Application of neural networks to turbulence control for drag reduction

Physics of Fluids

Volumn 9, Issue 6, 1997, Pages 1740-1747

  Lee, Changhoon ^a   Kim, John ^a   Babcock, David ^b   Goodman, Rodney ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b California Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DRAG REDUCTION; FLOW CONTROL; NEURAL NETWORKS; TURBULENCE;

EID: 0030776830     PISSN: 10706631     EISSN: None     Source Type: Journal    
DOI: 10.1063/1.869290     Document Type: Article

References (13)

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2. H. Choi, P. Moin, and J. Kim, "Active turbulence control for drag reduction in wall-bounded flows," J. Fluid Mech. 262, 75 (1994).
3. R. Akhavan, W. J. Jung, and N. Mangiavacchi, "Turbulence control in wall-bounded flows by spanwise oscillations," Appl. Sci. Res. 51, 299 (1993).
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6. P. Moin and T. Bewley, "Application of control theory to turbulence," in the 12th Australasian Fluid Mechanics Conference, Sydney 10-15 December 1995 (unpublished), p. 109.
7. S. L. Jacobson and W. C. Reynolds, "Active control of boundary layer wall shear stress using self-learning neural networks," AIAA Shear Flow Conference, Orlando, 1993 (unpublished), p. 1.
8. M. Moller, "Efficient training if feed-forward neural networks," Ph.D. thesis, Aarhus University, Denmark, 1993.
9. J. Kim, P. Moin, and R. Moser, "Tubulence statistics in fully-developed channel flow at low Reynolds number," J. Fluid Mech. 177, 133 (1987).
10. B. Widrow, "Adaptive inverse control," In the Second IFAC Workshop on Adaptive Systems in Control and Signal Processing. Lund, Sweden, 1987 (unpublished), pp. 1-5.
11. C. R. Smith, J. D. A. Walker, A. H. Haidari, and U. Sobrun, "On the dynamics of near-wall turbulence," Philos. Trans. R. Soc. London Ser. A 336, 131 (1991).
12. This staggered distribution of sensors and actuators would result in actuators at only half the grid points and this might reduce the effectiveness of the control. This problem can be solved simply by increasing resolution twice so that actuators locate at every grid point of the old grid system (these correspond to half the grid points in the new grid system) still with sensors located in staggered positions. This is possible since the weight distribution does not depend on the grid distance, but depends on the grid index as shown in Figs. 5 and 7.
13. C. Liu, Y. C. Tai, J. B. Huang, and C. M. Ho, "Surface micromachined thermal shear stress sensor," in ASME Application of Microfabrication to Fluid Mechanics, Chicago, 1994 (unpublished), pp. 9-15.