The use of GANs and transfer learning in model-order reduction of turbulent wake of an isolated high-rise building

Abstract

The high cost of computational fluid dynamics (CFD) simulations has limited their use for real-time and long-term simulations. To address this limitation, reduced-order modeling has been developed, and recent advancements in artificial intelligence (AI) algorithms have led to the creation of non-intrusive reduced-order models (NIROMs). The present study proposes a framework for using various AI techniques to develop NIROMs for the turbulent wake of an isolated high-rise building. The framework is demonstrated using datasets from two different flow conditions, isothermal and unstable thermal stratification. A residual adversarial autoencoder (AAE) network with convolutional layers and Wasserstein generative adversarial network (WGAN) is first used to reduce the dimensionality of the unstable dataset. Afterward, a parallel bidirectional long short-term memory (BiLSTM) network computes its evolution through time. The proposed framework provides commendable predictions of the airflow field. The weights of the trained model are utilized to create a NIROM based on transfer learning (TL) for a smaller dataset that represents the isothermal condition. The results showed that when the training dataset is limited and the model cannot be trained on sufficient vortex shedding cycles, the proper use of TL can lead to better outcomes compared to the traditional training method (i.e., training from scratch).