Deep Reinforcement Learning for the Heat Transfer Control of Pulsating
  Impinging Jets
Sajad Salavatidezfouli Giovanni Stabile Gianluigi Rozza
This research study explores the applicability of Deep Reinforcement Learning
(DRL) for thermal control based on Computational Fluid Dynamics. To accomplish
that, the forced convection on a hot plate prone to a pulsating cooling jet
with variable velocity has been investigated. We begin with evaluating the
efficiency and viability of a vanilla Deep Q-Network (DQN) method for thermal
control. Subsequently, a comprehensive comparison between different variants of
DRL is conducted. Soft Double and Duel DQN achieved better thermal control
performance among all the variants due to their efficient learning and action
prioritization capabilities. Results demonstrate that the soft Double DQN
outperforms the hard Double DQN. Moreover, soft Double and Duel can maintain
the temperature in the desired threshold for more than 98% of the control
cycle. These findings demonstrate the promising potential of DRL in effectively
addressing thermal control systems.