.png){kind=icon}
返回
Multi-fidelity deep learning for aerodynamic shape optimization using convolutional neural network
.png){kind=icon}
PRE
AI
DOI：10.1063/5.0205780.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Aerodynamic shape design is essential for improving aircraft performance and efficiency. First, this study introduces a data-driven optimization framework utilizing a multi-fidelity convolutional neural network (MFCNN) for aerodynamic shape optimization. To achieve better optimization results with reduced computational cost, the framework dynamically incorporates new data in each optimization cycle. Specifically, it constantly involves the optimal solution from previous cycle as a new high-fidelity sample and employs a low-fidelity infilling strategy that maximizes the minimum Euclidean distance for selecting new low-fidelity samples. Moreover, a standard synthetic benchmark is used to elaborate the procedure of optimization and show the capability and effectiveness of the framework. Finally, the framework is applied to two aerodynamic shape optimization problems: maximizing the lift-to-drag ratio for the Royal Aircraft Establishment 2822 (RAE2822) airfoils and minimizing the cruise drag coefficient for the three-dimensional (3D) drooped and scarfed non-axisymmetric nacelles. The framework increases the lift-to-drag ratio by 51.21% over the baseline and achieves an 18.79% reduction in the cruise drag coefficient for nacelle optimization, outperforming traditional multi-fidelity deep neural network optimization framework. Sufficiently utilizing the implicit relations between different fidelity levels of data through defined local perceptual fields and convolution, our MFCNN-based optimization framework signifies a step forward in the efficiency and accuracy of aerodynamic shape optimization.
Keyword:
SURROGATE
DESIGN
MODEL
期刊
IF:
4.3
论文数:
2.9W
被引数:
8.0W
机构
引用论文
Regioselective Transfer Hydrogenative Defluorination of Polyfluoroarenes Catalyzed by Bifunctional Azairidacycle.png)
Organics
OAAI
OrganicsIF0
OAAI分享收藏Efficient aerodynamic shape optimization using variable-fidelity surrogate models and multilevel computational grids使用可变保真度代理模型和多级计算网格进行有效的空气动力学形状优化
OAAI
OAAI分享收藏
查看更多内容