2024
DOI: 10.1063/5.0194264
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A novel framework for predicting active flow control by combining deep reinforcement learning and masked deep neural network

Yangwei Liu,
Feitong Wang,
Shihang Zhao
et al.

Abstract: Active flow control (AFC) through deep reinforcement learning (DRL) is computationally demanding. To address this, a masked deep neural network (MDNN), aiming to replace the computational fluid dynamics (CFD) environment, is developed to predict unsteady flow fields under the influence of arbitrary object motion. Then, a novel DRL-MDNN framework that combines the MDNN-based environment with the DRL algorithm is proposed. To validate the reliability of the framework, a blind test in a pulsating baffle system is… Show more

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Cited by 13 publications
(1 citation statement)
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“…[44]. With the rapid development of computer resources, the computational fluid dynamics (CFD) technique was rapidly developed and has been widely used to study aerodynamic and aeroelastic problems [45,46]. The choice of the turbulence model plays a pivotal role in the Reynolds-averaged Navier-Stokes equations (RANS) method within engineering fields [47], and it represents a notable challenge in the aerodynamics of compressors [48], particularly concerning the analysis of tip leakage flows [49].…”
Section: Numerical Setups 231 Experimental Configurationsmentioning
confidence: 99%
“…[44]. With the rapid development of computer resources, the computational fluid dynamics (CFD) technique was rapidly developed and has been widely used to study aerodynamic and aeroelastic problems [45,46]. The choice of the turbulence model plays a pivotal role in the Reynolds-averaged Navier-Stokes equations (RANS) method within engineering fields [47], and it represents a notable challenge in the aerodynamics of compressors [48], particularly concerning the analysis of tip leakage flows [49].…”
Section: Numerical Setups 231 Experimental Configurationsmentioning
confidence: 99%