Experimental jet control with Bayesian optimization and persistent data topology

doi:10.1063/5.0217519

Experimental jet control with Bayesian optimization and persistent data topology

2024-09-24

0

OA

AI

J

Johann Moritz Reumschüssel

Y

Yiqing Li

P

Philipp Maximilian zur Nedden

Christian Oliver Paschereit *

DOI：10.1063/5.0217519

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Abstract

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This study experimentally optimizes the mixing of a turbulent jet at Reynolds number 10 000 with the surrounding air by targeted shear layer actuation. The forcing is composed of superposed harmonic signals of different azimuthal wavenumber m generated by eight loudspeakers circumferentially distributed around the nozzle lip. Amplitudes and frequencies of the individual harmonic contributions serve as optimization parameters, and the time-averaged centerline velocity downstream of the potential core is used as a metric for mixing optimization. The actuation is optimized through Bayesian optimization. Three search spaces are explored-axisymmetric forcing, m = 0, superposed axisymmetric and helical forcing, m is an element of{0,1}, and axisymmetric actuation combined with two counter-rotating helical modes, m is an element of{-1,0,1}. High-speed particle image velocimetry (PIV) is employed to analyze the jet response to the optimized forcing. The optimization processes are analyzed by persistent data topology. In the search space of axisymmetric excitation, the routine identifies an actuation at the natural frequency of the flow to be most efficient, with the centerline velocity being decreased by 15%. The optimal solutions in both the two-mode and three-mode search space converge to a similar forcing with one axial and one helical mode combined at a frequency ratio of around 2.3. Spectral analysis of the PIV images reveals that for the identified optimal forcing frequencies, a non-linear interaction between forced and natural structures in the jet flow is triggered, leading to a reduction in centerline velocity of around 35%. The topology of the most complex search space from the discrete data reveals four basins of attractions, classified into three forcing patterns including axisymmetric, axisymmetric-helical, and axisymmetric-flapping. Two deep basins are related to the optimal pattern found as axisymmetric-helical, and the others are shallower.

Keywords:

AXISYMMETRICAL JET

INSTABILITIES

DISTURBANCES

EVOLUTION

MODE

Experimental jet control with Bayesian optimization and persistent data topology

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