Browse by author
Lookup NU author(s): Dr Xiaonan Chen, Mike Diessner, Professor Kevin WilsonORCiD, Dr Richard Whalley
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
© TSFP 2024. All rights reserved. In this study, low-amplitude wall blowing was employed to reduce the skin-friction drag in zero-pressure-gradient turbulent boundary layers in a wind tunnel. In the first part of the experiment, the global skin-friction drag distribution was measured using a hot-wire at several streamwise positions in the wind tunnel. Generally, the reduction effect of global skin-friction drag is closely linked to the control parameters of wall blowing, including blowing amplitude, frequency, angle, duty cycle, and wavelengths in both the streamwise and spanwise directions. Thus, achieving optimal control strategies to attain the maximum global drag reduction across different Reynolds numbers is equivalent to finding an optimal solution for a complex N-dimensional problem hidden within a black box. In the second part of the experiment, a Bayesian optimization framework (NUBO, Newcastle University Bayesian Optimization) was used to optimize blowing amplitude for maximal local drag reduction. NUBO progressively identified the optimal control strategy to achieve the maximum local drag reduction across various freestream velocities, showing the potential of using this kind of machine learning approach for refined designs in turbulent boundary layer control.
Author(s): Chen X, Diessner M, Wilson KJ, Whalley RD
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 13th International Symposium on Turbulence and Shear Flow Phenomena (TSFP13)
Year of Conference: 2024
Online publication date: 28/06/2024
Acceptance date: 02/04/2018
URL: http://www.tsfp-conference.org/proceedings/2023/47.pdf
