERSON] and [PERSON] [53], and [PERSON] et al. [54] have integrated the blade element momentum theory (BEMT) with [PERSON]'s model, UCD-QuietFly, and empirical models, respectively. These approaches have demonstrated a good degree of accuracy in predicting blade self-noise, though they fall short in capturing lower frequency noise. Several low-fidelity turbulence ingestion studies have been conducted by [PERSON] et al. [55] and [PERSON] and [PERSON] [57]. They utilized the Sears function for unsteady loading coupled with [PERSON]'s [7] model, reporting that [PERSON]'s theory can accurately predict turbulence ingestion noise from open rotors. A review of various experimental and numerical studies reveals that broadband noise originates from both blade self-noise and wake effects, with blade self-noise being dominant at higher frequencies. While blade-wake interaction (BWI) noise may be negligible during hovering, it cannot be ignored during edgewise flight. The haystacking phenomenon at the blade-passing frequency becomes apparent when turbulence is ingested. In multi-rotor systems, studies on forward flight have suggested that the increase in broadband noise is primarily due to rotor-wake interaction effects. Machine learning (ML) demonstrates significant potential for predicting broadband noise. However, current applications are equation-based and rely on extensive data training.