Controlling Rayleigh–Bénard convection via reinforcement learning. Issue 9 (2nd October 2020)

• Record Type: Journal Article
• Authors: Beintema, Gerben; Corbetta, Alessandro; Biferale, Luca; Toschi, Federico
• Is Part Of: Journal of turbulence. Volume 21:Issue 9/10(2020)
• Page Start: 585
• Page End: 605
• Publication Date: 2020-10-02
• Subjects: Reinforcement learning -- Thermal convection -- Rayleigh–Bénard -- Control -- Chaos; Turbulence -- Periodicals; Turbulence -- Périodiques; 532.0527
• DOI: 10.1080/14685248.2020.1797059
• Languages: English
• ISSNs: 1468-5248

Abstract

Abstract : Thermal convection is ubiquitous in nature as well as in many industrial applications. The identification of effective control strategies to, e.g. suppress or enhance the convective heat exchange under fixed external thermal gradients is an outstanding fundamental and technological issue. In this work, we explore a novel approach, based on a state-of-the-art Reinforcement Learning (RL) algorithm, which is capable of significantly reducing the heat transport in a two-dimensional Rayleigh–Bénard system by applying small temperature fluctuations to the lower boundary of the system. By using numerical simulations, we show that our RL-based control is able to stabilise the conductive regime and bring the onset of convection up to a Rayleigh number R a c ≈ 3 ⋅ 10 4, whereas state-of-the-art linear controllers have Ra c ≈ 10 4 . Additionally, for Ra > 3 ⋅ 10 4, our approach outperforms other state-of-the-art control algorithms reducing the heat flux by a factor of about 2.5. In the last part of the manuscript, we address theoretical limits connected to controlling an unstable and chaotic dynamics as the one considered here. We show that controllability is hindered by observability and/or capabilities of actuating actions, which can be quantified in terms of characteristic time delays. When these delays become comparable with the Lyapunov time of the system, control becomes impossible.