Processing time-series of randomly forced self-oscillators: the example of beer bottle whistling

TL;DR

This paper introduces a model-based method for identifying parameters of self-oscillating systems driven by noise, demonstrated on a beer bottle whistling experiment, separating deterministic and stochastic influences.

Contribution

It develops an output-only identification approach using a Van der Pol oscillator model to analyze aeroacoustic oscillations under stochastic forcing.

Findings

The Van der Pol model effectively captures the aeroacoustic limit cycle.

The method distinguishes between deterministic growth and stochastic fluctuations.

Experimental validation confirms the approach's ability to analyze different operating conditions.

Abstract

We present a model-based, output-only parameter identification method for self-sustained oscillators forced by dynamic noise, which we illustrate experimentally with an aeroacoustic setup: a turbulent jet impinging a beer bottle and producing a whistling tone in a finite range of jet angles and velocities. Given a low-order model of the system, the identification analyzes stationary time series of a single observable: acoustic pressure fluctuations in the bottle. As this observable exhibits the characteristics of weakly non-linear self-oscillations, we choose as a minimal model the Van der Pol (VdP) oscillator: a linear acoustic oscillator (bottle) subject to stochastic forcing and non-linear deterministic forcing (turbulent jet). Although very simple, the VdP oscillator driven by random noise proves to be a sufficient phenomenological description of the aeroacoustic limit cycle for model-based linear growth rate identification. The associated stochastic amplitude equation allows us to describe the stochastic fluctuations of the acoustic amplitude resulting from a competition between (i) linear growth rate from the coherent unsteady vortex force, (ii) random forcing from the turbulence, (iii) non-linear saturation of the coherent flapping of the jet. Finally, the associated adjoint Fokker-Planck equation reveals the deterministic potential well and the stochastic forcing intensity governing these random fluctuations. Additional experiments with an external control validate the identification results. We observe that different operating conditions can yield similar statistics but different dynamics, showing that the identification method can disentangle deterministic and stochastic effects in systems forced stochastically. This output-only identification approach can be used in a wide range of phenomena exhibiting stationary self-sustained oscillations in the presence of random forcing.