An Intelligent Method for Predicting the Pressure Coefficient Curve of Airfoil-Based Conditional Generative Adversarial Networks

• Published in: IEEE transaction on neural networks and learning systems Vol. 34; no. 7; pp. 3538 - 3552
• Main Authors: Wang, Yueqing, Deng, Liang, Wan, Yunbo, Yang, Zhigong, Yang, Wenxiang, Chen, Cheng, Zhao, Dan, Wang, Fang, Guo, Yang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerodynamic coefficient; Aerodynamic coefficients; Aerodynamics; Aircraft; Aircraft design; airfoil; Airfoils; Atmospheric modeling; Automotive components; Computational fluid dynamics; Computational modeling; Computer applications; Computing time; conditional generative adversarial network (cGAN); Data augmentation; Fluid dynamics; Generative adversarial networks; Hydrodynamics; info-generative adversarial network (GAN); Machine learning; Methods; Neural networks; pressure coefficient; Shape; Simulation
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2021.3111911

Recently, extensive studies have focused on analyzing aerodynamic performance due to its important impact on aircraft design. Most of these works compute the aerodynamic coefficient of the airfoil through computational fluid dynamics (CFD) simulation, which is too time-consuming. To reduce the computational time required, some intelligence-based methods have been presented. However, these methods also suffer from certain issues. First, most of them directly implement existing machine learning methods used to predict the aerodynamic coefficient without adding any improvements. Second, some methods convert the airfoil shape and aerodynamic curves into images, which may lead to curve distortion and the introduction of noise. Third, some methods learn the relationship between the airfoil shape and aerodynamic coefficients but ignore the influence of initial inflow conditions. Accordingly, to address these issues, we propose an intelligent method for predicting the pressure coefficients (Cp) of airfoil based on a conditional generative adversarial network (cGAN). More specifically, we first present a two-step data augmentation strategy designed to expand the original airfoil dataset. Subsequently, we design a novel cGAN-based neural network to predict the Cp curve. To the best of our knowledge, this is the first work to apply generative adversarial network (GAN) to aerodynamic coefficient prediction. Moreover, we design a new loss function to train our network. Extensive experimental results demonstrate that the Cp curve predicted by our method is very close to that generated via CFD simulation. More importantly, our method achieves a speedup close to 1000x compared with CFD simulation.