A generative deep learning framework for airfoil flow field prediction with sparse data

• Published in: Chinese journal of aeronautics Vol. 35; no. 1; pp. 470 - 484
• Main Authors: WU, Haizhou, LIU, Xuejun, AN, Wei, LYU, Hongqiang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2022; State Key Laboratory of Aerodynamics,Mianyang 621000,China; Key Laboratory of Aerodynamic Noise Control,Mianyang 621000,China; MHIT Key Laboratory of Pattern Analysis and Machine Intelligence,College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China; Collaborative Innovation Center of Novel Software Technology and Industrialization,Nanjing 210023,China%MHIT Key Laboratory of Pattern Analysis and Machine Intelligence,College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China; Collaborative Innovation Center of Novel Software Technology and Industrialization,Nanjing 210023,China%College of Aerospace Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
• Subjects: CFD; Flow field; Generative adversarial networks (GANs); Sparse data; Supercritical airfoil; CFD; Sparse data; Flow field; Supercritical airfoil; Generative adversarial networks (GANs); Generative adversarial net-works(GANs)
• ISSN: 1000-9361; 2588-9230
• DOI: 10.1016/j.cja.2021.02.012

Deep learning has been probed for the airfoil performance prediction in recent years. Compared with the expensive CFD simulations and wind tunnel experiments, deep learning models can be leveraged to somewhat mitigate such expenses with proper means. Nevertheless, effective training of the data-driven models in deep learning severely hinges on the data in diversity and quantity. In this paper, we present a novel data augmented Generative Adversarial Network (GAN), daGAN, for rapid and accurate flow filed prediction, allowing the adaption to the task with sparse data. The presented approach consists of two modules, pre-training module and fine-tuning module. The pre-training module utilizes a conditional GAN (cGAN) to preliminarily estimate the distribution of the training data. In the fine-tuning module, we propose a novel adversarial architecture with two generators one of which fulfils a promising data augmentation operation, so that the complement data is adequately incorporated to boost the generalization of the model. We use numerical simulation data to verify the generalization of daGAN on airfoils and flow conditions with sparse training data. The results show that daGAN is a promising tool for rapid and accurate evaluation of detailed flow field without the requirement for big training data.