Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

Ricardo Vinuesa,Oriol Lehmkuhl,Adrian Lozano-Durán,Jean Rabault

doi:10.3390/fluids7020062

Abstract

In this review, we summarize existing trends of flow control used to improve the aerodynamic efficiency of wings. We first discuss active methods to control turbulence, starting with flat-plate geometries and building towards the more complicated flow around wings. Then, we discuss active approaches to control separation, a crucial aspect towards achieving a high aerodynamic efficiency. Furthermore, we highlight methods relying on turbulence simulation, and discuss various levels of modeling. Finally, we thoroughly revise data-driven methods and their application to flow control, and focus on deep reinforcement learning (DRL). We conclude that this methodology has the potential to discover novel control strategies in complex turbulent flows of aerodynamic relevance.

Highlights

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 everyday, allowing the transportation of people and goods and the establishment of global commercial relations
Due to the major environmental and economic impacts associated with aviation, it is desirable to improve the aerodynamic performance of airplane wings, with the aim of reducing the fuel consumption and emissions associated with air travel
Machine-learning-based control methods are an exciting set of techniques that are receiving considerable attention recently for performing active flow control

Summary

Introduction

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 everyday, allowing the transportation of people and goods and the establishment of global commercial relations. In order to develop more efficient wings, it is necessary to reduce the losses associated with their movement within the surrounding fluid This implies reducing the force parallel to the incoming flow (the drag), and one of the strategies to achieve such a reduction is to perform flow control. A wide range of methods aimed at controlling the flow to reduce the drag have been reported, and some have documented net-energy savings, i.e., taking into account the energy spent on the control, as documented by Fahland et al [8] These strategies include passive methods, such as riblets [9], which are drag-reducing surfaces proven to be successful in passenger aircraft [10], and active techniques, in which the drag reduction effect is achieved through an action that requires additional energy to be transferred to the flow [11]. We will discuss different types of active control in more detail

Active Control of Turbulent Flows

Active Control of Separation

Turbulence Simulation Approaches

Data-Driven Methods for Control and Deep Reinforcement Learning

Findings

Conclusions and Outlook