An empirical mean-field model of symmetry-breaking in a turbulent wake

TL;DR

This paper develops a low-dimensional stochastic model of symmetry-breaking in a turbulent wake, using a parametric modal basis that captures nonlinear interactions and mean flow deformation from experimental data.

Contribution

It introduces a novel parametric modal basis dependent on the unsteady center of pressure, enabling better modeling of nonlinear symmetry-breaking in turbulent wakes.

Findings

Model reproduces empirical power spectra.

Model captures probability distributions of mode coefficients.

Parametric basis improves nonlinear interaction representation.

Abstract

This work develops a low-dimensional nonlinear stochastic model of symmetry-breaking coherent structures from experimental measurements of a turbulent axisymmetric bluff body wake. Traditional model reduction methods decompose the field into a set of modes with fixed spatial support but time-varying amplitudes. However, this fixed basis cannot resolve the mean flow deformation due to variable Reynolds stresses, a central feature of Stuart's nonlinear stability mechanism, without the further assumption of weakly nonlinear interactions. Here, we introduce a parametric modal basis that depends on the instantaneous value of the unsteady aerodynamic center of pressure, which quantifies the degree to which the rotational symmetry of the wake is broken. Thus, the modes naturally interpolate between the unstable symmetric state and the nonlinear equilibrium. We estimate the modes from experimental measurements of the base pressure distribution by reducing the symmetry via phase alignment and averaging conditioned on the center of pressure. The amplitude dependence of the symmetric mode deviates significantly from the polynomial scaling predicted by weakly nonlinear analysis, confirming that the parametric basis is crucial for capturing the effect of strongly nonlinear interactions. We also introduce a second model term capturing axisymmetric fluctuations associated with the mean-field deformation. We then apply the Langevin regression system identification method to construct a stochastically forced nonlinear model for these two generalized mode coefficients. The resulting model reproduces empirical power spectra and probability distributions, suggesting a path towards developing interpretable low-dimensional models of globally unstable turbulent flows from experimental measurements.