# Prediction and Control of Broadband Noise Associated with Advanced Air Mobility--A Review
## 3 Methods for Prediction of BBN
### Numerical Methods  
A mid-fidelity method has recently been investigated for BBN prediction of a rotor. [PERSON] et al. [9] compared different mid-fidelity methods to predict the noise from a DJI 9443 CF rotor. As mentioned before, the BPM BBN prediction model needs a boundary layer parameter and an effective angle of attack as inputs. To obtain these inputs, 2D and 3D RANS simulations were applied to obtain the boundary layer parameters, while the effective AOA was extracted from 3D RANS and FLIGHTLAB [58], which employed [PERSON]'s [59] finite state wake model. Figure 14a,b shows the difference of BBN prediction based on the effect of the AOA computational sources. The 3D RANS approach demonstrated higher accuracy than FLIGHTLAB at high frequencies but showed worse underprediction at low-to-mid frequencies due to laminar boundary layer vortex shedding (LBL-VS) noise. They explained that the noise mechanism associated with LBL-VS needs further study, considering the limitation of the LBL-VS noise production for ideal flow. It was worth noticing that the BBN results used 2D boundary layer parameters have better agreement with experiment, as shown in Figure 14c,d. This could be due to the fact that the boundary layer correction in the BPM method used 2D experimental data. Other mid-fidelity methods were tested for tone noise prediction that might be applied to predict BBN in the future, such as a non-linear unsteady vortex lattice method coupled with F1A [60] and a modern surface-vorticity panel solver coupled with F1A [61]. Higher fidelity requires resolving the fluctuation turbulence structure. Thus, conventional Reynolds-averaged Navier-Stokes solutions are not appropriate. An unsteady RANS (URANS) approach is the lowest computational cost, but it is unable to capture the transient flow physics and therefore less accurately predicts noise. [PERSON] et al. [62] used the ANOPP suite to predict the blade self-noise of the DJI-CF by OVERFLOW2, which  
Figure 13: SPL of observer point \(\theta\) = 127.3\({}^{\circ}\) at (**a**) \(J\) = 1.44, (**b**) \(J\) = 1.05, and (**c**) \(J\) = 0.87 (Arrows indicate the BPF) [55]. is an unsteady Reynolds-average Navier-Stokes (URANS) code. The individual source mechanisms that contribute to the total broadband noise are presented in Figure 15. The overall trends of the broadband predictions were seen to agree with the experimental data, and the result proved that the turbulence boundary layer source mechanisms were prominent BBN source for a rotor at a high frequency. However, it underpredicted at mid-frequency. The researchers explained that maybe the ineffective extraction technique caused retained mid-frequency tonal content that contaminated the extracted broadband signals from the experiment. The hybrid LES-URANS (DES) model is a competitive method for rotor simulation. It is less time-consuming compared with LES but more accurate compared with URANS. [PERSON] et al. [63] utilized a hybrid LES-unsteady RANS (DES) approach to model the  
Figure 14: SPL\({}_{1/3}\) results using (**a**) effect AOA computed from FLIGHTLAB, (**b**) effect AOA computed from 3D RANS, (**c**) \(\delta\) and \(\delta^{*}\) computed from 3D RANS, and (**d**) \(\delta\) and \(\delta^{*}\) computed from 2D RANS [9]. Figure 15: BBN source identification for DJI-CF at 6000 RPM [62]. aerodynamics of a 2-bladed 9-inch DJI propeller in hovering condition. The approach utilizes large eddy simulation (LES) away from the wall and unsteady RANS with the Spalart-Allmaras turbulence model near the walls, effectively reducing computational costs while maintaining high resolution. For far-field noise prediction, [PERSON]'s 1A Formulation was employed. As shown in Figure 16, the broadband noise (BBN) calculated from a permeable control surface closely matched the experimental data, whereas the BBN predictions from an impermeable surface (blade surface) were underpredicted, highlighting the significant contribution of the propeller wake to the BBN.