General framework for unsteady aerodynamic prediction of airfoils based on deep transfer learning

Abstract

Analyzing the unsteady aerodynamic performance of airfoils under dynamic stall using computational fluid dynamics (CFD) is computationally intensive. Although deep learning models can quickly predict aerodynamic parameters, their generalization capability on a small-scale dataset is often poor. This paper presents a novel deep transfer learning (TL) framework that combines model-based and instance-based transfer methods, termed synergistic instance-model TL. This framework facilitates rapid predictions of unsteady aerodynamic performance for various airfoils and pitch oscillations from the small-scale dataset. The framework integrates the accelerated training speed of model-based methods with the dynamic dataset expansion benefits of instance-based approaches. Initially, a pre-trained Wasserstein-deep convolutional generative adversarial network (W-DCGAN) is developed, combining a convolutional neural network with a generative adversarial network to predict aerodynamic hysteresis loops for the SC1095 airfoil in the source domain. The framework then fine-tunes the pre-trained model and incorporates weighted source domain dataset into the small-scale target domain dataset, producing the transferred model W-DCGAN-TL. This approach significantly reduces prediction inaccuracies compared to model-based and non-TL methods when applied to the small-scale dataset. The framework's flexibility allows the use of pre-trained models and datasets from related aerodynamic problems to address issues with insufficient data. Consequently, it is expected to reduce the dependency on extensive datasets, enhance design efficiency, and minimize resource requirements.