A study on supersonic coherent jet characteristics using computational fluid dymanics
Bibliographic record
Abstract
Supersonic gas jets are widely used in BOF and EAF steelmaking for refining the liquid iron inside the furnace. Supersonic gas jets are preferred over subsonic jets because of high dynamic pressure associated with it which results in higher depth of penetration and better mixing. Laval nozzles are used to accelerate the gas jets to supersonic velocities of around 2.0 Mach number in steelmaking. When a supersonic gas jet exits from a Laval nozzle, it interacts with surrounding environment to produce a region of turbulent mixing. This process results in an increase in jet diameter and decrease in jet velocity with increasing distance from nozzle exit. During oxygen blowing, the higher the distance between liquid surface and the nozzle exit the more is the entrainment of surrounding fluid which in turn decreases the impact velocity as well as momentum transfer to the liquid. Hence, it is desirable to locate the nozzle close to the liquid metal surface. But the disadvantage of this is the sticking of slag/metal droplets on the lance tip which results in poor tip life. In order to solve the problem, coherent jet technology has been introduced in the EAF steelmaking process at the end of last century. The potential core length (the length up to which the axial jet velocity is equal to the exit velocity at the nozzle) of a coherent supersonic jet is about 40 nozzle diameters compared to 10 nozzle diameters in case of normal supersonic jet. Coherent gas jets are produced by surrounding the normal supersonic jet with flame envelope. The flame envelope is created using a fuel and oxidant. Due to the flame, the entrainment of the surrounding gas into the supersonic jet is reduced, leading to a higher potential core length of the supersonic jet. Although the steelmaking industries have been using the coherent supersonic jet for last one decade, not much research work has been done to investigate the physics involved in supersonic coherent jet. In this study, Computational fluid dynamics (CFD) simulations of supersonic jet with and without shrouding flame were carried out and validated against experimental data. The numerical results showed that the potential core length of the coherent supersonic jet is 4 times longer than that of a supersonic jet without flame shrouding which were in good agreement with experimental results. The CFD model results were then used to analyse the flame shrouding effect on the central supersonic jet.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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 teacher head, 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".