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An improved aerodynamic performance optimization method of 3-D low Reynolds number rotor blade

  • Shuyi Zhang and Bo Yang EMAIL logo
Published/Copyright: May 20, 2021
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Abstract

In this paper, an improved aerodynamic performance optimization method for 3-D low Reynolds number (Re) rotor blade is proposed. A conventional optimization procedure of blade is usually divided into three parts, such as the parameterization method, the fitness value evaluation and the optimization algorithm. This work is mainly focused on the first two parts. The parametrization method, Camber-FFD, is presented based on the camber parametrization method and the free-form deformation algorithm (FFD). The shape of 3-D blade is parameterized by the incidence angles and the coordinates of the maximum camber points. The fitness value evaluation has been realized with the help of an adaptive topological back propagation multi-layer forward artificial neural network (BP-MLFANN). During the training of BP-MLFANN, the hybrid particle swarm optimization method combined with the modified very fast simulate annealing algorithm (HPSO-MVFSA) is adopted to determine the neural network topology adaptively. To verify the effectiveness of this aerodynamic optimization method, the aerodynamic performance of a 3-D low-Re blade, such as Blade D900, is optimized, and the results are compared and analyzed based on the experiments and simulations. It is proved that this aerodynamic optimization method is feasible.


Corresponding author: Bo Yang, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China, E-mail:

Funding source: National Science and Technology Major Project of China

Award Identifier / Grant number: 2017-V-0012-0064

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This research was supported by National Science and Technology Major Project of China (2017-V-0012-0064).

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Received: 2021-04-27
Accepted: 2021-05-04
Published Online: 2021-05-20
Published in Print: 2023-08-28

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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