### Abstract

A new adaptive controller based on a neural network was constructed and applied to turbulent channel flow for drag reduction. A simple control network, which employs blowing and suction at the wall based only on the wall-shear stresses in the spanwise direction, was shown to reduce the skin friction by as much as 20% in direct numerical simulations of a low-Reynolds number turbulent channel flow. Also, a stable pattern was observed in the distribution of weights associated with the neural network. This allowed us to derive a simple control scheme that produced the same amount of drag reduction. This simple control scheme generates optimum wall blowing and suction proportional to a local sum of the wall-shear stress in the spanwise direction. The distribution of corresponding weights is simple and localized, thus making real implementation relatively easy. Turbulence characteristics and relevant practical issues are also discussed.

Original language | English |
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Pages (from-to) | 1740-1747 |

Number of pages | 8 |

Journal | Physics of Fluids |

Volume | 9 |

Issue number | 6 |

DOIs | |

Publication status | Published - 1997 Jun |

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### All Science Journal Classification (ASJC) codes

- Computational Mechanics
- Condensed Matter Physics
- Mechanics of Materials
- Mechanical Engineering
- Fluid Flow and Transfer Processes

### Cite this

*Physics of Fluids*,

*9*(6), 1740-1747. https://doi.org/10.1063/1.869290

}

*Physics of Fluids*, vol. 9, no. 6, pp. 1740-1747. https://doi.org/10.1063/1.869290

**Application of neural networks to turbulence control for drag reduction.** / Lee, Changhoon; Kim, John; Babcock, David; Goodman, Rodney.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Application of neural networks to turbulence control for drag reduction

AU - Lee, Changhoon

AU - Kim, John

AU - Babcock, David

AU - Goodman, Rodney

PY - 1997/6

Y1 - 1997/6

N2 - A new adaptive controller based on a neural network was constructed and applied to turbulent channel flow for drag reduction. A simple control network, which employs blowing and suction at the wall based only on the wall-shear stresses in the spanwise direction, was shown to reduce the skin friction by as much as 20% in direct numerical simulations of a low-Reynolds number turbulent channel flow. Also, a stable pattern was observed in the distribution of weights associated with the neural network. This allowed us to derive a simple control scheme that produced the same amount of drag reduction. This simple control scheme generates optimum wall blowing and suction proportional to a local sum of the wall-shear stress in the spanwise direction. The distribution of corresponding weights is simple and localized, thus making real implementation relatively easy. Turbulence characteristics and relevant practical issues are also discussed.

AB - A new adaptive controller based on a neural network was constructed and applied to turbulent channel flow for drag reduction. A simple control network, which employs blowing and suction at the wall based only on the wall-shear stresses in the spanwise direction, was shown to reduce the skin friction by as much as 20% in direct numerical simulations of a low-Reynolds number turbulent channel flow. Also, a stable pattern was observed in the distribution of weights associated with the neural network. This allowed us to derive a simple control scheme that produced the same amount of drag reduction. This simple control scheme generates optimum wall blowing and suction proportional to a local sum of the wall-shear stress in the spanwise direction. The distribution of corresponding weights is simple and localized, thus making real implementation relatively easy. Turbulence characteristics and relevant practical issues are also discussed.

UR - http://www.scopus.com/inward/record.url?scp=0030776830&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030776830&partnerID=8YFLogxK

U2 - 10.1063/1.869290

DO - 10.1063/1.869290

M3 - Article

AN - SCOPUS:0030776830

VL - 9

SP - 1740

EP - 1747

JO - Physics of Fluids

JF - Physics of Fluids

SN - 1070-6631

IS - 6

ER -