## 2 Flow Field State Detection Technology for Multiphase Sink Vortex
### Advances in the Multiphase Vortex-Induced Vibration Technology  
Many researchers have selected the vibration detection approach to explore the vortex state detection procedure because of the above advantages. The vibration sensor was originally utilized to detect the occurrence of ladle whirling slag in a Kawasaki factory in Japan in the early 1980s. The manufacturer discovered that the vortex pulled certain steel slag impurities into the nozzle case. The vibration sensor's signal was altered dramatically, and its detection accuracy needed to be enhanced [2; 37]. The diverse application situations of infrared imaging and vibration detection technologies were described by [PERSON] et al. [38]. They suggested an image detection approach based on a CCD camera and discovered that the swirling slag might be spotted when the abrupt increase in light intensity surpasses a specified limit. However, because it is a post hoc detection, it cannot identify slagging in advance. [PERSON] et al. [39] developed a flow pattern learning-based vibration signal processing approach and used a local target space alignment (LTSA) algorithm based on wavelet decomposition to analyze nonstationary vibration data. This approach extracts the major kind of liquid steel and obtains the anomalous spectral energy distribution generated by a molten steel vortex and slag inclusion using a sliding time window and an incremental LTSA algorithm. They used the vibration signal of a 60-ton ladle as an example to show that the approach can detect vortex and slag phenomena. However, the detection effect is dependent on the sliding time window's parameters. To identify the vortex state of steel flow, [PERSON] et al. [40] suggested a simulated annealing artificial neural network (SA-ANN) approach (Figure 3). The liquid steel vibration signal is used as the target signal, and the time point of slag carry-over is determined based on the amplitude difference between molten steel and steel slag. The real detection accuracy of vortex slag can reach more than 96%. [PERSON] et al. [41] employed a CCD camera and image processing technology to detect the vortex suction process, and they highlighted the limits of this approach and the vibration detection method. They found that the flow rate increased as the slag swirled, and that lots of solid slags had made their way into the lower equipment. While the vision-based detection approach can identify slag layer variation, its efficiency is limited by the computation of the cursor location with a fixed standard deviation, and detection accuracy must be increased. Figure 2: Basic principle of the vibration detection method. [PERSON] et al. [42] suggested an improved nonlinear vibration identification technique based on a wavelet packet to determine the ladle pouring state in recent years, as illustrated in Figure 4a,b. The empirical mode decomposition endpoint continuation approach is used to reduce endpoint effects during the reconstruction fitting phase. Experiments reveal that the accuracy of slag identification in common casting states may reach more than 98 percent, providing a useful benchmark for recognizing the fluid condition of unseen vessels. [PERSON] et al. [43] used a cumulative sum control graph to perform a real-time analysis of vibration acceleration data and implemented it in an integrated microcontroller. Experiments revealed that the system benefits from simplicity, low cost, mobility, and a lack of immersion. However, it can only detect a small quantity of steel slag change. [PERSON] et al. [44] analyzed the mechanism of the sensor monitoring position, the slag phase depth, and the vibration sound signal in the vortex detection process of continuous casting steel. The control technology of vibration acoustic signals is widely employed in vortex detection. It was found that noise sources could be solved, and irrelevant signals could be deleted using advanced analytic techniques. Figure 4: Vibration signal detection of steel flow. (**a**) Signal measurement point placement.