Characterization of the Turbulent Fluid Flow Structures in a Slab Mold with Experimental Measurements and Different Turbulence Models
Bibliographic record
Abstract
The turbulent flow of liquid steel in a slab mold was characterized using a commercial nozzle through physical water-model experiments and four turbulent models: 𝑘 -𝜀 Realizable (RKE), Detached Eddy Simulation (DES), Scale-Adaptive Simulation (SAS), and Large Eddy Simulation (LES).The comparison between numerical results and the experimental measurements (using Ultrasound Velocimetry) permitted the characterization of flow structures along a sub meniscus region, (of great importance for flux-dragging phenomena) In general, all four models predict characteristic unsteady state turbulent flows.However, the 𝑘 -𝜀 Realizable and DES models fail to predict instabilities in the internal flow of the nozzle, overpredicting sub-meniscus velocities in the mold.On the other hand, the SAS and LES models manage to predict the instabilities and changes in the internal flows inside the nozzles that occur at high frequencies and achieve an excellent agreement with the experimental velocity profiles.With the analysis of the results obtained, it is possible to say that the SAS model produces performances like those of the LES model with less computing effort and less cost.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".