Efficient Aerodynamic and Aeroacustic Optimization of Propeller Sections Using Bayesian Methods

Due to various design variables, like the blade shape and operating conditions, the optimization of propellers is a complex task. The aerodynamic and aeroacoustic design optimization is conducted by using an efficient machine learning optimization approach, the Bayesian optimization. On the one hand, this allows for a fast and efficient optimization process. On the other hand, the knowledge about the optimization problem is extend and the influence of the individual design variables can be identified. Exemplarily, the radial airfoil sections of the propeller blade are optimized regarding the glide ratio and the aeroacoustic footprint.