Dynamic chaos of imaging measurements for characterizing gas-liquid nonlinear flow behaviour in a metallurgical reactor stirred by top-blown air

A modern chaos detection strategy for imaging measurements is proposed in this work to quantify the thermal mixing state quality in a metallurgical reactor stirred by top-blown air. Specifically, the improved C-C algorithm is proposed to reconstruct the phase space of the nonlinear time series of the bubble characteristics under thermal conditions. Moreover, a chaos decision tree algorithm is introduced to extract the chaotic mixing characteristics of the thermal two-phase mixing system for the first time. Experimental and calculated results show that all the possible mixing states of the mixing system are visualized by reconstructing the phase space with the help of the visualization technique of the thermal gas-liquid two-phase flow. It is found that the attractor of the nonlinear time series of bubbles exhibited more serious variations while the chosen working condition was optimal. Furthermore, the obtained parameter represents the chaotic characteristics of the thermal gas-liquid two-phase mixing system which has chaotic characteristics under various experimental conditions. Hence, the new strategy would be helpful and effective in beneficial exploration for understanding the nonlinear intensification mechanism of metallurgical thermal processes.