Guest Lecturer Via Zoom July 4, 2023, Check Program for Exact Timing
Dr. Ricardo Vinuesa
Department of Engineering Mechanics
KTH Royal Institute of Technology,
Stockholm
Dr. Ricardo Vinuesa is an Associate Professor at the Department of Engineering Mechanics, at KTH Royal Institute of Technology in Stockholm. He is also Vice Director of the KTH Digitalization Platform and Lead Faculty at the KTH Climate Action Centre. He studied Mechanical Engineering at the Polytechnic University of Valencia (Spain), and he received his PhD in Mechanical and Aerospace Engineering from the Illinois Institute of Technology in Chicago. His research combines numerical simulations and data-driven methods to understand and model complex wall-bounded turbulent flows, such as the boundary layers developing around wings and urban environments. Dr. Vinuesa has received, among others, an ERC Consolidator Grant, the Göran Gustafsson Award for Young Researchers and he is the PI of several EU-funded projects.
Sensing and Control of Turbulent Flows through Deep Learning
The advent of new powerful deep neural networks (DNNs) has fostered their application in a wide range of research areas, including more recently in fluid mechanics. In this presentation, we will cover some of the fundamentals of deep learning applied to computational fluid dynamics (CFD). Furthermore, we explore the capabilities of DNNs to perform various predictions in turbulent flows: we will use convolutional neural networks (CNNs) for non-intrusive sensing, i.e. to predict the flow in a turbulent open channel based on quantities measured at the wall. We show that it is possible to obtain very good flow predictions, outperforming traditional linear models, and we showcase the potential of transfer learning between friction Reynolds numbers of 180 and 550. We also discuss other modeling methods based on auto-encoders (AEs) and generative adversarial networks (GANs), and we present results of deep-reinforcement-learning-based flow control.
