Chaotic forecasting of time series of heat-transfer coefficient for an evaporator with a two-phase flow

The time series of heat-transfer film coefficient for an evaporator with a vapor-liquid two-phase boiling flow were forecasted by the chaotic prediction or the local nonlinear short-term prediction. The forecasting method was based on the phase space reconstruction theory and a mathematic model in the form of datum driving had been developed to carry out the prediction. The signals of heat-transfer film coefficients predicted by this method were compared with those obtained from the experimental measurements. Different from the previous work, the comparisons in this work were done both from the point of view of the time trajectory and from the point of view of macroscopic or general characters. Besides the time-averaged statistics characteristics, such as the average value, average deviation and standard deviation, the power spectrum, phase plane map and chaotic invariants including the correlation dimension and Kolmogorov entropy had been calculated and been compared for the time series obtained both from the experimental measurement and from the forecast. The comparison for each parameter between the value calculated from the time series measured and that estimated from the time series forecasted by the chaotic forecast method shows satisfactory agreement. The limited length in time with an accuracy prediction indicates that the system is chaotic. The agreement of the comparison of the general parameter indicates that the chaotic prediction is effective for the estimations of the heat transfer characteristics of the two-phase flow boiling system and that the chaotic prediction method may be a potential tool for the effective thermo-fluid control for such evaporators.