New methods of characterizing spatio-temporal patterns in laboratory experiments

Lyapunov dimension;Thermal convection;Pattern formation;Patter characterization techniques;Principal component analysis;Fourier analysis;Curvature;Image characterization techniques

Kurtuldu, Huseyin ; Physics

University:Georgia Institute of Technology

Department:Physics

关键词: Lyapunov dimension;    Thermal convection;    Pattern formation;    Patter characterization techniques;    Principal component analysis;    Fourier analysis;    Curvature;    Image characterization techniques;   

Others  :  https://smartech.gatech.edu/bitstream/1853/37121/1/Kurtuldu_Huseyin_201012_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Complex patterns arise in many extended nonlinear nonequilibrium systems in physics, chemistry and biology. Information extraction from thesecomplex patterns is a challenge and has been a main subject of research for many years. We study patterns in Rayleigh-Benard convection (RBC) acquired from our laboratory experiments to develop new characterization techniques for complex spatio-temporal patterns. Computational homology, a new topological characterization technique, is applied to the experimental data to investigate dynamics by quantifying convective patterns in a unique way. The homology analysis is used to detect symmetry breakings between hot and cold flows as a function of thermaldriving in experiments, where other conventional techniques, e.g., curvature and wave-number distribution, failed to reveal this asymmetry.Furthermore, quantitative information is acquired from the outputs of homology to identify different spatio-temporal states. We use this information to obtain a reduced dynamical description of spatio-temporal chaos to investigate extensivity and physical boundary effects in RBC. The results fromhomological analysis are also compared to other dimensionality reduction techniques such as Karhunen-Loeve decomposition and Fourier analysis.