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SUMMARY:Wall turbulence drag reduction in the era of machine learning--Fro
 m optimized distributed actuation to automated reduced-order modeling - Be
 rnd Noack (Harbin Institute of Technology\, Technische Universität Berlin
 )
DTSTART:20220329T123000Z
DTEND:20220329T130000Z
UID:TALK171161@talks.cam.ac.uk
DESCRIPTION:The control of wall turbulence is of paramount engineering imp
 ortance. About 20% of the world energy is consumed by maritime\, ground an
 d airborne transport. Wall turbulence is a significant contribution to the
  parasitic drag of ships\, trains and airplaines as well as the main resis
 tance in pipe flows with oil\, gas\, water and other fluids. This talk foc
 uses on machine-learned optimization and modeling of wall turbulence drag 
 reduction with distributed actuation. First\,&nbsp\; 31% skin friction red
 uction of a turbulent boundary layer is achieved by traveling surface defo
 rmation in large eddy simulations. The actuation parameters are optimized 
 with a machine learned self-similar response model and accurately predicte
 d far outside the training data (Fernex et al. 2020 Phys. Rev. Fluids). Se
 cond\, these simulations are modeled using a cluster-based network model (
 Fernex et al. 2021 Sci. Adv.). These data-driven reduced-order models have
  distinct advantages over POD models\,like robustness\, human interpretabi
 lity\, and automated development. Third\,&nbsp\; first experimental result
 s of a self-learning smart skin separation control over a smooth ramp are 
 presented. The feedback laws of two-dimensional multi-modal actuator/senso
 r arrays are optimized with gradient-enriched machine learning control (Co
 rnejo Maceda et al. 2021 J. Fluid Mech). The talk concludes with &nbsp\;pe
 rspectives of future developments.\nCo-authors: Songqi Li\,&nbsp\; Jiayang
  Luo\,&nbsp\; Guy Cornejo Maceda\, Nan Gao (HIT\, China)\;Daniel Fernex (E
 PFL) Richard Semaan (TU Braunschweig)\, Marian Albers\, Wolfgang Schroeder
  (RWTH Aachen University)
LOCATION:Seminar Room 1\, Newton Institute
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