Effect of Top‐Swirling Turbulence Inhibitor on Multiphase Flow in a Single‐Strand Tundish During Transient Casting

Self Cite

The oxygen lance jet plays a crucial role in the steelmaking process in a converter. The quantitative relationship between the operating parameters of the oxygen lance and the volume of the impact cavity is established for the first time, and the leading factors affecting the volume of the impact cavity are identified through correlation analysis. This article examines the influence of operating pressure and lance position on the impact cavity of a 170 t converter in a nonisothermal environment through numerical analysis. The results show that the ambient temperature in the converter presents a layered phenomenon. The temperature near the free surface ranges from 1550 to 1600 K and then gradually decreases to 900 K at the oxygen lance outlet. When the ratio of the operating pressure to the design working pressure is less than 1.0 or greater than 1.3, the cavity volume is significantly affected by the operating pressure but has less of an impact when the ratio is between 1.0 and 1.3. The cavity volume shows a linear relationship with the oxygen lance position. Moreover, the oxygen lance position has a greater influence on the cavity volume and velocity distribution than do the operating pressure.

“…The volume of fluid model is used to calculate the interface behavior between the supersonic oxygen jet and the molten metal. The continuity and momentum equations are: [26,27] 1…”

Section: Governing Equationsmentioning

confidence: 99%

“…The volume of fluid model is used to calculate the interface behavior between the supersonic oxygen jet and the molten metal. The continuity and momentum equations are: [ 26,27 ]

\frac{1}{{{\rho _i}}}[\frac{\partial }{{\partial t}}({\theta _i}{\rho _i}) &amp;#x00026;amp;amp;amp;amp;amp;amp;plus; \nabla \cdot ({\theta _i}{\rho _i}\vec v)&amp;#x00026;amp;amp;amp;amp;amp;lt;?A3B2 show $146#?&amp;#x00026;amp;amp;amp;amp;amp;gt;= \mathop \sum \nolimits_{i = 1}^a ({m_{\overrightarrow {ij} }}{m_{\overrightarrow {ji} }})]

\frac{\partial }{{\partial t}}(\rho \vec v)&amp;#x00026;amp;amp;amp;amp;amp;lt;?A3B2 show $146#?&amp;#x00026;amp;amp;amp;amp;amp;gt;&amp;#x00026;amp;amp;amp;amp;amp;amp;plus; \nabla \cdot (\rho \vec v\vec v)&amp;#x00026;amp;amp;amp;amp;amp;lt;?A3B2 show $146#?&amp;#x00026;amp;amp;amp;amp;amp;gt;&amp;#x00026;amp;amp;amp;amp;amp;amp;plus; \nabla p = \nabla \cdot [{\mu _e}(\nabla \vec v &amp;#x00026;amp;amp;amp;amp;amp;amp;plus; \nabla {\vec v^T})] &amp;#x00026;amp;amp;amp;amp;amp;amp;plus; {\rm{W}} &amp;#x00026;amp;amp;amp;amp;amp;amp;plus; \vec F

where W represents gravity,

\overset{\rightarrow}{F}

is the surface tension, and

\left(\theta\right)_{\{i}}

is the volume fraction of each phase:

\sum_{i = 1}^{2} θ_{i} = θ_{}

…”

Section: Model Descriptionsmentioning

confidence: 99%

Simulating and Evaluating the Multiphase Jet Flow Characteristics of a Five‐Hole Oxygen Lance under Nonisothermal Conditions

Wang,

Zhu,

Zhang

et al. 2024

Self Cite

“…Previous studies have demonstrated that the liquid residence time and flow pattern are critical for the flotation and removal of inclusions. [2] To obtain appropriate liquid flow behavior, numerous studies have been conducted to optimize the flow channel by changing the inner structure of the tundish, including the ladle shroud, [3,4] nozzle, [5,6] turbulence inhibitor, [7,8] dam, [4,[9][10][11][12][13][14] weirs, [10,11,15] and baffle. [16][17][18] Among these structural elements, the baffle divides the tundish into several working spaces that can reduce the liquid flow velocity and effectively reduce its dead-zone ratio of the tundish.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Molten Steel Flow Behaviors in a Multistrand Tundish via Numerical Simulation and Grey Relational Analysis with a New Dimensionless Operator

Wu,

Liu,

et al. 2023

Optimizing the tundish structure can effectively improve billet or solid product quality. The correlation between the structural parameters of the baffle and the flow characteristics of molten steel in a tundish is a prerequisite for tundish structural optimization. In this study, molten steel flow behaviors are stimulated in a five‐strand tundish using computational fluid dynamics. The behaviors are characterized by the dead‐zone ratio of the entire tundish and the flow consistency of five strands at the outlets. Further, the influences of different structural parameters on the characteristics are analyzed, and the degree of influence is evaluated using grey relational analysis (GRA). Four traditional nondimensionalization operators are used for GRA. However, the results obtained from different operators are inconsistent. Therefore, a benchmark deviation dimensionless operator is proposed to improve the accuracy of GRA. The improved grey correlation degrees are consistent with the results obtained through numerical simulation. The rank of the grey correlation degree indicates that the diameter and inclination angle of Orifice B, which is at the lower position of the baffle, are highly correlated with the dead‐zone ratio. The inclination angles should have a larger correlation degree for flow consistency than the orifice diameter.

A Computational Fluid Dynamics and Support Vector Regression Combined Method for Predicting the Flow Performance of the Five‐Strand Tundish

Wu,

Liu,

et al. 2024

To optimize the flow field of a multistrand tundish and improve the steel quality, a prediction model integrating the techniques of computational fluid dynamics (CFD) and support vector regression (SVR) is proposed. A total of 125 cases with different structure parameters, i.e., the diameter and inclination angle of the orifices, are designed. The flow field of liquid steel in the tundish is calculated by CFD, and a sample dataset is established. Then, the SVR, combining the genetic algorithm with K‐fold, is employed to train and predict the sample dataset. In the results, it is shown that the SVR model demonstrates good learning performance on the training dataset. The average absolute errors of predictions for dead volume fraction and consistency are 0.50% and 0.71%. In addition, the SVR model takes much less computational cost than the CFD method to predict the flow performance of tundish in a given baffle structure.