## Abstract

Wall-pressure fluctuations are a practically robust input for real-time control systems aimed at modifying wall-bounded turbulence. The scaling behaviour of the wall-pressure-velocity coupling requires investigation to properly design a controller with such input data so that it can actuate upon the desired turbulent structures. A comprehensive database from direct numerical simulations (DNS) of turbulent channel flow is used for this purpose, spanning a Reynolds-number range. Spectral analysis reveals that the streamwise velocity is most strongly coupled to the linear term of the wall pressure, at a Reynolds-number invariant distance-from-the-wall scaling of (and for the wall-normal velocity). When extending the analysis to both homogeneous directions in and, the peak coherence is centred at and for and, and and, respectively. A stronger coherence is retrieved when the quadratic term of the wall pressure is concerned, but there is only little evidence for a wall-attached-eddy type of scaling. An experimental dataset comprising simultaneous measurements of wall pressure and velocity complements the DNS-based findings at one value of k, with ample evidence that the DNS-inferred correlations can be replicated with experimental pressure data subject to significant levels of (acoustic) facility noise. It is furthermore shown that velocity-state estimations can be achieved with good accuracy by including both the linear and quadratic terms of the wall pressure. An accuracy of up to 72 % in the binary state of the streamwise velocity fluctuations in the logarithmic region is achieved; this corresponds to a correlation coefficient of 0.6. This thus demonstrates that wall-pressure sensing for velocity-state estimation - e.g. for use in real-time control of wall-bounded turbulence - has merit in terms of its realization at a range of Reynolds numbers.

Original language | English |
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Article number | A15 |

Number of pages | 36 |

Journal | Journal of Fluid Mechanics |

Volume | 981 |

DOIs | |

Publication status | Published - 2024 |