Flow Field of Supersonic Oxygen Jet Generated by Various Wear Lengths at the Laval Nozzle Exit
Bibliographic record
Abstract
As a main apparatus, the oxygen lance is used to deliver the oxygen element and transfer kinetic energy into the molten bath in the steelmaking process. However, the Laval nozzle exit would be gradually worn out during the service life, which suppresses the performance of the oxygen lance. This paper investigated three different wear length (Lw) conditions at the exit of the Laval nozzle through numerical simulations and high-temperature experiments with various oxygen flow rates. The result showed that the entrainment of the ambient gas was the key factor of the wear phenomenon for the Laval nozzle exit. The maximum total temperature of the gas phase at the Laval nozzle exit formed by the Lw of 0 mm, 2 mm, and 4 mm were 300 K, 959 K, and 1700 K, respectively. Thus, by increasing the Lw value, the total temperature of the gas phase was rapidly improved at the exit of the Laval nozzle, which further accelerated the wear phenomenon at the exit of the Laval nozzle. Besides, axial velocities at the end of the potential core formed by the Lw of 0 mm, 2 mm, and 4 mm were 483.7 m/s, 480.0 m/s, and 478.7 m/s, respectively. As a result, the wear phenomenon reduced the impaction ability of the oxygen jet, which suppressed the impaction depth and radius, resulting in a smaller droplet generation rate.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".