采用知识迁移加速的智能气动设计优化方法

郭振东; 李存晰; 宋立明; 李军; 丰镇平

doi:10.7652/xjtuxb202310006

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采用知识迁移加速的智能气动设计优化方法

—————————————————————— | 更新时间：2025-05-08

- 采用知识迁移加速的智能气动设计优化方法
- Knowledge Transfer-Accelerated Intelligent Aerodynamic Design Optimization
- 西安交通大学学报 2023年57卷第10期页码：53-63
- 作者机构：
  
  西安交通大学能源与动力工程学院,西安,710049
- 作者简介：
  
  : 2023-01-20。作者简介: 郭振东(1990—),男,助理教授
  宋立明(通信作者),男,教授,博士生导师。基金项目: 国家科技重大专项资助项目(2019-Ⅱ-0008-0028)
- 基金信息：
  
  : 2023-01-20。作者简介: 郭振东(1990—),男,助理教授;宋立明(通信作者),男,教授,博士生导师。基金项目: 国家科技重大专项资助项目(2019-Ⅱ-0008-0028);秦创原引用高层次创新创业人才项目(QCYRCXM-2022-210)。
- DOI：10.7652/xjtuxb202310006
  中图分类号： TK421.4
- 网络首发：2023-10-10，
  
  纸质出版：2023
- 稿件说明：
移动端阅览
郭振东, 李存晰, 宋立明, 等. 采用知识迁移加速的智能气动设计优化方法[J]. 西安交通大学学报, 2023,57(10):53-63.

Knowledge Transfer-Accelerated Intelligent Aerodynamic Design Optimization[J]. 2023, 57(10): 53-63.
郭振东, 李存晰, 宋立明, 等. 采用知识迁移加速的智能气动设计优化方法[J]. 西安交通大学学报, 2023,57(10):53-63. DOI： 10.7652/xjtuxb202310006.

Knowledge Transfer-Accelerated Intelligent Aerodynamic Design Optimization[J]. 2023, 57(10): 53-63. DOI： 10.7652/xjtuxb202310006.

摘要

为缩短精细气动形状设计优化所需的最少性能评估次数与任务周期内所能容许的最大性能评估次数之间的差距

基于机器学习领域迁移学习理念

开展了采用知识迁移加速的智能气动设计优化方法研究。首先

搭建了翼型变分自编码器模型

利用其解码器实现了气动形状的智能参数化

同时借助其编码器将已完成任务样本统一至目标任务参数化空间; 其次结合单保真度和多保真度代理模型

建立了贝叶斯迁移优化算法; 然后

将翼型变分自编码器模型与贝叶斯迁移优化算法相结合

搭建了智能气动形状迁移优化框架; 最后

通过任务相关性分析对知识迁移加速优化过程的机理进行了讨论。研究结果表明:通过开展翼型设计优化

智能气动形状迁移优化框架所获得的最优解中位数相较于无知识迁移的变分自编码器优化方法

性能提升了4.8%

比其他各参比方法提升了19.9%以上

验证了该知识迁移策略的有效性。

Abstract

To address the gap between the minimum number of evaluations required for refined aerodynamic shape optimization tasks and the maximum number of evaluations constrained by the mission period

a study on intelligent aerodynamic design optimization accelerated by knowledge transfer was conducted based on the concept of transfer learning in the field of machine learning. Firstly

an airfoil variational autoencoder(VAE)model was built. The model's decoder enabled the intelligent parameterization of aerodynamic shapes

while its encoder unified samples from source tasks into the parameterization space of the target task. Secondly

the Bayesian transfer optimization algorithm was established

incorporating both single and multi-fidelity surrogate models. Then

an intelligent transfer optimization framework for aerodynamic shapes was established by integrating the airfoil VAE model and Bayesian transfer optimization algorithm. Last

task correlation analysis was made to discuss the mechanism of the optimization process accelerated by knowledge transfer. The results from airfoil optimization test demonstrate that such a framework improves the performance of median of the optimal solution by 4.8% compared to using VAE optimization lack of knowledge transfer

and by more than 19.9% compared to other methods

confirming the effectiveness of knowledge transfer strategy.

关键词

Keywords

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