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Seminar

Explaining and controlling turbulent flows through deep learning

AI for Science seminar with Ricardo Vinuesa, KTH Royal Institute of Technology.

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Overview

Zoom password: ai4science

Ricardo Vinuesa, KTH Royal Institute of Technology

The on-site event will be followed by fika in the Analysen coffee area (fika from 16:00-16:30).

Abstract:

In this presentation we first use a framework for deep-learning explainability to identify the most important Reynolds-stress (Q) events in a turbulent channel (simulated via DNS) and a turbulent boundary layer (obtained experimentally). This objective way to assess importance reveals that the most important Q events are not the ones with the highest Reynolds shear stress.

This framework is also used to identify completely new coherent structures, and we find that the most important coherent regions in the flow only have an overlap of 70% with the classical Q events.

In the second part of the presentation we use deep reinforcement learning (DRL) to discover completely new strategies of active flow control. We show that DRL applied to a blowing-and-suction scheme significantly outperforms the classical opposition control in a turbulent channel: while the former yields 30% drag reduction, the latter only 20%.

We conclude that DRL has tremendous potential for drag reduction in a wide range of complex turbulent-flow configurations.

About the speaker:

Dr. Ricardo Vinuesa is an Associate Professor at the Department of Engineering Mechanics, KTH Royal Institute of Technology in Stockholm. He is also 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, control and predict 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 TSFP Kasagi Award, the MST Emerging Leaders Award, the Goran Gustafsson Award for Young Researchers, the IIT Outstanding Young Alumnus Award, the SARES Young Researcher Award and he leads several large Horizon Europe projects. He is also a member of the Young Academy of Science of Spain.

 

Structured learning

This theme focuses on how to make use of structure in data to build machine learning (ML) and artificial intelligence (AI) systems which are safer, more trustworthy and generalize better. Structure includes the relationship between data, in time and space, and how the predictions change when data is transformed in specific ways, for example rotated or scaled. These topics are abstract and general but have a direct impact on the use of AI and ML in the sciences and in applications such as drugs and materials design, or medical imaging.