A Comparison Between 3-D Thermal Model and 3-D CFD Model for Vertical DC Casting of Rolling Ingots of Aluminum Alloy 7050
Bibliographic record
Abstract
In this study, first a 3-D thermal model is developed for an open top, vertical direct chill (DC) casting process of rolling slabs (ingots) by taking into account the casting speed in the form of slag flow in the thermal connective-diffusion equation. The mushy region solidification characteristics of the process are accounted for through the implementation of the enthalpy porosity technique. The thermal model is later extended to a 3-D CFD model to account for the coupled turbulent heat transfer and solidification aspect of the process. Both models simulate an industrial-sized, hot-top type vertical Direct Chill (DC) slab caster for high strength aluminum alloy AA-7050. A staggered control volume based finite-difference scheme is used to solve the modeled equations and the associated boundary conditions. In the CFD model, the turbulent aspects of flow and solidification heat transfer are modeled using a low Reynolds number version of the k–ε eddy viscosity approach. Computed results for the steady-state phase of the casting process are presented for four casting speeds varying from 60 to 180 mm/min for a fixed inlet melt superheat of 32°C. Simulation results of the velocity and temperature fields and heat fluxes along the caster surface are presented for the CFD model and the shell thickness and sump depth are compared between the CFD and thermal models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".