A transformer-based synthetic-inflow generator for spatially-developing turbulent boundary layers

TL;DR

This paper introduces a transformer-based deep learning model combined with a super-resolution GAN to generate realistic turbulent inflow conditions for boundary layer simulations, demonstrating high accuracy and efficiency across different Reynolds numbers.

Contribution

The study presents a novel transformer and GAN-based approach for synthetic turbulent inflow generation, showing improved accuracy and computational efficiency over existing methods.

Findings

Accurately predicts instantaneous velocity fields and turbulence statistics.

Demonstrates effectiveness across unseen Reynolds numbers.

Shows transformer-based models can efficiently simulate turbulent flow dynamics.

Abstract

This study proposes a newly-developed deep-learning-based method to generate turbulent inflow conditions for spatially-developing turbulent boundary layer (TBL) simulations. A combination of a transformer and a multiscale-enhanced super-resolution generative adversarial network is utilized to predict velocity fields of a spatially-developing TBL at various planes normal to the streamwise direction. Datasets of direct numerical simulation (DNS) of flat plate flow spanning a momentum thickness-based Reynolds number, Re_theta = 661.5 - 1502.0, are used to train and test the model. The model shows a remarkable ability to predict the instantaneous velocity fields with detailed fluctuations and reproduce the turbulence statistics as well as spatial and temporal spectra with commendable accuracy as compared with the DNS results. The proposed model also exhibits a reasonable accuracy for predicting velocity fields at Reynolds numbers that are not used in the training process. With the aid of transfer learning, the computational cost of the proposed model is considered to be effectively low. The results demonstrate, for the first time that transformer-based models can be efficient in predicting the dynamics of turbulent flows. It also shows that combining these models with generative adversarial networks-based models can be useful in tackling various turbulence-related problems, including the development of efficient synthetic-turbulent inflow generators.