AUTHOR=Schmekel D. , Alcántara-Ávila F. , Hoyas S. , Vinuesa R. TITLE=Predicting Coherent Turbulent Structures via Deep Learning JOURNAL=Frontiers in Physics VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2022.888832 DOI=10.3389/fphy.2022.888832 ISSN=2296-424X ABSTRACT=

Turbulent flow is widespread in many applications, such as airplane wings or turbine blades. Such flow is highly chaotic and impossible to predict far into the future. Some regions exhibit a coherent physical behavior in turbulent flow, satisfying specific properties; these regions are denoted as coherent structures. This work considers structures connected with the Reynolds stresses, which are essential quantities for modeling and understanding turbulent flows. Deep-learning techniques have recently had promising results for modeling turbulence, and here we investigate their capabilities for modeling coherent structures. We use data from a direct numerical simulation (DNS) of a turbulent channel flow to train a convolutional neural network (CNN) and predict the number and volume of the coherent structures in the channel over time. Overall, the performance of the CNN model is very good, with a satisfactory agreement between the predicted geometrical properties of the structures and those of the reference DNS data.