Artificial intelligence control of flow separation from a curved ramp

This work aims to control flow separation from a two-dimensional curved ramp. The Reynolds number examined is Reθ = 5700 based on the momentum thickness of the turbulent boundary layer right before the ramp. Three steady jets, blowing tangentially along the ramp from three spanwise slits, are deployed at the most likely flow separation position, upstream and downstream of this position, respectively. Three different control modes are investigated, i.e., a single jet, multiple jets, and genetic algorithm-optimized blowing rates of three jets. The single jet placed at the time-averaged flow separation position is found to be most effective and efficient in eliminating flow separation among the first and second control modes. However, it is the third control mode that may not only eliminate the separation bubble completely but also cut down the energy consumption, by up to 30%, compared to the single jet blowing at the flow separation position. The flow physics underlying the control modes is also discussed.

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Publication Details

Journal
Physics of Fluids
Published
2024-11-01
DOI
https://doi.org/10.1063/5.0234188
Citations
1
Primary Topic
Fluid Dynamics and Turbulent Flows
Type
article
Field-Weighted Citation Impact
0.37

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article

Artificial intelligence control of flow separation from a curved ramp

Zhi Jun Wu, Shengtai He, Yu Zhou, Ge Xu
1 citations
Physics of Fluids
Fluid Dynamics and Turbulent Flows
0.37
article

Artificial intelligence control of flow separation from a curved ramp

Zhi Jun Wu, Shengtai He, Yu Zhou, Ge Xu
article en
1 citations

Abstract

This work aims to control flow separation from a two-dimensional curved ramp. The Reynolds number examined is Reθ = 5700 based on the momentum thickness of the turbulent boundary layer right before the ramp. Three steady jets, blowing tangentially along the ramp from three spanwise slits, are deployed at the most likely flow separation position, upstream and downstream of this position, respectively. Three different control modes are investigated, i.e., a single jet, multiple jets, and genetic algorithm-optimized blowing rates of three jets. The single jet placed at the time-averaged flow separation position is found to be most effective and efficient in eliminating flow separation among the first and second control modes. However, it is the third control mode that may not only eliminate the separation bubble completely but also cut down the energy consumption, by up to 30%, compared to the single jet blowing at the flow separation position. The flow physics underlying the control modes is also discussed.

Physics of FluidsVol. 36(11)
Harbin Institute of Technology (CN), Eastern Institute of Technology, Ningbo
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 41%
Fluid Dynamics and Turbulent Flows
0.37
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