Closed-loop forced heat convection control using deep reinforcement learning

Wang, Yi-Zhe; He, Xianjun; Hua, Yue; Chen, Zhihua; Wu, Wei‐Tao; Zhou, Zhifu

doi:10.1016/j.ijheatmasstransfer.2022.123655

Cited by 14 publications

(2 citation statements)

References 30 publications

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“…In these cases, also known as open-loop control problems, the policy optimization does not depend on the environment response. On the other hand, a closed-loop control of a heat transfer problem is realized by Wang et al (2023). They confirm the DRL-based control strategy, made by an oscillatory flow rate, is the best one to minimize the maximum temperature in the system.…”

Section: Introductionmentioning

confidence: 64%

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A physics-driven and machine learning-based digital twinning approach to transient thermal systems

Di Meglio,

Massarotti,

Nithiarasu

2024

HFF

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Purpose In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system. Design/methodology/approach To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop. Findings The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems. Originality/value The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.

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Section: Introductionmentioning

confidence: 64%

Section: Introductionmentioning

confidence: 99%