Effect of shrouding nozzle arrangement on the flow field characteristics of coherent jet
Bibliographic record
Abstract
The coherent lance technology is widely used in the electric arc furnace (EAF) steelmaking process.It achieves a better metallurgical effect compared with the conventional supersonic lance technology.Herein, to further improve the stirring ability of the coherent lance, the effects of shrouding nozzle arrangement on the flow field and stirring ability of coherent jet were analyzed under two environment temperatures.Based on the results of the numerical simulation and combustion experiment, the characteristics of axial velocity and total temperature distributions has been introduced for researching the impaction ability of coherent jet using various shrouding nozzle arrangements.The appropriate simulation model was built by testing three types of mesh levels.The eddy dissipation concept (EDC) model and chemical kinetic mechanism were adopted to represent the combustion reactions of the O2-CH4jet flame in the numerical simulation process.Resultsreveals that the numerical simulation data shows good agreement with combustion experiment.The initial velocity at the exit of main oxygen Laval nozzle does not change with different shrouding nozzle arrangements.With increase in the distance between the main oxygen and shrouding nozzles, the length of the velocity potential core first increases and then reduces.Moreover, all shrouding nozzle arrangements increase the length of velocity potential core.
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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 | 0.002 |
| 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 | 0.001 |
| 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".