Application of neural networks to turbulence control for drag reduction

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  • Changhoon Lee
    Department of Mechanical and Aerospace Engineering, University of California at Los Angeles, Los Angeles, California 90095-1597
  • John Kim
    Department of Mechanical and Aerospace Engineering, University of California at Los Angeles, Los Angeles, California 90095-1597
  • David Babcock
    Department of Electrical Engineering, California Institute of Technology, Pasadena, California 91125
  • Rodney Goodman
    Department of Electrical Engineering, California Institute of Technology, Pasadena, California 91125

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<jats:p>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.</jats:p>

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