Mathematical Modeling of VOD Oxygen Nozzle Jets
Bibliographic record
Abstract
Abstract This study has focused on numerically exploring the oxygen flow in the convergent‐divergent De Laval nozzle. The De Laval nozzle has been commonly used as oxygen outlet at the lance tip in the vacuum oxygen decarburization (VOD) process. The nozzle geometry used in an active VOD plant was investigated by isentropic nozzle theory as well as by numerical modeling. Since an optimal nozzle design is only valid for a certain ambient pressure, one VOD nozzle will be less efficient for a large part of the pressure cycle. Different ambient pressures were used in the calculations that were based on the De Laval nozzle theory. Flow patterns of the oxygen jet under different ambient pressures were studied and the flow information at different positions from the nozzle was analyzed. In addition, the study compared the effects of different ambient temperatures on jet velocity and dynamic pressure. The predictions revealed that the modeling results obtained with the CFD modeling showed incorrect flow expansion, which agreed well with the results from the De Laval theory. Moreover, a little under‐expansion is somewhat helpful to improve the dynamic pressure. The jet dynamic pressure and its width for the specific nozzle geometry have also been studied. It has been observed that an altering ambient pressure can influence the jet momentum and its width. In addition, a high ambient temperature has a positive effect on the improvement of the jet dynamic pressure.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".