A Nonlinear Sensitivity Solver for Aerodynamic Shape Optimization and Rapid Design Space Exploration

2011 ◽  
Author(s):  
Travis Drayna ◽  
Graham Candler ◽  
Heath Johnson
Keyword(s):  
Shape Optimization ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Aerodynamic Shape Optimization ◽  
Aerodynamic Shape ◽  
Rapid Design

scholarly journals A GAN-based dimensionality reduction technique for aerodynamic shape optimization

2021 ◽  
Author(s):  
Jun Zhang ◽  
Wenzheng Wang ◽  
Qiuyu Wu ◽  
Liwei Hu
Keyword(s):  
Dimensionality Reduction ◽  
Shape Optimization ◽  
Design Space ◽  
Reduction Technique ◽  
Aerodynamic Shape Optimization ◽  
Generative Adversarial Network ◽  
Aerodynamic Shape ◽  
Adversarial Network ◽  
Dimensionality Reduction Technique ◽  
Dynamics Simulations

Abstract Aerodynamic shape optimization (ASO) based on computational fluid dynamics simulations is extremely computationally demanding because a search needs to be performed in a high-dimensional design space. One solution to this problem is to reduce the dimensionality of the design space for aircraft optimization. Hence, in this study, a dimensionality reduction technique is designed based on a generative adversarial network (GAN) to facilitate ASO. The novel GAN model is developed by combining the GAN with airfoil curve parameterization and can directly produce realistic and highly accurate airfoil curves from input data of aerodynamic shapes. In addition, the respective interpretable characteristic airfoil variables can be obtained by extracting latent codes with physical meaning, while reducing the dimensionality of the airfoil design space. The results of simulation experiments show that the proposed technique can significantly improve the optimization convergence rate of the ASO process.

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