Discovering novel control strategies for turbulent wings through deep reinforcement learning

Introduction

Over the past decades, aviation has become an essential component of today's globalized world. Before the current pandemic of coronavirus disease 2019 (COVID-19), over 100,000 flights took off every day worldwide. A number of studies indicate that after the pandemic, its relevance in the transportation mix will be similar to that before COVID-19.

Environmental Impact

Aviation alone is responsible for 12% of the carbon dioxide emissions from the whole transportation sector and for 3% of the total CO2 emissions in the world. Due to the major environmental and economic impacts associated with aviation, there is a pressing need for improving the aerodynamic performance of airplane wings to reduce fuel consumption and emissions.

Need for Improvement

This implies reducing the force parallel to the incoming flow, i.e., the drag. One of the strategies to achieve such a reduction is to perform flow control.

Project Overview

DEEPCONTROL aims at using high-fidelity simulations and deep reinforcement learning to develop a framework for real-time prediction and control of the flow around wing sections and three-dimensional wings based only on sparse measurements.

Methodology

1. High-Order Simulations: We will first perform high-order spectral-element simulations of wing sections and three-dimensional wings at high Reynolds numbers.
2. Velocity Reconstruction: Using sparse measurements at the wall, we will reconstruct the velocity fluctuations above the wall within a region of interest. To this end, we will employ:
   • A generative adversarial network (GAN)
   • A fully-convolutional network (FCN)
   • Modal decomposition
3. Flow Control: Then, we will perform flow control based on deep reinforcement learning (DRL), which will enable discovering novel solutions in terms of flow actuation and design of winglet geometry.

Experimental Validation

In order to assess the robustness of the framework for real-time applications, we will carry out detailed wind-tunnel experiments at KTH.

Conclusion

This framework will constitute a breakthrough in aviation sustainability and will enable developing more efficient aeronautical solutions worldwide.