# Review of Control Technologies for Quiet Operations of Advanced Air-Mobility
## 3 Multi-Disciplinary Optimization in Aeroacoustics
### Noise Reduction within Optimization Framework  
Once again, a lot of research has been done with the focus of MDO for rotors and propellers with an acoustic constraint. [PERSON] et al. [30] discussed an optimization method of coupling unsteady aerodynamic and structural dynamic models to account for the aero-elastic effects of the propeller. They utilize the 6-bladed propeller of the Piaggio P.180 Avanti aircraft (see Figure 5, Planform view of single blade, from root to tip) with an angular velocity of 188.5 rad/s. The aerodynamic parameters of surface pressures were generated using a \"full potential\" CFD model with a turbulent boundary layer model based on the \"defect formulation theory\". This was loosely coupled with a computational structural dynamics code (CSD). The blade loads information, as well as the boundary layer parameters were then used as input for tonal and broadband noise predictions using the Ffowcs Williams Hawkings equations [31] for tonal content, and [PERSON]'s equations [30] for turbulent boundary layer broadband noise. They applied this method to propellers in aircraft pusher configurations, with a performance constraint of shaft power availability of 633.8 kW for take-off condition, as well as an acoustic minimization constraint. Because much of the noise at such high rotational rates stems from the tip speeds, the optimal solution was obtained by modifying the tip geometries as seen in Figure 5. They realized an overall sound pressure level reduction of 3.5 dB at 45\({}^{\circ}\), 20 m away from the propeller axis at the take-off condition with the optimized blade geometry.  
Similar work was done by [PERSON] et al. [32] in which they utilized a Multi-Objective Differential Evolution (MODE) technique coupled with a genetic algorithm to optimize propeller blades for transonic flow. By allowing the algorithm to vary geometric variables such as airfoil chord, camber line, thickness ratio, and twist, generations of solutions were obtained. High-fidelity RANS simulations were performed and Ffowcs-Williams Hawkings equations [33] were utilized for noise computations at 4 Rotor radii away. A BPF noise reduction of 5.2 dB was obtained for \"individual B\" configuration (see Figure 6d), which was postulated to be a result of the aerodynamic loads shifting from the tips to the optimized hump inward of the blades.  
Figure 5: Comparison between reference blade (dashed line), and optimized blade (solid line) [31]. Reprinted with permission from [PERSON] (2010).  
[PERSON] et al. [34] designed an airfoil for low drag and low tonal noise using the genetic algorithm. They utilized an airfoil representative of a helicopter in forward flight, at three flight conditions summarized in Figure 7. The optimization constraints were to minimize form drag, and the acoustic signature in the form of OASPL at observer locations 50 m from the hub. They utilized XFOIL [35] for aerodynamics and WOPWOP [36] for aeroacoustics computations. The resulting optimized solution provided rather unconventional airfoil shapes that had about 25% improvement in the acoustic fitness function (OASPL) while maintaining a good aerodynamic performance compared to the NACA 0012 airfoil.  
More recently [PERSON] et al. [37] utilized a gradient-based propeller optimization with the focus on urban air mobility vehicle. Their work combined a blade element momentum theory tool \"OpenBEMT\" with an acoustic prediction tool \"ANOPP2\" within an optimization framework called OpenMDAO. They were able to achieve a 5 dB reduction in OASPL at a cost of 1% propeller efficiency.  
Figure 6: Optimization of blade geometry with acoustic constraint [32]. (**a**) Optimization Design Variables. (**b**) Workflow of Optimization Framework. (**c**) Noise Directivity of Optimized Blade Geometries. (**d**) Optimized Blade Geometries. Reprinted with permission from [PERSON], [PERSON] (2011).