  A Hopf bifurcation 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. In this thesis, we present a system identification (SI) framework
that exploits the noise-induced dynamics prior to a Hopf bifurcation. The
framework is novel in that it is capable of predicting the bifurcation point
and the post-bifurcation 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 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. The SI framework is tested on three different experimental
systems: a low-density jet, a flame-driven Rijke tube, and a gas-turbine
combustor. For these systems, we demonstrate that the proposed framework can
identify the 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 regime, without the need for knowledge of the location of the
bifurcation point.
