A Hopf bifurcation, where a fixed-point solution loses stability and a limit cycle is born, is prevalent in many nonlinear dynamical systems. When a system prior to a Hopf bifurcation is exposed to a sufficient level of noise, its noise-induced dynamics can provide valuable information about the impending bifurcation and the post-bifurcation dynamics. In this thesis, we present a system identification (SI) framework that exploits the noise-induced dynamics prior to a supercritical or subcritical Hopf bifurcation. The framework is novel in that it is capable of predicting the bifurcation point and the post-bifurcation (limit-cycle) dynamics using only pre-bifurcation data. Specifically, we present two different versions of the framework: input-output and output-only. For the inpu...[ Read more ]

A Hopf bifurcation, where a fixed-point solution loses stability and a limit cycle is born, is prevalent in many nonlinear dynamical systems. When a system prior to a Hopf bifurcation is exposed to a sufficient level of noise, its noise-induced dynamics can provide valuable information about the impending bifurcation and the post-bifurcation dynamics. In this thesis, we present a system identification (SI) framework that exploits the noise-induced dynamics prior to a supercritical or subcritical Hopf bifurcation. The framework is novel in that it is capable of predicting the bifurcation point and the post-bifurcation (limit-cycle) dynamics using only pre-bifurcation data. Specifically, we present two different versions of the framework: input-output and output-only. For the input-output version, the system is forced with additive noise generated by an external actuator, and its response is measured. For the output-only version, the intrinsic noise of the system acts as the noise source, so no external actuator is required, and only the output signal is measured. In both versions, the Fokker–Planck equations, which describe the probability density function of the fluctuation amplitude, are derived from self-excited oscillator models. Then, the coefficients of these models are extracted from the experimental probability density functions characterizing the noise-induced response in the fixed-point regime, prior to the Hopf point itself. These two versions of the SI framework are tested on three different experimental systems: a hydrodynamic system (a low-density jet), a laminar thermoacoustic system (a flame-driven Rijke tube), and a turbulent thermoacoustic system (a gas-turbine combustor). For these systems, we demonstrate that the proposed framework can identify the super/subcritical nature of the Hopf bifurcation and the system’s order of nonlinearity. Moreover, by extrapolating the identified model coefficients, we are able to forecast the locations of the bifurcation points and the limit-cycle features after those points. To the best of our knowledge, this is the first time that SI has been performed using data from only the pre-bifurcation (fixed-point) regime, without the need for a priori knowledge of the location of the bifurcation point. Given that such noise-induced dynamics are universal near a Hopf bifurcation, the proposed SI framework should be applicable to a variety of nonlinear dynamical systems in nature and engineering.