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Multi-fidelity optimization of a quiet propeller based on deep deterministic policy gradient and transfer learning.
- Source :
-
Aerospace Science & Technology . Jun2023, Vol. 137, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PROPELLERS
*REINFORCEMENT learning
*MACHINE learning
*NOISE control
Subjects
Details
- Language :
- English
- ISSN :
- 12709638
- Volume :
- 137
- Database :
- Academic Search Index
- Journal :
- Aerospace Science & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 163145639
- Full Text :
- https://doi.org/10.1016/j.ast.2023.108288