Multi-fidelity optimization of a quiet propeller based on deep deterministic policy gradient and transfer learning

In a propeller blade optimization, both aerodynamic and aeroacoustic performance were considered simultaneously. A multi-fidelity sampling scheme was adopted by Transfer Learning (TL) to improve the overall optimization efficiency. A Deep Neural Network (DNN) was selected to map the non-linear relationship between the blade parameters and the aerodynamic/aeroacoustic performance, with the optimization being achieved by implementing a deep reinforcement learning algorithm, namely, Deep Deterministic Policy Gradient (DDPG), upon which a Multi-fidelity DNN based surrogate model (TL-MFDNN) was introduced with Transfer Learning between pre-trained and retrained processes. It was found that, by comparing the TL-MFDNN surrogate model based optimization with DDPG optimization using direct CFD simulation, the overall computing cost can be saved up to 77.3% and the optimized propeller has maximum noise reduction of up to 1.69 dB, with a negligible penalty on propulsive performance.