Reduced hierarchy equations of motion approach with Drude plus Brownian spectral distribution: Probing electron transfer processes by means of two-dimensional correlation spectroscopy J. Chem. Phys. 137, 22A550 (2012) Rectified Brownian transport in corrugated channels: Fractional Brownian motion and Lévy flights J. Chem. Phys. 137, 174101 (2012) Brownian dynamics simulations with hard-body interactions: Spherical particles J. Chem. Phys. 137, 164108 (2012) Noncolliding Brownian motion with drift and time-dependent Stieltjes-Wigert determinantal point process J. Math. Phys. 53, 103305 (2012) Non-Markovian reduced dynamics based upon a hierarchical effective-mode representation J. Chem. Phys. 137, 144107 (2012) Additional information on Chaos We characterize complexities in combustion instability in a lean premixed gas-turbine model combustor by nonlinear time series analysis to evaluate permutation entropy, fractal dimensions, and short-term predictability. The dynamic behavior in combustion instability near lean blowout exhibits a self-affine structure and is ascribed to fractional Brownian motion. It undergoes chaos by the onset of combustion oscillations with slow amplitude modulation. Our results indicate that nonlinear time series analysis is capable of characterizing complexities in combustion instability close to lean blowout. An understanding of the physical process underlying combustion instability leading to lean blowout is of current interest in modern combustion physics as well as in nonlinear science. In this study, the characterization of complexities in combustion instability in a lean premixed gas-turbine combustor, which is of fundamental and practical importance for combustion systems, has been carried out from the viewpoint of nonlinear dynamics, focusing on characterizing the dynamic behavior of combustion instability close to lean blowout. The use of nonlinear time series analysis involving permutation entropy in combination with a surrogate data method, multifractal analysis, and nonlinear forecasting based on a radial basis function network allows us to capture the signature of self-affine structures and chaotic oscillations in combustion instability. Our results have not been reported in previous papers on combustion phenomena, in particular, thermoacoustic combustion oscillations.