Bubbles generation under turbulent conditions at the steel making ladle shroud. A water modeling and CFD study
Bibliographic record
Abstract
The mechanism of bubble generation under turbulent conditions within a steelmaking ladle shroud was studied experimentally by water modelling, and mathematically by CFD (Computational Fluid Dynamics) simulation. The sizes of bubbles generated were quantitatively characterized through optical measurements. Based on the bubble size distributions, six potentially important experimental parameters were assessed. The optimal operating condition for the generation of small bubbles in the steelmaking ladle shroud was proposed, based on combining the experimental data and CFD results. To directly produce super clean steel, one key task for the steelmaking industry is to remove tiny inclusions less than 50μm as far as possible. However, due to their low rising velocity, these tiny inclusions remain in the liquid steel entering into the casting processes. Small gas bubbles (0.25mm to 3.0mm) could enhance the removal efficiency of small inclusions from the liquid steel. Due to the high surface tension of liquid steel, and the non-wetting characteristics, traditional gas injection techniques, such as lances, porous plug, orifices can only produce gas bubbles in the range of 10-50mm diameters under conditions applying to liquid steel. Nonetheless, the technique of micro-bubble generation in the ladle shroud is now proposed. In the water modelling experiments, a new tundish model was built which provided the possibility to quantitatively characterize the sizes of bubbles generated. Six experimental factors were studied: the water speed in the ladle shroud model, the air injection rate, the slide gate opening ratio, the gas injection point distance from the slide gate, the air injection direction orientation and the orifice size. The experimental results showed that the water speed and the slide gate opening ratio determined the critical bubble size of the system. A high water speed and a low slide gate opening ratio lead to smaller critical bubble sizes. In general, the bubble size increased with any increase of the air injection rate and increase in the gas injection point distance from the slide gate. Therefore, the air injection rate must be kept low and the gas injection point should be located as close to the slide gate as possible, in order to generate very small bubbles. The direction of air injection and the orifice size did not show very strong correlations with the bubble sizes in the current experimental setups and conditions. The smallest bubbles generated from the experiments were about 0.5mm in diameter, which are predicted to be the optimum for removing mall inclusions (10-50μm) from liquid steel. In the CFD simulation, the commercial code package Ansys Fluent (version 14.5.7) was used. The simulations were carried out using the high performance computer in the McGill Metals Processing Centre (MMPC) at Stinson laboratory. The simulation results, particularly the local turbulence dissipation rate values, were used to calculate the theoretical critical bubble size. The trajectories of bubbles with different sizes were studied using the so-called discrete phase modelling (DPM). The mathematical simulation results were found to match with the experimental results reasonably well.Finally, a physical model analysis was performed by combining the results from the water modelling experiments and the CFD simulations. The optimal condition to produce small bubbles by injecting gas into ladle shroud is proposed in the end of this research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".