The identification and forecasting of chaos for natural circulation flow instabilities under rolling motion

Chaos identification and forecasting of the irregular complex flow oscillations in a two-phase natural circulation system under the rolling motion are performed. The irregular complex flow oscillation has chaotic characteristics by calculating the geometric invariants such as the correlation dimension, Kolmogorov entropy and the largest Lyapunov exponent. But the reliability of calculation result is liable to be influenced by data length and the interference of measurement noise, false judgment results may exist in the direct method. To avoid misjudgment for chaos flow oscillation, both the geometric invariants and chaos identification need to be calculated by surrogate-data method. The chaos is identified by the iterated-amplitude adjusted Fourier transform method. Chaotic forecasting for the irregular complex flow oscillation is carried out by adding weight one-rank local region method. By surrogate-data method, we can confirm that the irregular complex flow oscillation is chaotic oscillation from the deterministic system. Comparisons between the prediction results and experimental data indicate that the chaos forecasting based on adding weight one-rank local region method is an effective way for two-phase natural circulation flow instabilities, and a way of dynamical forecast to monitor flow oscillation is presented.