Knowledge Transfer Based Aerodynamic Optimization Method for Efficient Turbine Cascade Design

doi:10.3901/JME.2025.04.333

Abstract

Abstract: For the efficient aerodynamic shape optimization(ASO) of turbine blades, inspired by the concept of knowledge transfer from artificial intelligence, research on knowledge transfer based intelligent aerodynamic shape optimization methods for turbine blades is conducted. Firstly, the variational autoencoder(VAE) model is built, with its decoder to realize the intelligent parameterization of turbine blades. Secondly, for the improvement of the reparameterization accuracy of samples from related completed tasks, a sample-weighted model retraining strategy is proposed for encoding samples to a common design space. Besides, a gradient-based reparameterization algorithm is also designed. Moreover, the Bayesian transfer optimization algorithm is constructed based on multi-fidelity surrogates to take full advantage of transfer source samples and accelerate the optimization progress of target tasks. Eventually, an intelligent transfer ASO framework for turbine blades is established. Transfer optimization for low pressure turbine blade of GE-E³ is carried out using the proposed framework. With the same computational cost, the total pressure loss coefficient of the optimal blade is reduced by 0.66% compared to benchmark design, and 0.18% lower than legacy approach without knowledge transfer strategy. With reaching the same level of optimized performance, the computational cost of the optimization with proposed framework can be reduced by at least 50% compared to legacy approach. Thereby, the effectiveness of proposed knowledge transfer accelerated ASO framework for turbine blades is well demonstrated.

Key words: knowledge transfer, Bayesian optimization, multi-fidelity surrogate, turbine aerodynamic shape optimization

CLC Number:

TK14

LI Cunxi, SONG Liming, GUO Zhendong, LI Jun. Knowledge Transfer Based Aerodynamic Optimization Method for Efficient Turbine Cascade Design[J]. Journal of Mechanical Engineering, 2025, 61(4): 333-343.

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