Active control for enhancing vortex induced vibration of a circular cylinder based on deep reinforcement learning

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Abstract
In the current paper, the active flow control for enhancing vortex induced vibration (VIV) of a circular cylinder, which can be potentially applied in ocean energy harvesting, is achieved by an artificial neural network (ANN) trained through deep reinforcement learning (DRL). The flow past a circular cylinder with and without jet control located on the cylinder is numerically investigated using OpenFOAM, and the ANN is applied to learn an active flow control strategy through experimenting with different mass flow rates of the jets. According to our results, the jets on the cylinder are able to dramatically destabilize the periodic shedding of the cylinder wake, which leads to a much larger VIV and work capability of the cylinder. Through controlling the flow rate of the jets based on the observation of the instantaneous flow field, the ANN successfully increases the drag by 30.78%, and the magnitude of the fluctuation of the drag and lift coefficient by 785.71% and 139.62%, respectively, while the energy consumption of the jets is almost negligible. Furthermore, the net energy output by VIV with jet control increases by 357.63% (case of water) compared with the uncontrolled situation. The results demonstrate that the performance of the active jet control strategy established by DRL for enhancing VIV is outstanding and promising for realizing the transformation from the ocean energy to electrical energy. Therefore, it is encouraged to perform further investigations on VIV enhancement using active flow control based on DRL.
Funding Information
  • national key laboratory of science and technology on helicopter transmission (Grant No. HTL-O-20G06)
  • the Project funded by China Postdoctoral Science Foundation (Grant No. 2021TQ0154)
  • the Fundamental Research Funds for the Central Universities (No. 30919011401)

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