Potential of color schlieren velocimetry for high-temperature pollutant emission sources and flow characteristics of induced airflow during high-temperature liquid pouring

Abstract

Accurate velocity distribution of high-temperature pollutant sources is difficult to measure by traditional velocimetry, while seedless velocimetry by schlieren shows great promise. In the present study, a color schlieren velocimetry (CSV) is proposed and its reliability is discussed. Then, three post-processing algorithms for schlieren image velocimetry are contrastively analyzed in terms of accuracy, velocity fluctuation, and time consumption, including the eddy recognition algorithm (ERA) of CSV, optical flow algorithm (OFA), and cross-correlation algorithm (CA) of traditional velocimetry. Finally, a case study on the flow characteristics of an induced airflow under various air–liquid temperature differences during high-temperature liquid pouring was performed by CSV with the ERA. The results show an average variation of 15% for velocities obtained by CSV and particle image velocimetry, which is 20% higher than those obtained by traditional schlieren velocimetry. Among the three algorithms, the ERA and OFA showed the highest velocity accuracy and lowest velocity fluctuation, respectively. Moreover, the run time of the CA was the shortest. Using CSV with the ERA, the flow evolution and velocity variations of an induced airflow were found to be related to the flow status of the pouring liquid column. Relatively large vortices appeared mainly in the upper-left region above the liquid column where polluted airflow needs to be controlled. In summary, CSV is recommended for high-temperature low-velocity airflow considering accuracy and time consumption, and the detailed flow parameters of the induced airflow in this study will be helpful for the design of a refined ventilation system.