Nonlinear spatiotemporal instabilities in two-dimensional electroconvective flows

Feng, Zhe; Wan, Dongdong; Zhang, Mengqi; Wang, Bo-Fu

doi:10.1103/physrevfluids.7.023701

Cited by 2 publications

(2 citation statements)

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“…For the convectively unstable flows, the spatiotemporal stability analysis is more relevant, which accounts for the wave development in both space and time. The spatiotemporal instability of the convective KS system subjected to an impulse disturbance is conducted, following the post-processing method in Brancher & Chomaz (1997); our code adapts that used in Feng et al (2022). To extract unambiguously the amplitude and phase of a wavepacket, the Hilbert transform is applied to the perturbation…”

Section: Appendix C Stability-enhanced Reward Designmentioning

confidence: 99%

“…( b ) Relation between group velocity and the corresponding growth rate . The spatiotemporal instability of the convective KS system subjected to an impulse disturbance is conducted, following the post-processing method in Brancher & Chomaz (1997); our code adapts that used in Feng et al. (2022). To extract unambiguously the amplitude and phase of a wavepacket, the Hilbert transform is applied to the perturbation velocity as where represents the complex-valued amplitude function, and is the phase of the wavepacket.…”

Section: Figure 22mentioning

confidence: 99%

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Reinforcement-learning-based control of convectively unstable flows

Zhang

2023

J. Fluid Mech.

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This work reports the application of a model-free deep reinforcement learning (DRL) based flow control strategy to suppress perturbations evolving in the one-dimensional linearised Kuramoto–Sivashinsky (KS) equation and two-dimensional boundary layer flows. The former is commonly used to model the disturbance developing in flat-plate boundary layer flows. These flow systems are convectively unstable, being able to amplify the upstream disturbance, and are thus difficult to control. The control action is implemented through a volumetric force at a fixed position, and the control performance is evaluated by the reduction of perturbation amplitude downstream. We first demonstrate the effectiveness of the DRL-based control in the KS system subjected to a random upstream noise. The amplitude of perturbation monitored downstream is reduced significantly, and the learnt policy is shown to be robust to both measurement and external noise. One of our focuses is to place sensors optimally in the DRL control using the gradient-free particle swarm optimisation algorithm. After the optimisation process for different numbers of sensors, a specific eight-sensor placement is found to yield the best control performance. The optimised sensor placement in the KS equation is applied directly to control two-dimensional Blasius boundary layer flows, and can efficiently reduce the downstream perturbation energy. Via flow analyses, the control mechanism found by DRL is the opposition control. Besides, it is found that when the flow instability information is embedded in the reward function of DRL to penalise the instability, the control performance can be further improved in this convectively unstable flow.

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Section: Appendix C Stability-enhanced Reward Designmentioning

confidence: 99%